The PHB repository contains workflows for the characterization, genomic epidemiology, and sharing of pathogen genomes of public health concern. Workflows are available for viruses, bacteria, and fungi.
All workflows in the PHB repository end with _PHB in order to differentiate them from earlier versions and from the original tools they incorporate.
Explore our workflows
Command-line Users
Learn how to use our workflows on the command-line!
Terra Users
Learn how to use our workflows on Terra!
Our Open Source Philosophy
PHB source code is publicly available on GitHub and available under GNU Affero General Public License v3.0!
All workflows can be imported directly to Terra via the Dockstore PHB collection!
You can also use our workflows on the command-line. Please see our guide on how to get started here!
When undertaking genomic analysis using the command-line, via Terra, or other data visualization platforms, it is essential to consider the necessary and appropriate workflows and resources for your analysis. To help you make these choices, take a look at the relationship between the most commonly used Theiagen workflows.
Analysis Approaches for Genomic Data
This diagram shows the Theiagen workflows (green boxes) available for analysis of genomic data in public health and the workflows that may be used consecutively (arrows). The blue boxes describe the major functions that these workflows undertake. The yellow boxes show functions that may be undertaken independently of workflows on Terra.
"},{"location":"#phb-development-is-a-cycle","title":"PHB development is a cycle","text":"We continuously work to improve our codebase and usability of our workflows by the public health community, so changes from version to version are expected. This documentation page reflects the state of the workflow at the version stated in the title.
What's new?
You can see the changes since PHB v2.2.0 here!
"},{"location":"#contributing-to-the-phb-repository","title":"Contributing to the PHB Repository","text":"We warmly welcome contributions to this repository! Our style guide may be found here for convenience of formatting.
If you would like to submit suggested code changes to our workflows, you may add or modify the WDL files and submit pull requests to the PHB GitHub repository.
You can expect a careful review of every PR and feedback as needed before merging, just like we do for PRs submitted by the Theiagen team. Our PR template can help prepare you for the review process. As always, reach out with any questions! We love recieving feedback and contributions from the community. When your PR is merged, we'll add your name to the contributors list below!
"},{"location":"#authorship-responsibility","title":"Authorship & Responsibility","text":""},{"location":"#authorship","title":"Authorship","text":"(Ordered by contribution [# of lines changed] as of 2024-12-04)
We would like to gratefully acknowledge the following individuals from the public health community for their contributions to the PHB repository:
The PHB repository would not be possible without its predecessors. We would like to acknowledge the following repositories, individuals, and contributors for their influence on the development of these workflows:
The PHB repository originated from collaborative work with Andrew Lang, PhD & his Genomic Analysis WDL workflows. The workflows and task development were influenced by The Broad's Viral Pipes repository. The TheiaCoV workflows for viral genomic characterization were influenced by UPHL's Cecret & StaPH-B's Monroe (now deprecated) workflows. The TheiaProk workflows for bacterial genomic characterization were influenced by Robert Petit's bactopia. Most importantly, the PHB user community drove the development of these workflows and we are grateful for their feedback and contributions.
If you would like to provide feedback, please raise a GitHub issue or contact us at support@theiagen.com.
"},{"location":"#maintaining-phb-pipelines","title":"Maintaining PHB Pipelines","text":"Theiagen Genomics has committed to maintaining these workflows for the forseeable future. These workflows are written using a standard workflow language (WDL) and uses Docker images based on the StaPHB-B Docker Builds. New versions that include bug fixes and additional features are released on a quarterly bases, with urgent bug fixes released as needed. Each version is accompanied by detailed release notes to lower the barrier of pipeline upkeep from the public health community at large.
"},{"location":"#point-of-contact","title":"Point of Contact","text":"If you have any questions or concerns, please raise a GitHub issue or email Theiagen's general support at support@theiagen.com.
"},{"location":"#conflict-of-interest","title":"Conflict of Interest","text":"The authors declare no conflict of interest.
"},{"location":"#citation","title":"Citation","text":"Please cite this paper if publishing work using any workflows:
Libuit, Kevin G., Emma L. Doughty, James R. Otieno, Frank Ambrosio, Curtis J. Kapsak, Emily A. Smith, Sage M. Wright, et al. 2023. \"Accelerating Bioinformatics Implementation in Public Health.\" Microbial Genomics 9 (7). https://doi.org/10.1099/mgen.0.001051.
Alternatively, please cite this paper if using the TheiaEuk workflow:
Ambrosio, Frank, Michelle Scribner, Sage Wright, James Otieno, Emma Doughty, Andrew Gorzalski, Danielle Siao, et al. 2023. \"TheiaEuk: A Species-Agnostic Bioinformatics Workflow for Fungal Genomic Characterization.\" Frontiers in Public Health 11. https://doi.org/10.3389/fpubh.2023.1198213.
"},{"location":"#about-theiagen","title":"About Theiagen","text":"Theiagen develops bioinformatics solutions for public health labs, and then trains and supports scientists to use these. If you would like to work with Theiagen, please\u00a0get in contact.
"},{"location":"assets/new_workflow_template/","title":"Workflow Name","text":""},{"location":"assets/new_workflow_template/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Link to Workflow Type Link to Applicable Kingdom PHB <version with last changes> <command-line compatibility> <workflow level on terra (set or sample)>"},{"location":"assets/new_workflow_template/#workflow_name_on_terra","title":"Workflow_Name_On_Terra","text":"Description of the workflow.
"},{"location":"assets/new_workflow_template/#inputs","title":"Inputs","text":"Input should be ordered as they appear on Terra
Terra Task Name Variable Type Description Default Value Terra Status task_name variable_name Type Description Default Value Required/Optional"},{"location":"assets/new_workflow_template/#workflow-tasks","title":"Workflow Tasks","text":"Description of the workflow tasks
tool_name: Description of tool Description of the task
Tool Name Technical Details
Links Task [link to task on GitHub] Software Source Code [link to tool's source code] Software Documentation [link to tool's documentation] Original Publication(s) [link to tool's publication]"},{"location":"assets/new_workflow_template/#outputs","title":"Outputs","text":"Variable Type Description variable_name Type Description"},{"location":"assets/new_workflow_template/#references-if-applicable","title":"References (if applicable)","text":"reference1
reference2
"},{"location":"contributing/code_contribution/","title":"PHB Code Contributions","text":"Theiagen Genomics\u2019 Public Health Bioinformatics (PHB) workflows are written in\u00a0WDL, a language for specifying data processing workflows with a human-readable and writable syntax. Contributions to the workflows contained in the repository are warmly welcomed.
This document gives coding conventions for the WDL code comprising the workflow and task development for PHB. This style guide evolves over time as additional conventions are identified and past conventions are rendered obsolete by changes in the language itself.
Style guide inspired by\u00a0Scott Frazer\u2019s\u00a0WDL Best Practices Style Guide.
"},{"location":"contributing/code_contribution/#general-guidelines","title":"General Guidelines","text":"Modularity and Metadata
Add a meta block to every task and workflow to provide a brief description of its purpose.
meta {\n description: \"This tool does X\"\n}\nDocker Containers
Use a specific Docker container version instead of 'latest' to ensure reproducibility and prevent unexpected changes in container behavior.
String docker = \"us-docker.pkg.dev/docker_image:version\"\nPreferentially use containers Google's Artifact Registry rather than those from quay.io or dockerhub
Indentation and Whitespace
Use 2-space indentation for all blocks. Avoid using tabs to ensure uniform formatting across editors:
# perform action\nif [ condition ]; then\n perform_action(variable)\nfi\nUse a single space when defining variables (this = that not this= that (unless a bash variable where this=that is required))
Bracket and Spacing Conventions
input { instead of input\\n{ # Correct\ninput {\n String input_variable\n}\n\n# Incorrect\ninput\n{\n String input_variable\n}\noutput { instead of output{)Command Block Syntax
Enclose command blocks in triple angle brackets (<<< ... >>>) for consistency and easier handling of multi-line scripts. It also avoids issues with unescaped special characters in the command block:
command <<<\n tool --input ~{input} --output ~{output}\n>>>\nA WDL task block defines a discrete, reusable step in a workflow. To ensure readability and consistency, follow these conventions when writing task blocks. Include single spaces between the input, command, output, and runtime sections and their enclosing curly brackets.
task example_task {\n input {\n\n }\n command <<<\n\n >>>\n output {\n\n }\n runtime {\n\n }\n}\ninput block","text":"input {\n Int cpu = 4 # Number of CPUs\n Int disk_size = 100 # Disk space in GB\n String docker = \"us-docker.pkg.dev/example:1.0.0\" # Docker container for the task\n Int memory = 16 # Memory in GB\n}\nInclude optional tool parameters as inputs to the task
input {\n Int? optional_tool_parameter1\n String optional_tool_parameter2_with_default = \"default_value\"\n}\nInput and output lists should not be formatted to have the equal sign aligned, but instead use a single space before and after the =
correct_output = \"output_file\"\nlong_variable_name = \"long_file_name\"\nExpose Docker as an input, an output (if versioning information not available), and runtime variable:
input {\n String docker = \"us-docker.pkg.dev/example:1.0.0\"\n}\n...\noutput {\n String used_docker = docker\n}\nruntime {\n docker: docker\n}\ncommand block","text":"Ensure use of line breaks between different sections of code to improve readability
# Perform task step 1\nif [ condition ]; then\n action1(variable)\nfi\n\n# Perform task step 2\nif [ another_condition ]; then\n action2(variable)\nfi\nSplit command calls into multiple lines if they have user input variables and/or if the length of the command is very long to avoid text wrapping and/or side-scrolling, e.g.
tool \\\n --input ~{input_file} \\\n --output ~{output_file} \\\n --option1 ~{option1} \\\n ...\n --optionN ~{optionN}\nAdd comments that
Explain what non-intuitive bash/python text wrangling actions do, e.g.
## awk for gene column ($6) to grab subtype ($15)\ncat ~{file} | awk -F '\\t' '{if ($6==\"M1\") print $15}' > FLU_TYPE\noutput block","text":"output {\n File result_csv = \"output.csv\" # CSV file generated\n File result_log = \"log.txt\" # Log file\n}\nEnsure the docker container is exposed as an output string, e.g.
input {\n String docker = \"us-docker.pkg.dev/general-theiagen/tool:version\"\n}\n...\noutput {\n String XX_docker = docker\n}\nruntime {\n docker: docker\n}\nruntime block","text":"Always specify a Docker:
runtime {\n docker: docker\n cpu: cpu\n memory: memory\n disk: disk_size\n}\nA WDL workflow block orchestrates the execution of tasks and subworkflows. It defines the inputs, calls tasks or subworkflows, and specifies the final outputs. To ensure readability and consistency, follow these conventions when writing workflow blocks:
"},{"location":"contributing/code_contribution/#the-import-section","title":"Theimport section","text":"import statements (sorted in alphabetical order).When a workflow imports a task, ensure it is imported under a unique name to avoid conflicts.
import \"../tasks/task_task1.wdl\" as task1_task\nimport \"../tasks/task_task2.wdl\" as task2_task\nOrder import statements alphabetically by the path of the imported file.
input block","text":"input {\n String input\n String task1_docker = \"us-docker.pkg.dev/general-theiagen/tool:version\"\n String? task1_optional_argument\n}\ncall sections","text":"call task1_task.task1 {\n input:\n input = input,\n docker = task1_docker\n}\noutput block","text":"output {\n # Task 1 outputs\n File task1_out_csv = task1.output_csv\n String task1_version = task1.version\n\n # Subworkflow outputs\n File subworkflow_out_tsv = subworkflow.task3_out_tsv\n String subworkflow_version = subworkflow.task3_version\n}\nimport \"../tasks/task_task1.wdl\" as task1_task\nimport \"../tasks/task_task2.wdl\" as task2_task\n\nimport \"../workflows/wf_subworkflow.wdl\" as subworkflow\n\nworkflow example_wf {\n input {\n String input\n String task1_docker = \"us-docker.pkg.dev/general-theiagen/task_1:version\"\n String task2_docker = \"us-docker.pkg.dev/general-theiagen//task_2:version\"\n String? hidden_task3_argument \n String? hidden_task3_docker\n String? hidden_task4_docker\n }\n call task1_task.task1 {\n input:\n input = input,\n docker = task1_docker\n }\n call task2_task.task2 {\n input: \n input = input,\n docker = task2_docker\n }\n call subworkflow.subworkflow {\n input:\n input = input,\n task3_argument = hidden_task3_argument,\n task3_docker = hidden_task3_docker\n task4_docker = hidden_task4_docker\n }\n output {\n # Task 1 outputs\n File task1_out_csv = task1.output_csv\n String task1_version = task1.version\n String task1_docker = task1.docker\n # Task 2 outputs\n File task2_out_tsv = task2.output_tsv\n String task2_version = task2.version\n String task2_docker = task2.docker\n # Subworkflow outputs for task 3\n File task3_out_tsv = subworkflow.task3_out_tsv\n String task3_version = subworkflow.task3_version\n String task3_docker = subworkflow.task3_docker\n # Subworkflow outputs for task 4\n String task4_output = subworkflow.task4_output\n String task4_version = subworkflow.task4_version\n } \n}\nimport \"../tasks/task_task3.wdl\" as task3_task\nimport \"../tasks/task_task4.wdl\" as task4_task\n\nworkflow subworkflow {\n input {\n String input\n\n # optional inputs for tasks inside subworkflows cannot\n # be seen on Terra, so make them available at the subworkflow\n # level so they can be modified by a Terra user\n String? task3_argument \n String? task3_docker\n String? task4_docker\n }\n call task3_task.task3 {\n input:\n input = input,\n args = task3_argument,\n docker = task3_docker\n }\n call task4_task.task4 {\n input:\n input = task3.output_tsv,\n docker = task4_docker\n }\n output {\n File task3_out_tsv = task3.output_tsv\n String task3_version = task3.version\n String task3_docker = task3.docker\n String task4_output = task4.output\n String task4_version = task4.version\n }\n}\nThe documentation for PHB is hosted in the docs/ directory. This documentation is written in Markdown and is built using MkDocs and the Material for MkDocs theme.
This guide is intended to provide a brief overview of the documentation structure and how to contribute to the documentation, including standard language and formatting conventions.
"},{"location":"contributing/doc_contribution/#local-installation-live-previews","title":"Local Installation & Live Previews","text":"Since the documentation is built off of the main branch, it is highly recommended to preview your changes before making a PR. You can do this by installing the necessary packages and previewing (\"serving\") the documentation locally.
To test your documentation changes, you will need to have the following packages installed on your local VM:
pip install mkdocs-material mkdocs-material-extensions mkdocs-git-revision-date-localized-plugin mike mkdocs-glightbox\nOnce installed, navigate to the top directory in PHB. The live preview server can be activated by running the following command:
mkdocs serve\nThis will prompt you to open your browser to the appropriate local host address (by default, localhost:8000). Every time you save a change, the documentation will automatically update in the browser.
"},{"location":"contributing/doc_contribution/#vscode-extensions","title":"VSCode Extensions","text":"Here are some VSCode Extensions can help you write and edit your markdown files (and allow you preview changes without running the server, though formatting will suffer):
** or _ characters.In order to maintain cohesive documentation, the following language and formatting conventions should be followed:
"},{"location":"contributing/doc_contribution/#language-conventions","title":"Language Conventions","text":"The following language conventions should be followed when writing documentation:
cpu - Number of CPUs to allocate to the taskdisk_size - Amount of storage (in GB) to allocate to the taskdocker or docker_image - The Docker container to use for the taskmemory - Amount of memory/RAM (in GB) to allocate to the task**bold text** to indicate text that should be bolded._italicized text_ to indicate text that should be italicized.==highlighted text== to indicate text that should be highlighted.Code - Use `code` (backticks) to indicate text that should be formatted as code.^^underlined text^^ to indicate text that should be underlined (works with our theme; not all Markdown renderers support this).Citations
> to activate quote formatting for a citation. Make sure to separate multiple citations with a comment line (<!-- -->) to prevent the citations from running together.Callouts/Admonitions - These features are called \"call-outs\" in Notion, but are \"Admonitions\" in MkDocs. I highly recommend referring to the Material for MkDocs documentation page on Admonitions to learn how best to use this feature. Use the following syntax to create a callout:
!!! note\n This is a note. Observe I am indented with four spaces.\nPlease see the Admonition documentation for more information on how to change the title, enable toggles, and more.
The following custom callout types are supported in addition to the standard admonitions supported by our theme more information here:
Dna
This is a DNA admonition. Admire the cute green DNA emoji. You can create this with the !!! dna syntax.
Use this admonition when wanting to convey general information or highlight specific facts.
ToggleThis is a toggle-able section. The emoji is an arrow pointing to the right downward. You can create this with the ??? toggle syntax. I have added a + at the end of the question marks to make it open by default.
Use this admonition when wanting to provide additional optional information or details that are not strictly necessary, or take up a lot of space.
TaskThis is a toggle-able section for a workflow task. The emoji is a gear. Use the ??? task syntax to create this admonition. Use !!! task if you want to have it be permanently expanded. I have add a + at the end of the question marks to make this admonition open by default and still enable its collapse.
Use this admonition when providing details on a workflow, task, or tool.
Caption
This is a caption. The emoji is a painting. You can create this with the !!! caption syntax. A caption can be added beneath the picture and will also look nice.
Use this admonition when including images or diagrams in the documentation.
Techdetails
This is where you will put technical details for a workflow task. You can create this by !!! techdetails syntax.
Use this admonition when providing technical details for a workflow task or tool. These admonitions should include the following table:
Links Task [link to the task file in the PHB repository on GitHub] Software Source Code [link to tool's source code] Software Documentation [link to tool's documentation] Original Publication(s) [link to tool's publication]If any of these items are unfillable, delete the row.
Images - Use the following syntax to insert an image:
!!! caption \"Image Title\"\n \nIndentation - FOUR spaces are required instead of the typical two. This is a side effect of using this theme. If you use two spaces, the list and/or indentations will not render correctly. This will make your linter sad :(
- first item\n - second item\n - third item\nTables - Use the following syntax to create a table
| Header 1 | Header 2 | Header 3 |\n|---|---|---|\n| value 1 | value2 | value3 |\nNote that this is not a \"pretty\" markdown table. This is because the spacing would be crazy in the markdown file, especially for tables with a lot of text and/or columns. The table will render correctly in the documentation.
Links - Use the following syntax to create a link. This is works for both files and websites. If linking a file, use the relative path.
[Link Text](https://www.example.com)\nEnd all pages with an empty line
A brief description of the documentation structure is as follows:
docs/ - Contains the Markdown files for the documentation.assets/ - Contains images and other files used in the documentation.figures/ - Contains images, figures, and workflow diagrams used in the documentation. For workflows that contain many images (such as BaseSpace_Fetch), it is recommended to create a subdirectory for the workflow.files/ - Contains files that are used in the documentation. This may include example outputs or templates. For workflows that contain many files (such as TheiaValidate), it is recommended to create a subdirectory for the workflow.logos/ - Contains Theiagen logos and symbols used in the documentation.metadata_formatters/ - Contains the most up-to-date metadata formatters for our submission workflows.new_workflow_template.md - A template for adding a new workflow page to the documentation. You can see this template herecontributing/ - Contains the Markdown files for our contribution guides, such as this filejavascripts/ - Contains JavaScript files used in the documentation.tablesort.js - A JavaScript file used to enable table sorting in the documentation.overrides/ - Contains HTMLs used to override theme defaultsmain.html - Contains the HTML used to display a warning when the latest version is not selectedstylesheets/ - Contains CSS files used in the documentation.extra.css - A custom CSS file used to style the documentation; contains all custom theme elements (scrollable tables, resizable columns, Theiagen colors), and custom admonitions.workflows/ - Contains the Markdown files for each workflow, organized into subdirectories by workflow categoryworkflows_overview/ - Contains the Markdown files for the overview tables for each display type: alphabetically, by applicable kingdom, and by workflow type.index.md - The home/landing page for our documentation.If you are adding a new workflow, there are a number of things to do in order to include the page in the documentation:
docs/workflows/. Feel free to use the template found in the assets/ folder.docs/workflows/ subdirectorydocs/workflows_overview/:workflows_alphabetically.md - Add the workflow in the appropriate spot based on the workflow name.workflows_kingdom.md - Add the workflow in the appropriate spot(s) based on the kingdom(s) the workflow is applicable to. Make sure it is added alphabetically within the appropriate subsection(s).workflows_type.md - Add the workflow in the appropriate spot based on the workflow type. Make sure it is added alphabetically within the appropriate subsection.mkdocs.yml file (under the nav: section) in the main directory of this repository. These should be the exact same spots as in the overview tables but without additional information. This ensures the workflow can be accessed from the navigation sidebar.What is WDL?
Running workflows on the command-line requires the direct use of the WDL (Workflow Development Language). As the name suggests, this is the workflow management language that is used to write and execute workflows. Frank has put together a great video describing \ud83d\udcfa WDL Task and Workflow Files and you can find full instructions below on running these WDL workflows.
"},{"location":"getting_started/commandline/#step-1-obtain-the-workflow-and-data","title":"Step 1: Obtain the Workflow and Data","text":"You will need to have access to the WDL workflow file (.wdl) and any associated input files (such as reference genomes, input data files, etc.). To do this, complete the following steps:
"},{"location":"getting_started/commandline/#1-install-git-if-not-already-installed","title":"1. Install Git (if not already installed)","text":"If you don't already have Git installed on your system, you will need to install it. Here's how you can install Git on some common operating systems:
Linux (Ubuntu/Debian)sudo apt update\nsudo apt install git\nGit is usually pre-installed on macOS. However, you can install or update it using Homebrew:
brew install git\nDownload and install Git from the official website: https://git-scm.com/download/win
"},{"location":"getting_started/commandline/#2-clone-the-repository","title":"2. Clone the Repository","text":"Create a directory where you want to store the cloned repository and navigate to it.
mkdir /path/to/your/desired/new/directory\ncd /path/to/your/desired/new/directory\nClone the https://github.com/theiagen/public_health_bioinformatics repository from GitHub using the following command:
git clone https://github.com/theiagen/public_health_bioinformatics.git\nAfter running the command, Git will download all the repository files and set up a local copy in the directory you specified.
Change your working directory to the newly cloned repository:
cd public_health_bioinformatics\nYou're now inside the cloned repository's directory. Here, you should find all the files and directories from the GitHub repository.
You can verify that the repository has been cloned successfully by listing the contents of the current directory using the ls (on Linux/macOS) or dir (on Windows) command:
ls\nThis should display the files and directories within the https://github.com/theiagen/public_health_bioinformatics.git repository.
Congratulations! You've successfully cloned the https://github.com/theiagen/public_health_bioinformatics.git repository from GitHub to your local command-line environment. You're now ready to proceed with running the bioinformatics analysis workflows using WDL as described in subsequent steps.
"},{"location":"getting_started/commandline/#step-2-install-docker-and-miniwdl","title":"Step 2: Install docker and miniWDL","text":"Docker and miniwdl will be required for command-line execution. We will check if these are installed on your system and if not, install them now.
Navigate to the directory where your workflow and input files are located using the cd command:
cd /path/to/your/workflow/directory\nCheck if Docker is installed:
docker --version\nIf Docker is not installed, follow the official installation guide for your operating system: https://docs.docker.com/get-docker/
Check if miniwdl is installed:
miniwdl --version\nIf miniwdl is not installed, you can install it using pip:
pip install miniwdl\nIn a WDL (Workflow Description Language) workflow, an input JSON file is used to provide attributes (values/files etc) for input variables into the workflow. The names of the input variables must match the names of inputs specified in the workflow file. The workflow files can be found within the git repository that you cloned. Each input variable can have a specific type of attribute, such as String, File, Int, Boolean, Array, etc. Here's a detailed outline of how to specify different types of input variables in an input JSON file:
String InputTo specify a string input, use the name of the input variable as the key and provide the corresponding string value. Example:
{\n \"sampleName\": \"VirusSample1\",\n \"primerSequence\": \"ACGTGTCAG\"\n}\nTo specify a file input, provide the path to the input file relative to the directory where you run the miniwdl command. Example:
{\n \"inputFastq\": \"data/sample.fastq\",\n \"referenceGenome\": \"reference/genome.fasta\"\n}\nTo specify an integer input, provide the integer value. These do not require quotation marks. Example:
{\n \"minReadLength\": 50,\n \"maxThreads\": 8\n}\nTo specify a boolean input, use true or false (lowercase). Example:
{\n \"useQualityFiltering\": true,\n \"useDuplicateRemoval\": false\n}\nTo specify an array input, provide the values as an array. Example:
{\n \"sampleList\": [\"Sample1\", \"Sample2\", \"Sample3\"],\n \"thresholds\": [0.1, 0.05, 0.01]\n}\nRun the workflow using miniwdl with the following command, replacing your_workflow.wdl with the actual filename of your WDL workflow and input.json with the filename of your input JSON file.
miniwdl run your_workflow.wdl --input input.json\nYou can monitor the progress of the workflow by checking the console output for updates and log messages. This can help you identify any potential issues or errors during execution.
Tips for monitoring your workflow What to do if you need to cancel a run"},{"location":"getting_started/commandline/#tips-for-monitoring","title":"Tips for monitoring workflow progress","text":"After you've started the workflow using the miniwdl run command, you'll see various messages appearing in the terminal. These messages provide information about the various steps of the workflow as they are executed. Monitoring this output is crucial for ensuring that the workflow is progressing as expected.
The console output will typically show:
Example Console Output:
Here's an example of what the console output might look like while the workflow is running:
Running: task1\nRunning: task2\nCompleted: task1 (Duration: 5s)\nRunning: task3\nError: task2 (Exit Code: 1)\nRunning: task4\n...\nIn this example, you can see that task1 completed successfully in 5 seconds, but task2 encountered an error and exited with a non-zero exit code. This kind of output provides insight into the progress and status of the workflow.
What to Look For:
As you monitor the console output, pay attention to:
Early Troubleshooting:
If you encounter errors or unexpected behavior, the console output can provide valuable information for troubleshooting. You can search for the specific error messages to understand the problem and take appropriate action, such as correcting input values, adjusting parameters, or addressing software dependencies.
Monitoring the workflow progress through the console output is an essential practice for successful execution. It allows you to track the status of individual tasks, identify errors, and ensure that your analysis is proceeding as planned. Regularly reviewing the output will help you address any issues and improve the efficiency of your bioinformatics workflow.
"},{"location":"getting_started/commandline/#canceling-a-run","title":"Canceling a Running Workflow","text":"Canceling a running workflow is an important step in case you need to stop the execution due to errors, unexpected behavior, or any other reason. If you're using miniwdl to run your workflow, here's how you can cancel a workflow run while it's in progress:
Ctrl + C. This sends an interrupt signal to the running process, which should gracefully terminate it. However, keep in mind that this might not work for all scenarios, and some tasks might not be able to cleanly terminate.docker ps command to list running containers and docker stop <container_id> to stop a specific container.Kill the miniwdl Process: If the Ctrl + C approach doesn't work, you might need to explicitly kill the miniwdl process running in the terminal. To do this, you can use the kill command. First, find the process ID (PID) of the miniwdl process by running:
ps aux | grep miniwdl\nIdentify the PID in the output and then run:
kill -9 <PID>\nThis forcefully terminates the process.
Clean Up Intermediate Files: Depending on the workflow and how tasks are structured, there might be intermediate files or resources that were generated before the cancellation. You might need to manually clean up these files to free up disk space.
Remember that canceling a workflow might leave the system in an inconsistent state, especially if some tasks were partially executed. After canceling, it's a good idea to review the output and logs to identify any cleanup actions you might need to take.
It's important to approach workflow cancellation carefully, as abruptly terminating processes can potentially lead to data loss or other unintended consequences. Always make sure you understand the workflow's behavior and any potential side effects of cancellation before proceeding.
"},{"location":"getting_started/commandline/#step-6-review-output","title":"Step 6: Review Output","text":"Once the workflow completes successfully, you will find the output files and results in the designated output directory as defined in your WDL workflow.
Substep 1: Locate the Output DirectoryBefore you begin reviewing outputs, make sure you know where the output directory of your workflow is located. This is typically specified in the workflow configuration or input JSON file. Navigate to this directory using the cd command in your terminal.
cd /path/to/your/output/directory\nLogs are a valuable source of information about what happened during each step of the workflow. Each task in the workflow might generate its own log file. Here's how to review logs:
Use the ls command to list the files in the output directory:
ls\nLook for log files with names that correspond to the tasks in your workflow. These files often have a .log extension.
Open a log file using a text editor like less or cat:
less task_name.log\nUse the arrow keys to navigate through the log, and press q to exit.
Inspect the log for messages related to the task's execution, input values, software versions, and any errors or warnings that might have occurred.
stderr and stdout are streams where processes write error messages and standard output, respectively. These are often redirected to files during workflow execution. Here's how to review them:
ls command to list the files in the output directory.task_name.err (for stderr) and task_name.out (for stdout).Open the files using a text editor:
less task_name.err\nless task_name.out\nThese files might contain additional information about the task's execution, errors, and output generated during the analysis.
Workflow tasks might generate various types of output files, such as plots, reports, or data files. Here's how to review them:
ls command to list the files in the output directory.less or a text editor for text-based files, or an image viewer for image files.As you review the outputs, keep these points in mind:
As you review the outputs, make notes of any issues, errors, or unexpected behavior you encounter. Depending on the severity of the issues, you might need to:
Output Review Conclusion
Reviewing the outputs of your bioinformatics workflow is a critical step to ensure the quality of your analysis. Logs, stderr, stdout, and generated output files provide valuable insights into the execution process and results. By carefully reviewing these outputs and addressing any issues, you can enhance the reliability and accuracy of your bioinformatics analysis.
"},{"location":"getting_started/commandline/#step-7-troubleshooting-and-debugging","title":"Step 7: Troubleshooting and Debugging","text":"Congratulations! You have successfully executed a bioinformatics analysis workflow using WDL on the command-line. This tutorial covered the basic steps to run a WDL workflow using the miniwdl command-line tool.
Remember that the specific steps and commands might vary depending on the details of your workflow, software versions, and environment. Be sure to consult the documentation for miniwdl, WDL, and any other tools you're using for more advanced usage and troubleshooting.
Happy analyzing!
"},{"location":"getting_started/terra/","title":"Getting Started with Terra","text":"Our Approach
Theiagen\u2019s approach to genomic analysis in public health typically uses the Terra platform to run workflows that undertake bioinformatic analysis, then uses other platforms for visualization of the resulting data. This is described in more depth in our paper Accelerating bioinformatics implementation in public health, and the application of this approach for genomic surveillance of SARS-CoV-2 in California is described in the paper Pathogen genomics in public health laboratories: successes, challenges, and lessons learned from California\u2019s SARS-CoV-2 Whole-Genome Sequencing Initiative, California COVIDNet.
When undertaking genomic analysis using Terra and other data visualization platforms, it is essential to consider the necessary and appropriate workflows and resources for your analysis. To help you make these choices, take a look at the relationship between the most commonly used Theiagen workflows, and the descriptions of the major stages in genomic data analysis below.
Analysis Approaches for Genomic Data
This diagram shows the Theiagen workflows (green boxes) available for analysis of genomic data in public health and the workflows that may be used consecutively (arrows). The blue boxes describe the major functions that these workflows undertake. The yellow boxes show functions that may be undertaken independently of workflows on Terra.
"},{"location":"getting_started/terra/#data-import-to-terra","title":"Data Import to Terra","text":"To start using Terra for data analysis, you will first need to import your data into your workspace. There are multiple ways to do this:
SOPs for importing data into a Terra workspace
SOP SOP Version PHB Version Compatibility Uploading Data, Creating Metadata Tables and TSV files, and Importing Workflows v3 v1.3.0, v2+ Linking BaseSpace and Importing BaseSpace Reads to Terra v3 v1.3.0, v2+"},{"location":"getting_started/terra/#genome-assembly-qc-and-characterization","title":"Genome assembly, QC, and characterization","text":""},{"location":"getting_started/terra/#theiax-workflows","title":"TheiaX workflows","text":"The TheiaX workflows are used for genome assembly, quality control, and characterization. The TheiaCoV Workflow Series, TheiaProk Workflow Series, and TheiaEuk Workflow Series workflows are intended for viral, bacterial, and fungal pathogens, respectively. TheiaMeta Workflow Series is intended for the analysis of a single taxon from metagenomic data.
SOPs for the TheiaX workflows
For analyzing SARS-CoV-2 SOP SOP Version PHB Version Compatibility Analyze SARS-COV-2 using TheiaCoV_Illumina_PE_PHB v3 v2+ Analyze SARS-COV-2 using TheiaCoV_Illumina_SE_PHB v3 v2+ Analyze SARS-COV-2 using TheiaCoV_ClearLabs v3 v2+ Analyze SARS-COV-2 using TheiaCoV_ONT v2 v1.x+ Analyzing SARS-CoV-2 using TheiaCoV_FASTA v2 v1.x+ For analyzing influenza SOP SOP Version PHB Version Compatibility Analyzing Flu Data in Terra using TheiaCov_Illumina_PE and Augur Workflows v1 v1.x+"},{"location":"getting_started/terra/#quality-evaluation","title":"Quality evaluation","text":"The TheiaX workflows will generate various quality metrics. These should be evaluated relative to quality thresholds that have been agreed upon within your laboratory or sequencing program and define the sufficient quality characteristics for a genome and sequence data to be used. For the TheiaCoV Workflow Series, TheiaProk Workflow Series, and TheiaEuk Workflow Series workflows, this quality evaluation may be undertaken using the optional QC_check task. Full instructions for the use of this task may be found on the relevant workflow page. Some quality metrics are not evaluated by the QC_check task and should be evaluated manually.
Genomes that fail to meet agreed quality thresholds should not be used. Results for characterization of these genomes may be inaccurate or unreliable. The inclusion of poor-quality genomes in downstream comparative analyses will bias their results. Samples that fail to meet QC thresholds will need to be re-sequenced and sample processing may need to be repeated (e.g. culture-based isolation of clonal bacteria, DNA/RNA extraction, and processing for sequencing).
"},{"location":"getting_started/terra/#update-workflows-for-sars-cov-2-genomes","title":"Update workflows for SARS-CoV-2 genomes","text":"Workflows are available for updating the Pangolin and VADR assignments made to SARS-CoV-2 genomes. The Pangolin Update workflow accounts for the delay in assigning names to newly emerging lineages that you may have already sequenced. The VADR_Update workflow similarly accounts for features that have been newly identified in SARS-CoV-2 genomes when assessing genome quality with VADR.
"},{"location":"getting_started/terra/#phylogenetics","title":"Phylogenetics","text":""},{"location":"getting_started/terra/#phylogenetic-construction","title":"Phylogenetic construction","text":"Phylogenetic trees are constructed to assess the evolutionary relationships between sequences in the tree. These evolutionary relationships are often used as a proxy for epidemiological relationships, and sometimes for inferring transmission between isolation sources.
There are various methods for constructing phylogenetic trees, depending on the sequencing data being used, the organism being analyzed and how it evolved, what you would like to infer from the tree, and the computational resources available for the tree construction. Theiagen has a number of workflows for constructing phylogenetic trees. For full details of these workflows, please see Guide to Phylogenetics which includes advice on the appropriate tree-building workflows and phylogenetic visualization approaches.
SOPs for phylogenetic construction
SOP SOP Version PHB Version Compatibility Analyzing Flu Data in Terra using TheiaCov_Illumina_PE and Augur Workflows v1 v1.x+ Analyzing Phylogenetic Relationships in Terra using Theiagen\u2019s Augur Workflows v1 v1.x+"},{"location":"getting_started/terra/#phylogenetic-placement","title":"Phylogenetic placement","text":"Phylogenetic placement is used to place your own sequences onto an existing phylogenetic tree. This may be used to find the closest relatives to your sequence(s). More details, including phylogenetic visualization approaches can be found in Guide to Phylogenetics
"},{"location":"getting_started/terra/#public-data-sharing","title":"Public Data Sharing","text":"SOPs for data submissions
SOP SOP Version PHB Version Compatibility Submitting SC2 Sequence Data to GISAID using Theiagen\u2019s Terra 2 GISAID Workflow v2 v2+"},{"location":"getting_started/terra/#sars-cov-2-metagenomic-analysis","title":"SARS-CoV-2 Metagenomic Analysis","text":"SOPs for SARS-CoV-2 metagenomic data analysis
SOP SOP Version PHB Version Compatibility Analyzing SARS-CoV-2 Metagenomic Samples using Freyja FASTQ v2 v2+ Plotting SARS-CoV-2 Metagenomic Sample Data using Freyja Plot v3 v2+ Creating a Dashboard Visualization of SARS-CoV-2 Metagenomic Samples using Freyja Dashboard v2 v2+ Creating Static Reference Files for Freyja Analysis in Terra using Freyja Update v2 v2+"},{"location":"workflows/data_export/concatenate_column_content/","title":"Concatenate_Column_Content","text":""},{"location":"workflows/data_export/concatenate_column_content/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Exporting Data From Terra Any taxa PHB v2.1.0 Yes Set-level"},{"location":"workflows/data_export/concatenate_column_content/#concatenate_column_content_phb","title":"Concatenate_Column_Content_PHB","text":"This set-level workflow will create a file containing all of the items from a given column in a Terra Data Table. This is useful when you want to investigate many results files. There is a video available with more information about the Concatenate_Column_Content workflow: \ud83d\udcfa Workflow Focus: Concatenate_Column_Content
"},{"location":"workflows/data_export/concatenate_column_content/#inputs","title":"Inputs","text":"This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status concatenate_column_content concatenated_file_name String The name of the output file. Include the extension, such as \".fasta\" or \".txt\". Required concatenate_column_content files_to_cat Array[File] The column that has the files you want to concatenate. Required cat_files cpu Int Number of CPUs to allocate to the task 2 Optional cat_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional cat_files docker_image String The Docker container to use for the task s-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional cat_files memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional cat_files skip_extra_headers Boolean If the files you are concatenating have identical headers, you can include only the first instance of the header and skip all of the others so they do not appear duplicated in the concatenated file. To activate this, set to true. false Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_export/concatenate_column_content/#outputs","title":"Outputs","text":"Info
Please note that if you run this workflow on the same Terra set (the same group of samples can be included in multiple Terra sets), the results will overwrite each other. We recommend either (1) renaming the output variable, or (2) creating a new set every time you run the workflow.
Variable Type Description concatenated_files File The file containing all of the items from the column you selected. concatenate_column_content_version String The version of the repository the workflow is hosted in concatenate_column_content_analysis_date String The date the workflow was run"},{"location":"workflows/data_export/transfer_column_content/","title":"Transfer_Column_Content","text":""},{"location":"workflows/data_export/transfer_column_content/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Exporting Data From Terra Any taxa PHB v1.3.0 Yes Set-level"},{"location":"workflows/data_export/transfer_column_content/#transfer_column_content_phb","title":"Transfer_Column_Content_PHB","text":"This set-level workflow will transfer all of the items from a given column in a Terra Data Table to a single GCP storage bucket location. This is useful when you want to transfer many files to another GCP storage bucket (can be a Terra workspace storage bucket or a non-Terra storage bucket).
Note
This workflow requires that the user's Terra pet-service account has sufficient privileges to read and write to the target storage bucket.
Note
If using Transfer_column_content workflow version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is transferred fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/data_export/transfer_column_content/#inputs","title":"Inputs","text":"This workflow runs on the set level.
Terra Task name input_variable Type Description Default attribute Status transfer_column_content files_to_transfer Array[File] The column that has the files you want to concatenate. Required transfer_column_content target_bucket String The GS URI of the target storage bucket. Note: Do not include spaces, but do include thegs:// at the beginning of the bucket URI Required transfer_files cpu Int Number of CPUs to allocate to the task 4 Optional transfer_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional transfer_files docker_image String The docker image used to perform the file transfer. us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional transfer_files memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_export/transfer_column_content/#outputs","title":"Outputs","text":"Info
Please note that if you run this workflow on the same Terra set (the same group of samples can be included in multiple Terra sets), the results will overwrite each other. We recommend either (1) renaming the output variable, or (2) creating a new set every time you run the workflow.
Variable Type Description transferred_files File A list of all of the files now located at the target bucket location (GSURI) transfer_column_content_version String The version of the repository the workflow is hosted in transfer_column_content_analysis_date String The date the workflow was run"},{"location":"workflows/data_export/zip_column_content/","title":"Zip_Column_Content","text":""},{"location":"workflows/data_export/zip_column_content/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Exporting Data From Terra Any taxa PHB v2.1.0 Yes Set-level"},{"location":"workflows/data_export/zip_column_content/#zip_column_content_phb","title":"Zip_Column_Content_PHB","text":"This workflow will create a zip file that contains all of the items in a column in a Terra Table.
"},{"location":"workflows/data_export/zip_column_content/#inputs","title":"Inputs","text":"This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status zip_column_content files_to_zip Array[File] The column that has the files you want to zip. Required zip_column_content zipped_file_name String The name you want your zipped file to have. The .zip file extension will be added to this name. Required zip_files cpu Int Number of CPUs to allocate to the task 2 Optional zip_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional zip_files docker_image String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional zip_files memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_export/zip_column_content/#outputs","title":"Outputs","text":"Info
Please note that if you run this workflow on the same Terra set (the same group of samples can be included in multiple Terra sets), the results will overwrite each other. We recommend either (1) renaming the output variable, or (2) creating a new set every time you run the workflow.
Variable Type Description zipped_files File The zipped file containing all of the items from the column you selected. zip_column_content_version String The version of the repository the workflow is hosted in zip_column_content_analysis_date String The date the workflow was run"},{"location":"workflows/data_import/assembly_fetch/","title":"Assembly Fetch","text":""},{"location":"workflows/data_import/assembly_fetch/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Data Import Any taxa PHB v1.3.0 Yes Sample-level"},{"location":"workflows/data_import/assembly_fetch/#assembly_fetch_phb","title":"Assembly_Fetch_PHB","text":"The Assembly_Fetch workflow downloads assemblies from NCBI. This is particularly useful when you need to align reads against a reference genome, for example during a reference-based phylogenetics workflow. This workflow can be run in two ways:
Assembly_Fetch will run only the NCBI genome download task to download this assembly,Assembly_Fetch will first use the ReferenceSeeker task to first find the closest reference genome in RefSeq to your query assembly and then will run the NCBI genome download task to download that reference assembly.Tip
NOTE: If using Assembly_Fetch workflow version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is downloaded fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/data_import/assembly_fetch/#inputs","title":"Inputs","text":"Assembly_Fetch requires the input samplename, and either the accession for a reference genome to download (ncbi_accession) or an assembly that can be used to query RefSeq for the closest reference genome to download (assembly_fasta).
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status reference_fetch samplename String Your sample's name Required reference_fetch assembly_fasta File Assembly FASTA file of your sample Optional reference_fetch ncbi_accession String NCBI accession passed to the NCBI datasets task to be downloaded. Example: GCF_000006945.2 (Salmonella enterica subsp. enterica, serovar Typhimurium str. LT2 reference genome) Optional ncbi_datasets_download_genome_accession cpu Int Number of CPUs to allocate to the task 1 Optional ncbi_datasets_download_genome_accession disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ncbi_datasets_download_genome_accession docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/ncbi-datasets:14.13.2\" Optional ncbi_datasets_download_genome_accession include_gbff Boolean set to true if you would like the GenBank Flat File (GBFF) file included in the output. It contains nucleotide sequence, metadata, and annotations. FALSE Optional ncbi_datasets_download_genome_accession include_gff3 Boolean set to true if you would like the Genomic Feature File v3 (GFF3) file included in the output. It contains nucleotide sequence, metadata, and annotations FALSE Optional ncbi_datasets_download_genome_accession memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional referenceseeker cpu Int Number of CPUs to allocate to the task 4 Optional referenceseeker disk_size Int Amount of storage (in GB) to allocate to the task 200 Optional referenceseeker docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/biocontainers/referenceseeker:1.8.0--pyhdfd78af_0\" Optional referenceseeker memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional referenceseeker referenceseeker_ani_threshold Float ANI threshold used to exclude ref genomes when ANI value less than this value. 0.95 Optional referenceseeker referenceseeker_conserved_dna_threshold Float Conserved DNA threshold used to exclude ref genomes when conserved DNA value is less than this value. 0.69 Optional referenceseeker referenceseeker_db File Database used by the referenceseeker tool that contains bacterial genomes from RefSeq release 205. Downloaded from referenceseeker GitHub repo. \"gs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-bacteria-refseq-205.v20210406.tar.gz\" Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_import/assembly_fetch/#analysis-tasks","title":"Analysis Tasks","text":"ReferenceSeeker (optional) Details NCBI Datasets Details"},{"location":"workflows/data_import/assembly_fetch/#referenceseeker","title":"ReferenceSeeker","text":"ReferenceSeeker uses your draft assembly to identify closely related bacterial, viral, fungal, or plasmid genome assemblies in RefSeq.
Databases for use with ReferenceSeeker are as follows, and can be used by pasting the gs uri in double quotation marks \" \" into the referenceseeker_db optional input:
gs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-archaea-refseq-205.v20210406.tar.gzgs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-bacteria-refseq-205.v20210406.tar.gzgs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-fungi-refseq-205.v20210406.tar.gzgs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-plasmids-refseq-205.v20210406.tar.gzgs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-viral-refseq-205.v20210406.tar.gzFor ReferenceSeeker to identify a genome, it must meet user-specified thresholds for sequence coverage (referenceseeker_conserved_dna_threshold) and identity (referenceseeker_ani_threshold). The default values for these are set according to community standards (conserved DNA >= 69 % and ANI >= 95 %). A list of closely related genomes is provided in referenceseeker_tsv. The reference genome that ranks highest according to ANI and conserved DNA values is considered the closest match and will be downloaded, with information about this provided in the assembly_fetch_referenceseeker_top_hit_ncbi_accession output.
ReferenceSeeker Technical Details
Links Task task_referenceseeker.wdl Software version 1.8.0 (\"us-docker.pkg.dev/general-theiagen/biocontainers/referenceseeker:1.8.0--pyhdfd78af_0\") Software Source Code https://github.com/oschwengers/referenceseeker Software Documentation https://github.com/oschwengers/referenceseeker Original Publication(s) ReferenceSeeker: rapid determination of appropriate reference genomes"},{"location":"workflows/data_import/assembly_fetch/#ncbi-datasets","title":"NCBI Datasets","text":"The NCBI Datasets task downloads specified assemblies from NCBI using either the virus or genome (for all other genome types) package as appropriate.
NCBI Datasets Technical Details
Links Task task_ncbi_datasets.wdl Software version 14.13.2 (us-docker.pkg.dev/general-theiagen/staphb/ncbi-datasets:14.13.2) Software Source Code https://github.com/ncbi/datasets Software Documentation https://github.com/ncbi/datasets Original Publication(s) Not known to be published"},{"location":"workflows/data_import/assembly_fetch/#outputs","title":"Outputs","text":"Variable Type Description assembly_fetch_analysis_date String Date of assembly download assembly_fetch_ncbi_datasets_assembly_data_report_json File JSON file containing report about assembly downloaded by Asembly_Fetch assembly_fetch_ncbi_datasets_assembly_fasta File FASTA file downloaded by Assembly_Fetch assembly_fetch_ncbi_datasets_docker String Docker file used for NCBI datasets assembly_fetch_ncbi_datasets_gff File Assembly downloaded by Assembly_Fetch in GFF3 format assembly_fetch_ncbi_datasets_gff3 File Assembly downloaded by Assembly_Fetch in GFF format assembly_fetch_ncbi_datasets_version String NCBI datasets version used assembly_fetch_referenceseeker_database String ReferenceSeeker database used assembly_fetch_referenceseeker_docker String Docker file used for ReferenceSeeker assembly_fetch_referenceseeker_top_hit_ncbi_accession String NCBI Accession for the top hit identified by Assembly_Fetch assembly_fetch_referenceseeker_tsv File TSV file of the top hits between the query genome and the Reference Seeker database assembly_fetch_referenceseeker_version String ReferenceSeeker version used assembly_fetch_version String The version of the repository the Assembly Fetch workflow is in"},{"location":"workflows/data_import/assembly_fetch/#references","title":"References","text":"ReferenceSeeker: Schwengers O, Hain T, Chakraborty T, Goesmann A. ReferenceSeeker: rapid determination of appropriate reference genomes. J Open Source Softw. 2020 Feb 4;5(46):1994.
NCBI datasets: datasets: NCBI Datasets is an experimental resource for finding and building datasets [Internet]. Github; [cited 2023 Apr 19]. Available from: https://github.com/ncbi/datasets
"},{"location":"workflows/data_import/basespace_fetch/","title":"BaseSpace_Fetch","text":""},{"location":"workflows/data_import/basespace_fetch/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Data Import Any taxa PHB v1.3.0 Yes Sample-level"},{"location":"workflows/data_import/basespace_fetch/#setting-up-basespace_fetch","title":"Setting up BaseSpace_Fetch","text":"The BaseSpace_Fetch workflow facilitates the transfer of Illumina sequencing data from BaseSpace (a cloud location) to a workspace on the Terra.bio platform. Rather than downloading the files to a local drive and then re-uploading them to another location, we can perform a cloud-to-cloud transfer with the BaseSpace_Fetch workflow.
Some initial set-up is required to use the workflow. To access one's BaseSpace account from within a workflow on Terra.bio, it is necessary to retrieve an access token and the API server address using the BaseSpace command-line tool. The access token is unique to a BaseSpace account. If it is necessary to transfer data from multiple BaseSpace accounts, multiple access tokens will need to be retrieved. Please see the \"Retrieving BaseSpace Access Credentials\" section below.
In this document, we provide instructions for both the retrieval of the BaseSpace access token and running the BaseSpace_Fetch workflow.
"},{"location":"workflows/data_import/basespace_fetch/#retrieving-basespace-access-credentials","title":"Retrieving BaseSpace Access Credentials","text":"This process must be performed on a command-line (ideally on a Linux or MacOS computer) before using the BaseSpace_Fetch workflow for the first time. This can be set up in Terra, however it will work in any command-line environment that has access to the internet to install & run the BaseSpace command-line tool: bs.
If you already have a command-line environment available, you can skip ahead to Step 2.
"},{"location":"workflows/data_import/basespace_fetch/#step-1-setup-jupyter-cloud-environment","title":"Step 1: Setup Jupyter Cloud Environment","text":"Click for more informationSelect the \"Environment configuration\" cloud icon on the right side of the workspace dashboard tab
Select the \"Settings\" button under Jupyter
Click \"CREATE\" in the \"Use default environment section\". There is no need to alter the default environment configuration.
Undertaking steps 1 and 2 again, you will see options to configure the environment.
gsutilOpen the \"Terminal\" app in the right side-bar of the Terra dashboard
Download and setup BaseSpace (BS) CLI (as per Illumina documentation) by following the commands below. The lines beginning with # are comments, the following lines are the commands to be copy/pasted into the termina
# create bin dir\nmkdir ~/bin\n\n# download bs cli\nwget \"https://launch.basespace.illumina.com/CLI/latest/amd64-linux/bs\" -O $HOME/bin/bs\n\n# provide proper permissions to bs cli executable \nchmod u+x $HOME/bin/bs\n\n# add the 'bs' command-line tool to the $PATH variable so that you can call the command-line tool from any directory\nexport PATH=\"$PATH:$HOME/bin/\"\n\n# authenticate with BaseSpace credentials\nbs auth\n\n# navigate to the link provided in stdout and accept the authentication request through BaseSpace\n\n# Print the api server and access token to stdout (replace the path below with the appropriate path returned by the find command above)\ncat ~/.basespace/default.cfg\nCopy and paste the contents (access_token & API server) of the default.cfg file into Terra as workspace data elements.
Best Practices for Sample Identifiers
Download the sample sheet from BaseSpace.
In Excel, set up a metadata sheet for Terra, with a row for each sample. Please feel free to use our BaseSpace_Fetch Template to help ensure the file is formatted correctly.
Create a column called basespace_sample_name and populate this with the data found under the Sample_Name column in the BaseSpace sample sheet.
Watch out
If the contents of the Sample_Name and Sample_ID columns in the BaseSpace sample sheet are different, make a basespace_sample_id column in your spreadsheet and populate this with the data found under the Sample_ID column in the BaseSpace sample sheet.
Create a basespace_collection_id column, and populate it with the BaseSpace Project or Run identifier
In Terra, navigate to the \"DATA\" tab, click \"IMPORT DATA\" then \"Upload TSV\"
Copy and paste the contents of the whole spreadsheet into the \"TEXT IMPORT\" tab and click \"START IMPORT JOB\"
BaseSpace_Fetch workflow ORBaseSpace_Fetch workflow from Dockstore via this link.BaseSpace_Fetch workflow by selecting the:Set up the BaseSpace_Fetch \"INPUTS\" form as below. Don't forget to fill out this.basespace_sample_id if your basespace sample IDs are different from the basespace sample names in the SampleSheet.csv file.
In the \"OUTPUTS\" tab, select \"use defaults\", then click \"SAVE\".
Call Caching Disabled
If using BaseSpace_Fetch workflow version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is downloaded fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
Sample_Name and Sample_ID
If the Sample_Name and Sample_ID in the BaseSpace sample sheet are different, set the basespace_sample_id input attribute to \"this.basespace_sample_id\".
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status basespace_fetch access_token String The access token is used in place of a username and password to allow the workflow to access the user account in BaseSpace from which the data is to be transferred. It is an alphanumeric string that is 32 characters in length. Example: 9e08a96471df44579b72abf277e113b7 Required basespace_fetch api_server String The API server is the web address to which data transfer requests can be sent by the workflow. Use this API server if you are unsure:\"https://api.basespace.illumina.com\" (this is the default set by the command-line tool) Required basespace_fetch basespace_collection String The collection ID is the BaseSpace Run or Project where the data to be transferred is stored. Required basespace_fetch basespace_sample_name String The BaseSpace sample name is the sample identifier used in BaseSpace. This identifier is set on the sample sheet at the onset of an Illumina sequencing run. Required basespace_fetch sample_name String The sample name is the sample identifier used in the Terra.bio data table corresponding to the metadata associated with the sample to be transferred from BaseSpace Required basespace_fetch basespace_sample_id String The BaseSpace sample ID is an optional additional identifier used in BaseSpace. If a sample has a BaseSpace sample ID it should be available on the sample sheet and must be included in the metadata sheet upload prior to running BaseSpace_Fetch. Optional fetch_bs cpu Int This input is the number of CPU's used in the data transfer. To facilitate the transfer of many files this runtime parameter may be increased. 2 Optional fetch_bs disk_size Int The disk size is the amount of storage in GigaBytes (GB) requested for the VM to run the data transfer task. 100 Optional fetch_bs docker_image String The Docker image used to run BaseSpace_Fetch task. \"us-docker.pkg.dev/general-theiagen/theiagen/basespace_cli:1.2.1\" Optional fetch_bs memory Int The memory is the amount of RAM/memory requested for running the data transfer task. 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_import/basespace_fetch/#outputs","title":"Outputs","text":"The outputs of this workflow will be the fastq files imported from BaseSpace into the data table where the sample ID information had originally been uploaded.
Variable Type Description basespace_fetch_analysis_date String Date of download basespace_fetch_version String Version of the workflow read1 File File with forward-facing reads read2 File File with reverse-facing read"},{"location":"workflows/data_import/create_terra_table/","title":"Create_Terra_Table","text":""},{"location":"workflows/data_import/create_terra_table/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Data Import Any taxa PHB v2.2.0 Yes Sample-level"},{"location":"workflows/data_import/create_terra_table/#create_terra_table_phb","title":"Create_Terra_Table_PHB","text":"The manual creation of Terra tables can be tedious and error-prone. This workflow will automatically create your Terra data table when provided with the location of the files.
"},{"location":"workflows/data_import/create_terra_table/#inputs","title":"Inputs","text":"Default Behavior
Files with underscores and/or decimals in the sample name are not recognized; please use dashes instead.
For example, name.banana.hello_yes_please.fastq.gz will become \"name\". This means that se-test_21.fastq.gz and se-test_22.fastq.gz will not be recognized as separate samples.
This can be changed by providing information in the file_ending optional input parameter. See below for more information.
data_location_path","text":""},{"location":"workflows/data_import/create_terra_table/#using-the-terra-data-uploader","title":"Using the Terra data uploader","text":"Click for more information Once you have named your new collection, you will see the collection name directly above where you can drag-and-drop your data files, or on the same line as the Upload button. Right-click the collection name and select \"Copy link address.\" Paste the copied link into the data_location_path variable, remembering to enclose it in quotes.
Note
If you click \"Next\" after uploading your files, it will ask for a metadata TSV. You do not have to provide this, and can instead exit the window. Your data will still be uploaded.
"},{"location":"workflows/data_import/create_terra_table/#using-the-files-section-in-the-data-tab","title":"Using the \"Files\" section in the Data tab","text":"Click for more informationNavigate to the folder where your data is (\"example_upload\" in this example) and right-click on the folder name and select \"Copy link address.\"
If you uploaded data with the Terra data uploader, your collection will be nested in the \"uploads\" folder.
"},{"location":"workflows/data_import/create_terra_table/#how-to-determine-the-appropriate-file_ending-for-your-data","title":"How to determine the appropriatefile_ending for your data","text":"The file_ending should be a substring of your file names that is held in common. See the following examples:
One or more elements in common
If you have the following files:
The default behavior would result in a single entry in the table called \"sample\" which is incorrect. You can rectify this by providing an appropriate file_ending for your samples.
In this group, the desired sample names are \"sample_01\" and \"sample_02\". For all the files following the desired names, the text contains _R. If we provide \"_R\" as our file_ending, then \"sample_01\" and \"sample_02\" will appear in our table with the appropriate read files.
No elements in common
If you have the following files:
The default behavior would result in a single entry in the table called \"sample\" which is incorrect. You can rectify this by providing an appropriate file_ending for your samples.
In this group, the desired sample names are \"sample_01\" and \"sample_02\". However, in this example, there is no common text following the sample name. Providing \"_\" would result in the same behavior as default. We can provide two different patterns in the file_ending variable: \"_1,_2\" to capture all possible options. By doing this, \"sample_01\" and \"sample_02\" will appear in our table with the appropriate read files.
To include multiple file endings, please separate them with commas, as shown in the \"no elements in common\" section.
"},{"location":"workflows/data_import/create_terra_table/#outputs","title":"Outputs","text":"Your table will automatically appear in your workspace with the following fields:
new_table_name_id column), which will be the section of the file's name before any decimals or underscores (unless file_ending is provided)sample01.lane2_flowcell3.fastq.gz will be represented by sample01 in the tablesample02_negativecontrol.fastq.gz will be represented by sample02 in the tablefile_ending for your data\" above to learn how to change this default behaviorYour data in the appropriate columns, dependent on the values of assembly_data and paired_end
assembly_data is true paired_end is true assembly_data AND paired_end are false read1 \u274c \u2705 \u2705 read2 \u274c \u2705 \u274c assembly_fasta \u2705 \u274c \u274c The date of upload under the upload_date column
table_created_by, to indicate the table was made by the Create_Terra_Table_PHB workflow.The SRA_Fetch workflow downloads sequence data from NCBI's Sequence Read Archive (SRA). It requires an SRA run accession then populates the associated read files to a Terra data table.
Read files associated with the SRA run accession provided as input are copied to a Terra-accessible Google bucket. Hyperlinks to those files are shown in the \"read1\" and \"read2\" columns of the Terra data table.
"},{"location":"workflows/data_import/sra_fetch/#inputs","title":"Inputs","text":"This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status fetch_sra_to_fastq sra_accession String SRA, ENA, or DRA accession number Required fetch_sra_to_fastq cpu Int Number of CPUs to allocate to the task 2 Optional fetch_sra_to_fastq disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional fetch_sra_to_fastq docker_image String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/biocontainers/fastq-dl:2.0.4--pyhdfd78af_0\" Optional fetch_sra_to_fastq fastq_dl_options String Additional parameters to pass to fastq_dl from here \"--provider sra\" Optional fetch_sra_to_fastq memory Int Amount of memory/RAM (in GB) to allocate to the task 8 OptionalThe only required input for the SRA_Fetch workflow is an SRA run accession beginning \"SRR\", an ENA run accession beginning \"ERR\", or a DRA run accession which beginning \"DRR\".
Please see the NCBI Metadata and Submission Overview for assistance with identifying accessions. Briefly, NCBI-accessioned objects have the following naming scheme:
STUDY SRP# SAMPLE SRS# EXPERIMENT SRX# RUN SRR#"},{"location":"workflows/data_import/sra_fetch/#outputs","title":"Outputs","text":"Read data are available either with full base quality scores (SRA Normalized Format) or with simplified quality scores (SRA Lite). The\u00a0SRA Normalized Format\u00a0includes full, per-base quality scores, whereas base quality scores have been simplified in SRA Lite files. This means that all quality scores have been artificially set to Q-30 or Q3. More information about these files can be found here.
Given the lack of usefulness of SRA Lite formatted FASTQ files, we try to avoid these by selecting as provided SRA directly (SRA-Lite is more probably to be the file synced to other repositories), but some times downloading these files is unavoidable. To make the user aware of this, a warning column is present that is populated when an SRA-Lite file is detected.
Variable Type Description Production Status read1 File File containing the forward reads Always produced read2 File File containing the reverse reads (not availablae for single-end or ONT data) Produced only for paired-end data fastq_dl_date String The date of download Always produced fastq_dl_docker String The docker used Always produced fastq_dl_metadata File File containing metadata of the provided accession such as submission_accession, library_selection, instrument_platform, among others Always produced fastq_dl_version String Fastq_dl version used Always produced fastq_dl_warning String This warning field is populated if SRA-Lite files are detected. These files contain all quality encoding as Phred-30 or Phred-3. Depends on internal workflow logic"},{"location":"workflows/data_import/sra_fetch/#references","title":"References","text":"This workflow relies on fastq-dl, a very handy bioinformatics tool by Robert A. Petit III
"},{"location":"workflows/genomic_characterization/freyja/","title":"Freyja Workflow Series","text":""},{"location":"workflows/genomic_characterization/freyja/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Viral PHB v2.3.0 Yes Sample-level, Set-level"},{"location":"workflows/genomic_characterization/freyja/#freyja-overview","title":"Freyja Overview","text":"Freyja is a tool for analysing viral mixed sample genomic sequencing data. Developed by Joshua Levy from the Andersen Lab, it performs two main steps:
Additional post-processing steps can produce visualizations of aggregated samples.
Wastewater and more
The typical use case of Freyja is to analyze mixed SARS-CoV-2 samples from a sequencing dataset, most often wastewater.
Default Values
The defaults included in the Freyja workflows reflect this use case but can be adjusted for other pathogens. See the Running Freyja on other pathogens section for more information.
Figure 1: Workflow Diagram for Freyja_FASTQ_PHB workflow
Four workflows have been created that perform different parts of Freyja:
The main workflow is Freyja_FASTQ_PHB (Figure 1). Depending on the type of input data (Illumina paired-end, Illumina single-end or ONT), it runs various QC modules before aligning the sample with either BWA (Illumina) or minimap2 (ONT) to the provided reference file, followed by iVar for primer trimming. After the preprocessing is completed, Freyja is run to generate relative lineage abundances (demix) from the sample. Optional bootstrapping may be performed.
Data Compatability
The Freyja_FASTQ_PHB workflow is compatible with the following input data types:
- Ilumina Single-End\n- Illumina Paired-End\n- Oxford Nanopore\nFreyja_Update_PHB will copy the SARS-CoV-2 reference files (curated_lineages.json and usher_barcodes.feather) from the source repository to a user-specific Google Cloud Storage (GCP) location (often a Terra.bio workspace-associated bucket). These files can then be used as input for the Freyja_FASTQ_PHB workflow.
Two options are available to visualize the Freyja results: Freyja_Plot_PHB and Freyja_Dashboard_PHB. Freyja_Plot_PHB aggregates multiple samples using output from Freyja_FASTQ_PHB to generate a plot that shows fractional abundance estimates for all samples. including the option to plot sample collection date information. Alternatively, Freyja_Dashboard_PHB aggregates multiple samples using output from Freyja_FASTQ to generate an interactive visualization. This workflow requires an additional input field called viral load, which is the number of viral copies per liter.
"},{"location":"workflows/genomic_characterization/freyja/#figure1","title":"Figure 1","text":"Depending on the type of data (Illumina or Oxford Nanopore), the Read QC and Filtering steps, as well as the Read Alignment steps use different software. The user can specify if the barcodes and lineages file should be updated with freyja update before running Freyja or if bootstrapping is to be performed with freyja boot.
This workflow will copy the Freyja reference files (usher_barcodes.feather and curated_lineages.json) to a GCP URI of your choice for usage in Freyja_FASTQ_PHB.
We recommend running this workflow with \"Run inputs defined by file paths\" selected since no information from a Terra data table is actually being used. We also recommend turning off call caching so new information is retrieved every time.
Terra Task Name Variable Type Description Default Value Terra Status freyja_update gcp_uri String The path where you want the Freyja reference files to be stored. Include gs:// at the beginning of the string. Full example with a Terra workspace bucket: \"gs://fc-87ddd67a-c674-45a8-9651-f91e3d2f6bb7\" Required freyja_update_refs cpu Int Number of CPUs to allocate to the task 4 Optional freyja_update_refs disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional freyja_update_refs docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.2-11_30_2024-02-00-2024-12-02\" Optional freyja_update_refs memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional transfer_files cpu Int Number of CPUs to allocate to the task 2 Optional transfer_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional transfer_files docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional transfer_files memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional"},{"location":"workflows/genomic_characterization/freyja/#outputs","title":"Outputs","text":"This workflow does not produce any outputs that appear in a Terra data table. The reference files will appear at the location specified with the gcp_uri input variable.
Freyja measures SNV frequency and sequencing depth at each position in the genome to return an estimate of the true lineage abundances in the sample. The method uses lineage-defining \"barcodes\" that, for SARS-CoV-2, are derived from the UShER global phylogenetic tree as a base set for demixing. Freyja_FASTQ_PHB returns as output a TSV file that includes the lineages present and their corresponding abundances, along with other values.
The Freyja_FASTQ_PHB workflow is compatible with the multiple input data types: Ilumina Single-End, Illumina Paired-End and Oxford Nanopore. Depending on the type of input data, different input values are used.
Table 1: Freyja_FASTQ_PHB input configuration for different types of input data.
Table Columns Illumina Paired-End Illumina Single-End Oxford Nanopore read1 \u2705 \u2705 \u2705 read2 \u2705 \u274c \u274c ontfalse false true"},{"location":"workflows/genomic_characterization/freyja/#freyja_fastq-inputs","title":"Freyja_FASTQ Inputs","text":"This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status freyja_fastq primer_bed File The bed file containing the primers used when sequencing was performed Required freyja_fastq read1 File The raw forward-facing FASTQ file (Illumina or ONT) Required freyja_fastq reference_genome File The reference genome to use; should match the reference used for alignment (Wuhan-Hu-1) Required freyja_fastq samplename String The name of the sample Required bwa cpu Int Number of CPUs to allocate to the task 6 Optional bwa disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional bwa docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan Optional bwa memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional freyja bootstrap Boolean Perform bootstrapping FALSE Optional freyja confirmed_only Boolean Include only confirmed SARS-CoV-2 lineages FALSE Optional freyja cpu Int Number of CPUs to allocate to the task 2 Optional freyja disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional freyja docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.2-11_30_2024-02-00-2024-12-02\" Optional freyja eps Float The minimum lineage abundance cut-off value 0.001 Optional freyja freyja_barcodes String Custom barcode file. Does not need to be provided if update_db is true if the freyja_pathogen is provided. None Optional freyja freyja_lineage_metadata File File containing the lineage metadata; the \"curated_lineages.json\" file found https://github.com/andersen-lab/Freyja/tree/main/freyja/data can be used for this variable. Does not need to be provided if update_db is true or if the freyja_pathogen is provided. None Optional, Required freyja freyja_pathogen String Pathogen of interest, used if not providing the barcodes and lineage metadata files. Options: SARS-CoV-2, MPXV, H5NX, H1N1pdm, FLU-B-VIC, MEASLESN450, MEASLES, RSVa, RSVb None Optional freyja memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional freyja number_bootstraps Int The number of bootstraps to perform (only used if bootstrap = true) 100 Optional freyja update_db Boolean Updates the Freyja reference files (the usher barcodes and lineage metadata files) but will not save them as output (use Freyja_Update for that purpose). If set to true, thefreyja_lineage_metadata and freyja_barcodes files are not required. FALSE Optional freyja_fastq depth_cutoff Int The minimum coverage depth with which to exclude sites below this value and group identical barcodes 10 Optional freyja_fastq kraken2_target_organism String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. \"Severe acute respiratory syndrome coronavirus 2\" Optional freyja_fastq ont Boolean Indicates if the input data is derived from an ONT instrument. FALSE Optional freyja_fastq read2 File The raw reverse-facing FASTQ file (Illumina only) Optional freyja_fastq trimmomatic_minlen Int The minimum length cut-off when performing read cleaning 25 Optional get_fasta_genome_size cpu Int Number of CPUs to allocate to the task 1 Optional get_fasta_genome_size disk_size Int Amount of storage (in GB) to allocate to the task 10 Optional get_fasta_genome_size docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/biocontainers/seqkit:2.4.0--h9ee0642_0\" Optional get_fasta_genome_size memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional minimap2 cpu Int Number of CPUs to allocate to the task 2 Optional minimap2 disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional minimap2 docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/staphb/minimap2:2.22\" Optional minimap2 memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional minimap2 query2 File Internal component. Do not modify None Do not modify, Optional nanoplot_clean cpu Int Number of CPUs to allocate to the task 4 Optional nanoplot_clean disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional nanoplot_clean docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0\" Optional nanoplot_clean max_length Int Maximum read length for nanoplot 100000 Optional nanoplot_clean memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional nanoplot_raw cpu Int Number of CPUs to allocate to the task 4 Optional nanoplot_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional nanoplot_raw docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0\" Optional nanoplot_raw max_length Int Maximum read length for nanoplot 100000 Optional nanoplot_raw memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional primer_trim cpu Int Number of CPUs to allocate to the task 2 Optional primer_trim disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional primer_trim docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan\" Optional primer_trim keep_noprimer_reads Boolean Include reads with no primers TRUE Optional primer_trim memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_pe adapters File A FASTA file containing adapter sequence None Optional read_QC_trim_pe bbduk_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_pe call_kraken Boolean By default this is set to false to skip kraken2; set to true to run kraken2 but a database must be also provided via the kraken_db input parameter for this to run successfully FALSE Optional read_QC_trim_pe call_midas Boolean By default this is set to true to run MIDAS; set to false to skip MIDAS FALSE Optional read_QC_trim_pe fastp_args String Additional arguments to use with fastp \"--detect_adapter_for_pe -g -5 20 -3 20\" Optional read_QC_trim_pe kraken_cpu Int Number of CPUs to allocate to the task 4 Optional read_QC_trim_pe kraken_db File A kraken2 database to use with the kraken2 optional task. The file must be a .tar.gz kraken2 database. None Optional, Sometimes required read_QC_trim_pe kraken_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional read_QC_trim_pe kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_pe midas_db File Database to use with MIDAS. Not required as one will be auto-selected when running the MIDAS task. None Optional, Sometimes required read_QC_trim_pe phix File The file containing the phix sequence to be used during bbduk task None Optional read_QC_trim_pe read_processing String Options: \"trimmomatic\" or \"fastp\" to indicate which read trimming module to use \"trimmomatic\" Optional read_QC_trim_pe read_qc String Allows the user to decide between fastq_scan (default) and fastqc for the evaluation of read quality. fastq_scan Optional read_QC_trim_pe target_organism String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. None Optional read_QC_trim_pe trim_quality_trim_score Int The minimum quality score to keep during trimming 30 Optional read_QC_trim_pe trim_window_size Int The window size to use during trimming 4 Optional read_QC_trim_pe trimmomatic_args String Additional command-line arguments to use with trimmomatic None Optional read_QC_trim_ont call_kraken Boolean By default this is set to false to skip kraken2; set to true to run kraken2 but a database must be also provided via the kraken_db input parameter for this to run successfully FALSE Optional read_QC_trim_ont downsampling_coverage Float The depth to downsample to with Rasusa. Internal component. Do not modify. 150 Do not modify, Optional read_QC_trim_ont genome_length Int Internal component. Do not modify None Do not modify, Optional read_QC_trim_ont kraken_cpu Int Number of CPUs to allocate to the task 4 Optional read_QC_trim_ont kraken_db File A kraken2 database to use with the kraken2 optional task. The file must be a .tar.gz kraken2 database. None Optional read_QC_trim_ont kraken_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional read_QC_trim_ont kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_ont max_length Int Internal component, do not modify Do not modify, Optional read_QC_trim_ont min_length Int Internal component, do not modify Do not modify, Optional read_QC_trim_ont run_prefix String Internal component, do not modify Do not modify, Optional read_QC_trim_ont target_organism String This string is searched for in the kraken2 outputs to extract the read percentage Optional read_QC_trim_se adapters File A FASTA file containing adapter sequence None Optional read_QC_trim_se bbduk_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_se call_kraken Boolean By default this is set to false to skip kraken2; set to true to run kraken2 but a database must be also provided via the kraken_db input parameter for this to run successfully FALSE Optional read_QC_trim_se call_midas Boolean By default this is set to true to run MIDAS; set to false to skip MIDAS FALSE Optional read_QC_trim_se fastp_args String Additional arguments to use with fastp \"--detect_adapter_for_pe -g -5 20 -3 20\" Optional read_QC_trim_se kraken_cpu Int Number of CPUs to allocate to the task 4 Optional read_QC_trim_se kraken_db File A kraken2 database to use with the kraken2 optional task. The file must be a .tar.gz kraken2 database. None Optional read_QC_trim_se kraken_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional read_QC_trim_se kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_se midas_db File Database to use with MIDAS. Not required as one will be auto-selected when running the MIDAS task. None Optional, Sometimes required read_QC_trim_se phix File The file containing the phix sequence to be used during bbduk task None Optional read_QC_trim_se read_processing String Options: \"trimmomatic\" or \"fastp\" to indicate which read trimming module to use \"trimmomatic\" Optional read_QC_trim_se read_qc String Allows the user to decide between fastq_scan (default) and fastqc for the evaluation of read quality. fastq_scan Optional read_QC_trim_se target_organism String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. None Optional read_QC_trim_se trim_quality_min_score Int The minimum quality score to keep during trimming 30 Optional read_QC_trim_se trim_window_size Int The window size to use during trimming 4 Optional read_QC_trim_se trimmomatic_args String Additional command-line arguments to use with trimmomatic None Optional sam_to_sorted_bam cpu Int Number of CPUs to allocate to the task 2 Optional sam_to_sorted_bam disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional sam_to_sorted_bam docker String Docker image used for this task. us-docker.pkg.dev/general-theiagen/staphb/samtools:1.17 Optional sam_to_sorted_bam memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/freyja/#freyja_fastq-analysis-tasks","title":"Freyja_FASTQ Analysis Tasks","text":"read_QC_trim_pe Details read_QC_trim_se Details read_QC_trim_ont Details bwa Details minimap2 Details primer_trim Details freyja Details"},{"location":"workflows/genomic_characterization/freyja/#read_QC_trim_pe","title":"read_QC_trim_pe","text":"This task runs a sub-workflow that gathers basic QC information, trimming (either with trimmomatic or fastp), human read scrubbing, and taxonomic identification (Kraken2). Optional parameters do not need to be modified. For information regarding the individual tasks performed during this, please visit the TheiaCoV documentation.
Read_QC_Trim_PE Technical Details
Links Task wf_read_QC_trim_pe.wdl"},{"location":"workflows/genomic_characterization/freyja/#read_QC_trim_se","title":"read_QC_trim_se","text":"This task runs a sub-workflow that gathers basic QC information, trimming (either with trimmomatic or fastp), human read scrubbing, and taxonomic identification (Kraken2). Optional parameters do not need to be modified. For information regarding the individual tasks performed during this, please visit the TheiaCoV documentation.
Read_QC_Trim_SE Technical Details
Links Task wf_read_QC_trim_se.wdl"},{"location":"workflows/genomic_characterization/freyja/#read_QC_trim_ont","title":"read_QC_trim_ont","text":"This task runs a sub-workflow that gathers basic QC information, trimming (nanoplot), human read scrubbing, and taxonomic identification (Kraken2). Optional parameters do not need to be modified. For information regarding the individual tasks performed during this, please visit the TheiaCoV documentation.
Read_QC_Trim_ONT Technical Details
Links Task wf_read_QC_trim_ont.wdl"},{"location":"workflows/genomic_characterization/freyja/#bwa","title":"bwa","text":"This task aligns the cleaned short reads (Illumina) to the reference genome provided by the user.
BWA Technical Details
Links Task task_bwa.wdl Software Source Code https://github.com/lh3/bwa Software Documentation https://bio-bwa.sourceforge.net/ Original Publication(s) Fast and accurate short read alignment with Burrows-Wheeler transform"},{"location":"workflows/genomic_characterization/freyja/#minimap2","title":"minimap2","text":"This task aligns the cleaned long reads (Oxford Nanopore) to the reference genome provided by the user.
Minimap2 Technical Details
Links Task task_minimap2.wdl Software Source Code https://github.com/lh3/minimap2 Software Documentation https://lh3.github.io/minimap2/ Original Publication(s) Minimap2: pairwise alignment for nucleotide sequences"},{"location":"workflows/genomic_characterization/freyja/#primer_trim","title":"primer_trim","text":"This task trims the primer sequences from the aligned bam file with iVar. The optional input, keep_noprimer_reads, does not have to be modified.
Primer Trim Technical Details
Links Task task_ivar_primer_trim.wdl Software Source Code https://github.com/andersen-lab/ivar Software Documentation https://andersen-lab.github.io/ivar/html/manualpage.html Original Publication(s) An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar"},{"location":"workflows/genomic_characterization/freyja/#freyja","title":"freyja","text":"The Freyja task will call variants and capture sequencing depth information to identify the relative abundance of lineages present. Optionally, if bootstrap is set to true, bootstrapping will be performed. After the optional bootstrapping step, the variants are demixed.
Freyja Technical Details
Links Task task_freyja_one_sample.wdl Software Source Code https://github.com/andersen-lab/Freyja Software Documentation https://andersen-lab.github.io/Freyja/index.html#"},{"location":"workflows/genomic_characterization/freyja/#freyja_fastq-outputs","title":"Freyja_FASTQ Outputs","text":"The main output file used in subsequent Freyja workflows is found under the freyja_demixed column. This TSV file takes on the following format:
summarized\u00a0array denotes a sum of all lineage abundances in a particular WHO designation (i.e. B.1.617.2 and AY.6 abundances are summed in the above example), otherwise they are grouped into \"Other\".lineage\u00a0array lists the identified lineages in descending orderabundances\u00a0array contains the corresponding abundances estimates.resid\u00a0corresponds to the residual of the weighted least absolute deviation problem used to estimate lineage abundances.coverage\u00a0value provides the 10x coverage estimate (percent of sites with 10 or greater reads)Click \"Ignore empty outputs\"
When running the Freyja_FASTQ_PHB workflow, it is recommended to select the \"Ignore empty outputs\" option in the Terra UI. This will hide the output columns that will not be generated for your input data type.
Variable Type Description Input Data Type aligned_bai File Index companion file to the bam file generated during the consensus assembly process ONT, PE, SE aligned_bam File Primer-trimmed BAM file; generated during consensus assembly process ONT, PE, SE alignment_method String The method used to generate the alignment ONT, PE, SE bbduk_docker String Docker image used to run BBDuk PE, SE bwa_version String Version of BWA used to map read data to the reference genome PE, SE fastp_html_report File The HTML report made with fastp PE, SE fastp_version String Version of fastp software used PE, SE fastq_scan_clean1_json File JSON file output fromfastq-scan containing summary stats about clean forward read quality and length PE, SE fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length PE fastq_scan_num_reads_clean_pairs String Number of clean read pairs PE fastq_scan_num_reads_clean1 Int Number of clean forward reads PE, SE fastq_scan_num_reads_clean2 Int Number of clean reverse reads PE fastq_scan_num_reads_raw_pairs String Number of raw read pairs PE fastq_scan_num_reads_raw1 Int Number of raw forward reads PE, SE fastq_scan_num_reads_raw2 Int Number of raw reverse reads PE fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length PE, SE fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length PE fastq_scan_version String Version of fastq_scan used for read QC analysis PE, SE fastqc_clean1_html File Graphical visualization of clean forward read quality from fastqc to open in an internet browser PE, SE fastqc_clean2_html File Graphical visualization of clean reverse read quality from fastqc to open in an internet browser PE fastqc_docker String Docker container used for fastqc PE, SE fastqc_num_reads_clean_pairs String Number of read pairs after cleaning by fastqc PE fastqc_num_reads_clean1 Int Number of forward reads after cleaning by fastqc PE, SE fastqc_num_reads_clean2 Int Number of reverse reads after cleaning by fastqc PE fastqc_num_reads_raw_pairs String Number of input read pairs by fastqc PE fastqc_num_reads_raw1 Int Number of input forward reads by fastqc PE, SE fastqc_num_reads_raw2 Int Number of input reverse reads by fastqc PE fastqc_raw1_html File Graphical visualization of raw forward read quality from fastqc to open in an internet browser PE, SE fastqc_raw2_html File Graphical visualization of raw reverse read qualityfrom fastqc to open in an internet browser PE fastqc_version String Version of fastqc software used PE, SE freyja_barcode_file File Barcode file used ONT, PE, SE freyja_barcode_version String Name of barcode file used, or the date if update_db is true ONT, PE, SE freyja_bootstrap_lineages File A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each lineage ONT, PE, SE freyja_bootstrap_lineages_pdf File A boxplot of the bootstrap lineages CSV file ONT, PE, SE freyja_bootstrap_summary File A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each WHO designated VOI/VOC ONT, PE, SE freyja_bootstrap_summary_pdf File A boxplot of the bootstrap summary CSV file ONT, PE, SE freyja_coverage Float Coverage identified by Freyja and parsed from freyja_demixed file ONT, PE, SE freyja_demixed File The main output TSV; see the section directly above this table for an explanation ONT, PE, SE freyja_depths File A TSV listing the depth of every position ONT, PE, SE freyja_fastq_wf_analysis_date String Date of analysis ONT, PE, SE freyja_fastq_wf_version String The version of the Public Health Bioinformatics (PHB) repository used ONT, PE, SE freyja_lineage_metadata_file File Lineage metadata JSON file used. Can be the one provided as input or downloaded by Freyja if update_db is true ONT, PE, SE freyja_metadata_version String Name of lineage metadata file used, or the date if update_db is true ONT, PE, SE freyja_barcode_file File Barcode feather file used. Can be the one provided as input or downloaded by Freyja if update_db is true ONT, PE, SE freyja_variants File The TSV file containing the variants identified by Freyja ONT, PE, SE freyja_version String version of Freyja used ONT, PE, SE ivar_version_primtrim String Version of iVar for running the iVar trim command ONT, PE, SE kraken_human Float Percent of human read data detected using the Kraken2 software ONT, PE, SE kraken_human_dehosted Float Percent of human read data detected using the Kraken2 software after host removal ONT, PE, SE kraken_report File Full Kraken report ONT, PE, SE kraken_report_dehosted File Full Kraken report after host removal ONT, PE, SE kraken_sc2 String Percent of SARS-CoV-2 read data detected using the Kraken2 software ONT, PE, SE kraken_sc2_dehosted String Percent of SARS-CoV-2 read data detected using the Kraken2 software after host removal ONT, PE, SE kraken_version String Version of Kraken software used ONT, PE, SE minimap2_docker String Docker image used to run minimap2 ONT minimap2_version String Version of minimap2 used ONT nanoplot_html_clean File Clean read file ONT nanoplot_html_raw File Raw read file ONT nanoplot_num_reads_clean1 Int Number of clean reads for the forward-facing file ONT nanoplot_num_reads_raw1 Int Number of reads for the forward-facing file ONT nanoplot_r1_est_coverage_clean Float Estimated coverage on the clean reads by nanoplot ONT nanoplot_r1_est_coverage_raw Float Estimated coverage on the raw reads by nanoplot ONT nanoplot_r1_mean_q_clean Float Mean quality score of clean forward reads ONT nanoplot_r1_mean_q_raw Float Mean quality score of raw forward reads ONT nanoplot_r1_mean_readlength_clean Float Mean read length of clean forward reads ONT nanoplot_r1_mean_readlength_raw Float Mean read length of raw forward reads ONT nanoplot_r1_median_q_clean Float Median quality score of clean forward reads ONT nanoplot_r1_median_q_raw Float Median quality score of raw forward reads ONT nanoplot_r1_median_readlength_clean Float Median read length of clean forward reads ONT nanoplot_r1_median_readlength_raw Float Median read length of raw forward reads ONT nanoplot_r1_n50_clean Float N50 of clean forward reads ONT nanoplot_r1_n50_raw Float N50 of raw forward reads ONT nanoplot_r1_stdev_readlength_clean Float Standard deviation read length of clean forward reads ONT nanoplot_r1_stdev_readlength_raw Float Standard deviation read length of raw forward reads ONT nanoplot_tsv_clean File Output TSV file created by nanoplot ONT nanoplot_tsv_raw File Output TSV file created by nanoplot ONT nanoq_version String Version of nanoq used in analysis ONT primer_bed_name String Name of the primer bed file used for primer trimming ONT, PE, SE primer_trimmed_read_percent Float Percentage of read data with primers trimmed as determined by iVar trim ONT, PE, SE read1_clean File Forward read file after quality trimming and adapter removal ONT, PE, SE read1_dehosted File Dehosted forward reads ONT, PE, SE read2_clean File Reverse read file after quality trimming and adapter removal PE read2_dehosted File Dehosted reverse reads PE samtools_version String The version of SAMtools used to sort and index the alignment file ONT, PE, SE samtools_version_primtrim String The version of SAMtools used to create the pileup before running iVar trim ONT, PE, SE trimmomatic_docker String Docker container for Trimmomatic PE, SE trimmomatic_version String The version of Trimmomatic used PE, SE"},{"location":"workflows/genomic_characterization/freyja/#freyja_plot","title":"Freyja_Plot_PHB","text":"This workflow visualizes aggregated freyja_demixed output files produced by Freyja_FASTQ in a single plot (pdf format) which provides fractional abundance estimates for all aggregated samples.
Options exist to provide lineage-specific breakdowns and/or sample collection time information.
"},{"location":"workflows/genomic_characterization/freyja/#freyja_plot-inputs","title":"Freyja_Plot Inputs","text":"This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status freyja_plot freyja_demixed Array[File] An array containing the output files (freyja_demixed) made by Freyja_FASTQ Required freyja_plot freyja_plot_name String The name of the plot to be produced. Example: \"my-freyja-plot\" Required freyja_plot samplename Array[String] An array containing the names of the samples Required freyja_plot collection_date Array[String] An array containing the collection dates for the sample (YYYY-MM-DD format) Optional freyja_plot_task cpu Int Number of CPUs to allocate to the task 2 Optional freyja_plot_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional freyja_plot_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.2-11_30_2024-02-00-2024-12-02 Optional freyja_plot_task memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional freyja_plot_task mincov Int The minimum genome coverage used as a cut-off of data to include in the plot 60 Optional freyja_plot_task plot_day_window Int The width of the rolling average window; only used if plot_time_interval is \"D\" 14 Optional freyja_plot_task plot_lineages Boolean If true, will plot a lineage-specific breakdown FALSE Optional freyja_plot_task plot_time Boolean If true, will plot sample collection time information (requires the collection_date input variable) FALSE Optional freyja_plot_task plot_time_interval String Options: \"MS\" for month, \"D\" for day MS Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/freyja/#analysis-tasks","title":"Analysis Tasks","text":"freyja_plot_task Details"},{"location":"workflows/genomic_characterization/freyja/#freyja_plot_task","title":"freyja_plot_task","text":"This task will aggregate multiple samples together, and then creates a plot. Several optional inputs dictate the plot appearance (see each variable's description for more information).
Freyja Plot Technical Details
Links Task wf_freyja_plot.wdl Software Source Code https://github.com/andersen-lab/Freyja Software Documentation https://github.com/andersen-lab/Freyja"},{"location":"workflows/genomic_characterization/freyja/#freyja_plot-outputs","title":"Freyja_Plot Outputs","text":"Variable Type Description freyja_demixed_aggregate File A TSV file that summarizes thefreyja_demixed otuputs for all samples freyja_plot File A PDF of the plot produced by the workflow freyja_plot_metadata File The metadata used to create the plot freyja_plot_version String The version of Freyja used freyja_plot_wf_analysis_date String The date of analysis freyja_plot_wf_version String The version of the Public Health Bioinformatics (PHB) repository used"},{"location":"workflows/genomic_characterization/freyja/#freyja_dashboard","title":"Freyja_Dashboard_PHB","text":"This workflow creates a group of interactive visualizations based off of the aggregated freyja_demixed output files produced by Freyja_FASTQ called a \"dashboard.\" Creating this dashboard requires knowing the viral load of your samples (viral copies/L).
This dashboard is not \"live\" \u2014 that is, you must rerun the workflow every time you want new data to be included in the visualizations.
"},{"location":"workflows/genomic_characterization/freyja/#freyja_dashboard-inputs","title":"Freyja_Dashboard Inputs","text":"This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status freyja_dashboard collection_date Array[String] An array containing the collection dates for the sample (YYYY-MM-DD format) Required freyja_dashboard freyja_dashboard_title String The name of the dashboard to be produced. Example: \"my-freyja-dashboard\" Required freyja_dashboard freyja_demixed Array[File] An array containing the output files (freyja_demixed) made by Freyja_FASTQ workflow Required freyja_dashboard samplename Array[String] An array containing the names of the samples Required freyja_dashboard viral_load Array[String] An array containing the number of viral copies per liter Required freyja_dashboard dashboard_intro_text File A file containing the text to be contained at the top of the dashboard. SARS-CoV-2 lineage de-convolution performed by the Freyja workflow (https://github.com/andersen-lab/Freyja). Optional freyja_dashboard_task config File (found in the optional section, but is required) A yaml file that applies various configurations to the dashboard, such as grouping lineages together, applying colorings, etc. See also https://github.com/andersen-lab/Freyja/blob/main/freyja/data/plot_config.yml. None Optional, Required freyja_dashboard_task cpu Int Number of CPUs to allocate to the task 2 Optional freyja_dashboard_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.2-11_30_2024-02-00-2024-12-02 Optional freyja_dashboard_task headerColor String A hex color code to change the color of the header Optional freyja_dashboard_task memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional freyja_dashboard_task mincov Float The minimum genome coverage used as a cut-off of data to include in the dashboard. Default is set to 60 by the freyja command-line tool (not a WDL task default, per se) None Optional freyja_dashboard_task scale_by_viral_load Boolean If set to true, averages samples taken the same day while taking viral load into account FALSE Optional freyja_dashboard_task thresh Float The minimum lineage abundance cut-off value None Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/freyja/#freyja_dashboard-tasks","title":"Freyja_Dashboard Tasks","text":"freyja_dashboard_task Details This task will aggregate multiple samples together, and then create an interactive HTML visualization. Several optional inputs dictate the dashboard appearance (see each variable's description for more information).
Freyja Dashboard Technical Details
Links Task wf_freyja_dashboard.wdl Software Source Code https://github.com/andersen-lab/Freyja Software Documentation https://github.com/andersen-lab/Freyja"},{"location":"workflows/genomic_characterization/freyja/#freyja_dashboard-outputs","title":"Freyja_Dashboard Outputs","text":"Variable Type Description freyja_dashboard File The HTML file of the dashboard created freyja_dashboard_metadata File The metadata used to create the dashboard freyja_dashboard_version String The version of Freyja used freyja_dashboard_wf_analysis_date String The date of analysis freyja_dashboard_wf_version String The version of the Public Health Bioinformatics (PHB) repository used freyja_demixed_aggregate File A TSV file that summarizes thefreyja_demixed outputs for all samples"},{"location":"workflows/genomic_characterization/freyja/#running-freyja-on-other-pathogens","title":"Running Freyja on other pathogens","text":"The main requirement to run Freyja on other pathogens is the existence of a barcode file for your pathogen of interest. Currently, barcodes exist for the following organisms
Freyja barcodes for other pathogens
Data for various pathogens can be found in the following repository:\u00a0Freyja Barcodes
Folders are organized by pathogen, with each subfolder named after the date the barcode was generated, using the format YYYY-MM-DD. Barcode files are named barcode.csv, and reference genome files are named reference.fasta.
The appropriate barcode file and reference sequence need to be downloaded and uploaded to your Terra.bio workspace.
When running Freyja_FASTQ_PHB, the appropriate reference and barcodes file need to be passed as inputs. The first is a required input and will show up at the top of the workflows inputs page on Terra.bio (Figure 2).
Figure 2: Required input for Freyja_FASTQ_PHB to provide the reference genome to be used by Freyja
The barcodes file can be passed directly to Freyja by the freyja_barcodes optional input (Figure 3).
Figure 3: Optional input for Freyja_FASTQ_PHB to provide the barcodes file to be used by Freyja
"},{"location":"workflows/genomic_characterization/freyja/#figure2","title":"Figure 2","text":""},{"location":"workflows/genomic_characterization/freyja/#figure3","title":"Figure 3","text":""},{"location":"workflows/genomic_characterization/freyja/#references","title":"References","text":"If you use any of the Freyja workflows, please cite:
Karthikeyan, S., Levy, J.I., De Hoff, P.\u00a0et al.\u00a0Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission.\u00a0Nature 609, 101\u2013108 (2022). https://doi.org/10.1038/s41586-022-05049-6
Freyja source code can be found at https://github.com/andersen-lab/Freyja
Freyja barcodes (non-SARS-CoV-2): https://github.com/gp201/Freyja-barcodes
"},{"location":"workflows/genomic_characterization/pangolin_update/","title":"Pangolin_Update","text":""},{"location":"workflows/genomic_characterization/pangolin_update/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Viral, SARS-Cov-2 PHB v2.0.0 Yes Sample-level"},{"location":"workflows/genomic_characterization/pangolin_update/#pangolin_update_phb","title":"Pangolin_Update_PHB","text":"The Pangolin_Update workflow re-runs Pangolin updating prior lineage calls from one docker image to meet the lineage calls specified in an alternative docker image. The most common use case for this is updating lineage calls to be up-to-date with the latest Pangolin nomenclature by using the latest available Pangolin docker image (found\u00a0here).
"},{"location":"workflows/genomic_characterization/pangolin_update/#inputs","title":"Inputs","text":"This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status pangolin_update assembly_fasta File SARS-CoV-2 assembly file in FASTA format Required pangolin_update old_lineage String The Pangolin lineage previously assigned to the sample Required pangolin_update old_pangolin_assignment_version String Version of the Pangolin software previously used for lineage assignment. Required pangolin_update old_pangolin_docker String The Pangolin docker image previously used for lineage assignment. Required pangolin_update old_pangolin_versions String All pangolin software and database versions previously used for lineage assignment. Required pangolin_update samplename String The name of the sample being analyzed. Required pangolin_update lineage_log File TSV file detailing previous lineage assignments and software versions for this sample. Optional pangolin_update new_pangolin_docker String The Pangolin docker image used to update the Pangolin lineage assignments. Optional pangolin4 analysis_mode String Pangolin inference engine for lineage designations (usher or pangolearn) None Optional pangolin4 cpu Int Number of CPUs to allocate to the task 4 Optional pangolin4 disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pangolin4 expanded_lineage Boolean True/False that determines if a lineage should be expanded without aliases (e.g., BA.1 \u2192 B.1.1.529.1) TRUE Optional pangolin4 max_ambig Float Maximum proportion of Ns allowed for Pangolin to attempt assignment 0.5 Optional pangolin4 memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional pangolin4 min_length Int Minimum query length allowed for pangolin to attempt assignment 10000 Optional pangolin4 pangolin_arguments String Optional arguments for pangolin e.g. \"--skip-scorpio\" None Optional pangolin4 skip_designation_cache Boolean True/False that determines if the designation cache should be used FALSE Optional pangolin4 skip_scorpio Boolean True/False that determines if scorpio should be skipped. FALSE Optional pangolin_update_log cpu Int Number of CPUs to allocate to the task 4 Optional pangolin_update_log disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pangolin_update_log docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional pangolin_update_log memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional pangolin_update_log timezone String Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/pangolin_update/#outputs","title":"Outputs","text":"Variable Type Description pango_lineage String Pango lineage as determined by Pangolin pango_lineage_expanded String Pango lineage without use of aliases; e.g., BA.1 \u2192 B.1.1.529.1 pango_lineage_log File TSV file listing Pangolin lineage assignments and software versions for this sample pango_lineage_report File Full Pango lineage report generated by Pangolin pangolin_assignment_version String Version of the Pangolin software (e.g. PANGO or PUSHER) used for lineage assignment pangolin_conflict String Number of lineage conflicts as determined by Pangolin pangolin_docker String The Docker container to use for the task pangolin_notes String Lineage notes as determined by Pangolin pangolin_update_analysis_date String Date of analysis pangolin_update_version String Version of the Public Health Bioinformatics (PHB) repository used pangolin_updates String Result of Pangolin Update (lineage changed versus unchanged) with lineage assignment and date of analysis pangolin_versions String All Pangolin software and database versions"},{"location":"workflows/genomic_characterization/pangolin_update/#references","title":"References","text":"Pangolin: RRambaut A, Holmes EC, O'Toole \u00c1, Hill V, McCrone JT, Ruis C, du Plessis L, Pybus OG. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat Microbiol. 2020 Nov;5(11):1403-1407. doi: 10.1038/s41564-020-0770-5. Epub 2020 Jul 15. PMID: 32669681; PMCID: PMC7610519.
"},{"location":"workflows/genomic_characterization/theiacov/","title":"TheiaCoV Workflow Series","text":""},{"location":"workflows/genomic_characterization/theiacov/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Viral PHB v2.2.0 Yes, some optional features incompatible Sample-level"},{"location":"workflows/genomic_characterization/theiacov/#theiacov-workflows","title":"TheiaCoV Workflows","text":"The TheiaCoV workflows are for the assembly, quality assessment, and characterization of viral genomes. There are currently five TheiaCoV workflows designed to accommodate different kinds of input data:
Additionally, the TheiaCoV_FASTA_Batch workflow is available to process several hundred SARS-CoV-2 assemblies at the same time.
Key Resources
Reference Materials for SARS-CoV-2
Reference Materials for Mpox
HIV Input JSONsTheiaCoV Workflow Diagram
"},{"location":"workflows/genomic_characterization/theiacov/#supported-organisms","title":"Supported Organisms","text":"These workflows currently support the following organisms:
\"sars-cov-2\", \"SARS-CoV-2\") - default organism input\"MPXV\", \"mpox\", \"monkeypox\", \"Monkeypox virus\", \"Mpox\")\"HIV\")\"WNV\", \"wnv\", \"West Nile virus\")\"flu\", \"influenza\", \"Flu\", \"Influenza\")\"rsv_a\", \"rsv-a\", \"RSV-A\", \"RSV_A\")\"rsv_b\", \"rsv-b\", \"RSV-B\", \"RSV_B\")The compatibility of each workflow with each pathogen is shown below:
SARS-CoV-2 Mpox HIV WNV Influenza RSV-A RSV-B Illumina_PE \u2705 \u2705 \u2705 \u2705 \u2705 \u2705 \u2705 Illumina_SE \u2705 \u2705 \u274c \u2705 \u274c \u2705 \u2705 ClearLabs \u2705 \u274c \u274c \u274c \u274c \u274c \u274c ONT \u2705 \u2705 \u2705 \u274c \u2705 \u2705 \u2705 FASTA \u2705 \u2705 \u274c \u2705 \u2705 \u2705 \u2705We've provided the following information to help you set up the workflow for each organism in the form of input JSONs.
"},{"location":"workflows/genomic_characterization/theiacov/#inputs","title":"Inputs","text":"All TheiaCoV Workflows (not TheiaCoV_FASTA_Batch)
TheiaCoV_Illumina_PE Input Read DataThe TheiaCoV_Illumina_PE workflow takes in Illumina paired-end read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip before Terra uploads to minimize data upload time.
By default, the workflow anticipates\u00a02 x 150bp\u00a0reads (i.e. the input reads were generated using a 300-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate shorter read data, such as the 2 x 75bp reads generated using a 150-cycle sequencing kit.
TheiaCoV_Illumina_SE takes in Illumina single-end reads. Read file names should end with .fastq or .fq, with the optional addition of .gz. Theiagen highly recommends zipping files with gzip before uploading to Terra to minimize data upload time & save on storage costs.
By default, the workflow anticipates 1 x 35 bp reads (i.e. the input reads were generated using a 70-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate longer read data.
The TheiaCoV_ONT workflow takes in base-called ONT read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip before uploading to Terra to minimize data upload time.
The ONT sequencing kit and base-calling approach can produce substantial variability in the amount and quality of read data. Genome assemblies produced by the TheiaCoV_ONT workflow must be quality assessed before reporting results.
TheiaCoV_FASTA Input Assembly DataThe TheiaCoV_FASTA workflow takes in assembly files in FASTA format.
TheiaCoV_ClearLabs Input Read DataThe TheiaCoV_ClearLabs workflow takes in read data produced by the Clear Dx platform from ClearLabs. However, many users use the TheiaCoV_FASTA workflow instead of this one due to a few known issues when generating assemblies with this pipeline that are not present when using ClearLabs-generated FASTA files.
Terra Task Name Variable Type Description Default Value Terra Status * Organism theiacov_clearlabs primer_bed File The bed file containing the primers used when sequencing was performed Required CL sars-cov-2 theiacov_clearlabs read1 File Read data produced by the Clear Dx platform from ClearLabs Required CL sars-cov-2 theiacov_fasta assembly_fasta File Input assembly FASTA file Required FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 theiacov_fasta input_assembly_method String Method used to generate the assembly file Required FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 theiacov_illumina_pe read1 File Forward Illumina read in FASTQ file format (compression optional) Required PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 theiacov_illumina_pe read2 File Reverse Illumina read in FASTQ file format (compression optional) Required PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 theiacov_illumina_se read1 File Forward Illumina read in FASTQ file format (compression optional) Required SE MPXV, WNV, sars-cov-2 theiacov_ont read1 File Demultiplexed ONT read in FASTQ file format (compression optional) Required ONT HIV, MPXV, WNV, flu, sars-cov-2 workflow name samplename String Name of the sample being analyzed Required CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name seq_method String The sequencing methodology used to generate the input read data Required FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 clean_check_reads cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 clean_check_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 clean_check_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/bactopia/gather_samples:2.0.2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 clean_check_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 consensus cpu Int Number of CPUs to allocate to the task 8 Optional CL, ONT sars-cov-2 consensus disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, ONT sars-cov-2 consensus docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/artic:1.2.4-1.12.0 Optional CL, ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 consensus medaka_model String In order to obtain the best results, the appropriate model must be set to match the sequencer's basecaller model; this string takes the format of {pore}{device}. See the list of available models in the }_{caller_versionartic_consensus documentation section. See also https://github.com/nanoporetech/medaka?tab=readme-ov-file#models. r941_min_high_g360 Optional CL, ONT sars-cov-2 consensus memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional CL, ONT sars-cov-2 consensus_qc cpu Int Number of CPUs to allocate to the task 1 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 consensus_qc disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 consensus_qc docker String The Docker container to use for the task ngolin Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 consensus_qc genome_length Int Internal component, do not modify Do not modify, Optional CL, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 consensus_qc memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 fastq_scan_clean_reads cpu Int Number of CPUs to allocate to the task 1 Optional CL sars-cov-2 fastq_scan_clean_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 fastq_scan_clean_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional CL sars-cov-2 fastq_scan_clean_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional CL sars-cov-2 fastq_scan_clean_reads read1_name Int Internal component, do not modify Do not modify, Optional CL sars-cov-2 fastq_scan_raw_reads cpu Int Number of CPUs to allocate to the task 1 Optional CL sars-cov-2 fastq_scan_raw_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 fastq_scan_raw_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional CL sars-cov-2 fastq_scan_raw_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional CL sars-cov-2 fastq_scan_raw_reads read1_name Int Internal component, do not modify Do not modify, Optional CL sars-cov-2 flu_track abricate_flu_cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE flu flu_track abricate_flu_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE flu flu_track abricate_flu_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/abricate:1.0.1-insaflu-220727 Optional FASTA, ONT, PE flu flu_track abricate_flu_memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional FASTA, ONT, PE flu flu_track abricate_flu_mincov Int Minimum DNA % coverage 60 Optional FASTA, ONT, PE flu flu_track abricate_flu_minid Int Minimum DNA % identity 70 Optional FASTA, ONT, PE flu flu_track antiviral_aa_subs String Additional list of antiviral resistance associated amino acid substitutions of interest to be searched against those called on the sample segments. They take the format of :, e.g. NA:A26V Optional ONT, PE flu flu_track assembly_metrics_cpu Int Number of CPUs to allocate to the task 2 Optional PE flu flu_track assembly_metrics_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE flu flu_track assembly_metrics_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional PE flu flu_track assembly_metrics_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE flu flu_track flu_h1_ha_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_h1n1_m2_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_h3_ha_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_h3n2_m2_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_n1_na_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_n2_na_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_pa_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_pb1_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_pb2_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_subtype String The influenza subtype being analyzed. Used for picking nextclade datasets. Options: \"Yamagata\", \"Victoria\", \"H1N1\", \"H3N2\", \"H5N1\". Only use to override the subtype call from IRMA and ABRicate. Optional CL, ONT, PE, SE flu flu_track genoflu_cpu Int Number of CPUs to allocate to the task 1 Optional FASTA, ONT, PE flu flu_track genoflu_cross_reference File An Excel file to cross-reference BLAST findings; probably useful if novel genotypes are not in the default file used by genoflu.py Optional FASTA, ONT, PE flu_track genoflu_disk_size Int Amount of storage (in GB) to allocate to the task 25 Optional FASTA, ONT, PE flu_track genoflu_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/genoflu:1.03 Optional FASTA, ONT, PE flu_track genoflu_memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional FASTA, ONT, PE flu_track irma_cpu Int Number of CPUs to allocate to the task 4 Optional ONT, PE flu flu_track irma_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE flu flu_track irma_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/cdcgov/irma:v1.1.5 Optional ONT, PE flu flu_track irma_keep_ref_deletions Boolean True/False variable that determines if sites missed during read gathering should be deleted by ambiguation. TRUE Optional ONT, PE flu flu_track irma_memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT, PE flu flu_track nextclade_cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE flu flu_track nextclade_disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ONT, PE flu flu_track nextclade_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/nextstrain/nextclade:3.3.1 Optional ONT, PE flu flu_track nextclade_memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional ONT, PE flu flu_track nextclade_output_parser_cpu Int Number of CPUs to allocate to the task 2 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 flu_track nextclade_output_parser_disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 flu_track nextclade_output_parser_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/python/python:3.8.18-slim Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 flu_track nextclade_output_parser_memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 flu_track read2 File Internal component. Do not use. Optional ONT flu gene_coverage cpu Int Number of CPUs to allocate to the task 2 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage min_depth Int The minimum depth to determine if a position was covered. 10 Optional ONT, PE, SE MPXV, sars-cov-2 gene_coverage sc2_s_gene_start Int start nucleotide position of the SARS-CoV-2 Spike gene 21563 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage sc2_s_gene_stop Int End/Last nucleotide position of the SARS-CoV-2 Spike gene 25384 Optional CL, ONT, PE, SE MPXV, sars-cov-2 ivar_consensus ivar_bwa_cpu Int Number of CPUs to allocate to the task 6 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_bwa_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_bwa_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_bwa_memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_consensus_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_consensus_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_consensus_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_consensus_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_trim_primers_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_trim_primers_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_trim_primers_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_trim_primers_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_variant_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_variant_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_variant_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_variant_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus read2 File Internal component, do not modify Do not modify, Optional SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus skip_N Boolean True/False variable that determines if regions with depth less than minimum depth should not be added to the consensus sequence FALSE Optional PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_primtrim_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_primtrim_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_primtrim_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_primtrim_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 kraken2_dehosted cpu Int Number of CPUs to allocate to the task 4 Optional CL sars-cov-2 kraken2_dehosted disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 kraken2_dehosted docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/kraken2:2.1.2-no-db Optional CL sars-cov-2 kraken2_dehosted kraken2_db File The database used to run Kraken2. Must contain viral and human sequences. \"gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz\" Optional CL sars-cov-2 kraken2_dehosted memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL sars-cov-2 kraken2_dehosted read2 File Internal component, do not modify Do not modify, Optional CL sars-cov-2 kraken2_raw cpu Int Number of CPUs to allocate to the task 4 Optional CL sars-cov-2 kraken2_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 kraken2_raw docker_image Int Docker container used in this task us-docker.pkg.dev/general-theiagen/staphb/kraken2:2.1.2-no-db Optional CL sars-cov-2 kraken2_raw kraken2_db File The database used to run Kraken2. Must contain viral and human sequences. \"gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz\" Optional CL sars-cov-2 kraken2_raw memory String Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL sars-cov-2 kraken2_raw read_processing String The tool used for trimming of primers from reads. Options are trimmomatic and fastp trimmomatic Optional HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 kraken2_raw read2 File Internal component, do not modify Do not modify, Optional CL sars-cov-2 nanoplot_clean cpu Int Number of CPUs to allocate to the task 4 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_clean disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_clean docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_clean max_length Int The maximum length of clean reads, for which reads longer than the length specified will be hidden. 100000 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_clean memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw cpu Int Number of CPUs to allocate to the task 4 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw max_length Int The maximum length of clean reads, for which reads longer than the length specified will be hidden. 100000 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 ncbi_scrub_se cpu Int Number of CPUs to allocate to the task 4 Optional CL sars-cov-2 ncbi_scrub_se disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 ncbi_scrub_se docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/ncbi/sra-human-scrubber:2.2.1 Optional CL sars-cov-2 ncbi_scrub_se memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL sars-cov-2 nextclade_output_parser cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_output_parser disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_output_parser docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/python/python:3.8.18-slim Optional ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_output_parser memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 auspice_reference_tree_json File An Auspice JSON phylogenetic reference tree which serves as a target for phylogenetic placement. Inherited from nextclade dataset Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 cpu Int Number of CPUs to allocate to the task 2 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/nextstrain/nextclade:3.3.1 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 gene_annotations_gff File A genome annotation to specify how to translate the nucleotide sequence to proteins (genome_annotation.gff3). specifying this enables codon-informed alignment and protein alignments. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/03-genome-annotation.html Inherited from nextclade dataset Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 input_ref File A nucleotide sequence which serves as a reference for the pairwise alignment of all input sequences. This is also the sequence which defines the coordinate system of the genome annotation. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/02-reference-sequence.html Inherited from nextclade dataset Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 nextclade_pathogen_json File General dataset configuration file. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/05-pathogen-config.html Inherited from nextclade dataset Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 verbosity String other options are: \"off\" , \"error\" , \"info\" , \"debug\" , and \"trace\" (highest level of verbosity) warn Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 organism_parameters auspice_config File Auspice config file used in Augur_PHB workflow.Defaults set for various organisms & Flu segments. A minimal auspice config file is set in cases where organism is not specified and user does not provide an optional input config file. Optional Augur, CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 organism_parameters flu_segment String Influenza genome segment being analyzed. Options: \"HA\" or \"NA\". Automatically determined. This input is ignored if provided for TheiaCoV_Illumina_SE and TheiaCoV_ClearLabs N/A Optional CL, ONT, PE, SE flu organism_parameters flu_subtype String The influenza subtype being analyzed. Options: \"Yamagata\", \"Victoria\", \"H1N1\", \"H3N2\", \"H5N1\". Automatically determined. This input is ignored if provided for TheiaCoV_Illumina_SE and TheiaCoV_ClearLabs N/A Optional CL, ONT, PE, SE flu organism_parameters gene_locations_bed_file File Use to provide locations of interest where average coverage will be calculated Default provided for SARS-CoV-2 (\"gs://theiagen-public-files-rp/terra/sars-cov-2-files/sc2_gene_locations.bed\") and mpox (\"gs://theiagen-public-files/terra/mpxv-files/mpox_gene_locations.bed\") Optional CL, FASTA organism_parameters genome_length_input Int Use to specify the expected genome length; provided by default for all supported organisms Default provided for SARS-CoV-2 (29903), mpox (197200), WNV (11000), flu (13000), RSV-A (16000), RSV-B (16000), HIV (primer versions 1 [9181] and 2 [9840]) Optional CL organism_parameters hiv_primer_version String The version of HIV primers used. Options are \"https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl#L156\" and \"https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl#L164\". This input is ignored if provided for TheiaCoV_Illumina_SE and TheiaCoV_ClearLabs v1 Optional CL, FASTA, ONT, PE, SE HIV organism_parameters kraken_target_organism_input String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. Default provided for mpox (Monkeypox virus), WNV (West Nile virus), and HIV (Human immunodeficiency virus 1) Optional FASTA, ONT, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 organism_parameters pangolin_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.29 Optional CL, FASTA organism_parameters primer_bed_file File The bed file containing the primers used when sequencing was performed REQUIRED FOR SARS-CoV-2, MPOX, WNV, RSV-A & RSV-B. Provided by default only for HIV primer versions 1 (\"gs://theiagen-public-files/terra/hivgc-files/HIV-1_v1.0.primer.hyphen.bed\" and 2 (\"gs://theiagen-public-files/terra/hivgc-files/HIV-1_v2.0.primer.hyphen400.1.bed\") Optional, Sometimes required CL, FASTA organism_parameters reference_gff_file File Reference GFF file for the organism being analyzed Default provided for mpox (\"gs://theiagen-public-files/terra/mpxv-files/Mpox-MT903345.1.reference.gff3\") and HIV (primer versions 1 [\"gs://theiagen-public-files/terra/hivgc-files/NC_001802.1.gff3\"] and 2 [\"gs://theiagen-public-files/terra/hivgc-files/AY228557.1.gff3\"]) Optional CL, FASTA, ONT organism_parameters vadr_max_length Int Maximum length for the fasta-trim-terminal-ambigs.pl VADR script Default provided for SARS-CoV-2 (30000), mpox (210000), WNV (11000), flu (0), RSV-A (15500) and RSV-B (15500). Optional CL organism_parameters vadr_memory Int Amount of memory/RAM (in GB) to allocate to the task 32 (RSV-A and RSV-B) and 8 (all other TheiaCoV organisms) Optional CL, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 organism_parameters vadr_options String Options for the v-annotate.pl VADR script Default provided for SARS-CoV-2 (\"--noseqnamemax --glsearch -s -r --nomisc --mkey sarscov2 --lowsim5seq 6 --lowsim3seq 6 --alt_fail lowscore,insertnn,deletinn --out_allfasta\"), mpox (\"--glsearch -s -r --nomisc --mkey mpxv --r_lowsimok --r_lowsimxd 100 --r_lowsimxl 2000 --alt_pass discontn,dupregin --out_allfasta --minimap2 --s_overhang 150\"), WNV (\"--mkey flavi --mdir /opt/vadr/vadr-models-flavi/ --nomisc --noprotid --out_allfasta\"), flu (\"\"), RSV-A (\"-r --mkey rsv --xnocomp\"), and RSV-B (\"-r --mkey rsv --xnocomp\") Optional CL organism_parameters vadr_skip_length Int Minimum assembly length (unambiguous) to run VADR 10000 Optional CL MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 pangolin4 analysis_mode String Pangolin inference engine for lineage designations (usher or pangolearn). Default is Usher. Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 cpu Int Number of CPUs to allocate to the task 4 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 expanded_lineage Boolean True/False that determines if a lineage should be expanded without aliases (e.g., BA.1 \u2192 B.1.1.529.1) TRUE Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 max_ambig Float The maximum proportion of Ns allowed for pangolin to attempt an assignment 0.5 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 min_length Int Minimum query length allowed for pangolin to attempt an assignment 10000 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 pangolin_arguments String Optional arguments for pangolin e.g. ''--skip-scorpio'' Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 skip_designation_cache Boolean A True/False option that determines if the designation cache should be used FALSE Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 skip_scorpio Boolean A True/False option that determines if scorpio should be skipped. FALSE Optional CL, FASTA, ONT, PE, SE sars-cov-2 qc_check_task ani_highest_percent Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task ani_highest_percent_bases_aligned Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task assembly_length Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task assembly_mean_coverage Int Internal component, do not modify Do not modify, Optional FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task busco_results String Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task combined_mean_q_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task combined_mean_q_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task combined_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task combined_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task cpu Int Number of CPUs to allocate to the task 4 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task est_coverage_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task est_coverage_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task gambit_predicted_taxon String Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task kraken_human String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task kraken_human_dehosted String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task kraken_sc2 String Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task kraken_sc2_dehosted String Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task kraken_target_organism Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task kraken_target_organism_dehosted Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task midas_secondary_genus_abundance Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task midas_secondary_genus_coverage Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task minbaseq_trim Int Internal component, do not modify Do not modify, Optional FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task n50_value Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task num_reads_clean2 Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, SE qc_check_task num_reads_raw2 Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, SE qc_check_task number_contigs Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task quast_gc_percent Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r1_mean_q_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r1_mean_q_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r1_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r1_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r2_mean_q_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r2_mean_q_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r2_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r2_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task sc2_s_gene_mean_coverage Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task sc2_s_gene_percent_coverage Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 quasitools_illumina_pe cpu Int Number of CPUs to allocate to the task 2 Optional PE HIV quasitools_illumina_pe disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional PE HIV quasitools_illumina_pe docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/quasitools:0.7.0--pyh864c0ab_1 Optional PE HIV quasitools_illumina_pe memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional PE HIV quasitools_ont cpu Int Number of CPUs to allocate to the task 2 Optional ONT HIV quasitools_ont disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ONT HIV quasitools_ont docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/quasitools:0.7.0--pyh864c0ab_1 Optional ONT HIV quasitools_ont memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional ONT HIV quasitools_ont read2 File Internal component. Do not use. Do not modify, Optional ONT HIV raw_check_reads cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 raw_check_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 raw_check_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/bactopia/gather_samples:2.0.2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 raw_check_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim bbduk_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim call_kraken Boolean True/False variable that determines if the Kraken2 task should be called. FALSE Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim call_midas Boolean True/False variable that determines if the MIDAS task should be called. TRUE Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim downsampling_coverage Float The desired coverage to sub-sample the reads to with RASUSA 150 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim fastp_args String Additional fastp task arguments --detect_adapter_for_pe -g -5 20 -3 20 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim kraken_db File The database used to run Kraken2. Must contain viral and human sequences. \"gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz\" Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim kraken_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim midas_db File The database used by the MIDAS task gs://theiagen-public-files-rp/terra/theiaprok-files/midas/midas_db_v1.2.tar.gz Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim read_processing String The name of the tool to perform basic read processing; options: \"trimmomatic\" or \"fastp\" trimmomatic Optional PE, SE read_QC_trim read_qc String The tool used for quality control (QC) of reads. Options are fastq_scan and fastqc fastq_scan Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim target_organism String Organism to search for in Kraken Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim trimmomatic_args String Additional arguments to pass to trimmomatic -phred33 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 set_flu_ha_nextclade_values reference_gff_file File Reference GFF file for flu HA Do not modify, Optional ONT flu set_flu_na_nextclade_values reference_gff_file Int Reference GFF file for flu NA Do not modify, Optional ONT flu set_flu_na_nextclade_values vadr_mem Int Memory, in GB, allocated to this task 8 Do not modify, Optional ONT flu stats_n_coverage cpu Int Number of CPUs to allocate to the task 2 Optional CL, ONT stats_n_coverage disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, ONT stats_n_coverage docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional CL, ONT stats_n_coverage memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, ONT stats_n_coverage_primtrim cpu Int Number of CPUs to allocate to the task 2 Optional CL, ONT stats_n_coverage_primtrim disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, ONT stats_n_coverage_primtrim docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional CL, ONT stats_n_coverage_primtrim memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, ONT vadr cpu Int Number of CPUs to allocate to the task 2 Optional CL, FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/vadr:1.5.1 Optional CL, FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr max_length Int Maximum length of contig allowed to run VADR Optional CL HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr memory Int Amount of memory/RAM (in GB) to allocate to the task 32 (RSV-A and RSV-B) and 8 (all other TheiaCoV organisms) Optional CL MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr min_length Int Minimum length subsequence to possibly replace Ns for the http://fasta-trim-terminal-ambigs.pl/ VADR script 50 Optional CL, FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr skip_length Int Minimum assembly length (unambiguous) to run VADR 10000 Optional CL MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr vadr_opts String Additional options to provide to VADR Optional CL HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional ONT, PE, SE, FASTA, CL HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional ONT, PE, SE, FASTA, CL HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name adapters File File that contains the adapters used /bbmap/resources/adapters.fa Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name consensus_min_freq Float The minimum frequency for a variant to be called a SNP in consensus genome 0.6 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name flu_segment String Influenza genome segment being analyzed. Options: \"HA\" or \"NA\". HA Optional, Required FASTA workflow name flu_subtype String The influenza subtype being analyzed. Options: \"Yamagata\", \"Victoria\", \"H1N1\", \"H3N2\", \"H5N1\". Automatically determined. Optional FASTA workflow name genome_length Int Use to specify the expected genome length Optional FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name max_genome_length Int Maximum genome length able to pass read screening 2673870 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name max_length Int Maximum length for a read based on the SARS-CoV-2 primer scheme 700 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name medaka_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/artic-ncov2019:1.3.0-medaka-1.4.3 Optional CL workflow name min_basepairs Int Minimum base pairs to pass read screening 34000 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_coverage Int Minimum coverage to pass read screening 10 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_depth Int Minimum depth of reads required to call variants and generate a consensus genome. This value is passed to the iVar software. 100 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_genome_length Int Minimum genome length to pass read screening 1700 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_length Int Minimum length of a read based on the SARS-CoV-2 primer scheme 400 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_proportion Int Minimum read proportion to pass read screening 40 Optional PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_reads Int Minimum reads to pass read screening 113 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name nextclade_dataset_name String Nextclade organism dataset names. However, if organism input is set correctly, this input will be automatically assigned the corresponding dataset name. See organism defaults for more information Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name nextclade_dataset_tag String Nextclade dataset tag. Used for pulling up-to-date reference genomes and associated information specific to nextclade datasets (QC thresholds, organism-specific information like SARS-CoV-2 clade & lineage information, etc.) that is required for running the Nextclade tool. Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name normalise Int Used to normalize the amount of reads to the indicated level before variant calling 20000 for CL, 200 for ONT Optional CL, ONT workflow name organism String The organism that is being analyzed. Options: \"sars-cov-2\", \"MPXV\", \"WNV\", \"HIV\", \"flu\", \"rsv_a\", \"rsv_b\". However, \"flu\" is not available for TheiaCoV_Illumina_SE sars-cov-2 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name pangolin_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.29 Do not modify, Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name phix File File that contains the phix used /bbmap/resources/phix174_ill.ref.fa.gz Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name primer_bed File The bed file containing the primers used when sequencing was performed Optional ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 workflow name qc_check_table File A TSV file with optional user input QC values to be compared against the default workflow value Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name reference_gene_locations_bed File Use to provide locations of interest where average coverage will be calculated Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name reference_genome File An optional reference genome used for consensus assembly and QC Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name reference_gff File The general feature format (gff) of the reference genome. Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name seq_method String The sequencing methodology used to generate the input read data ILLUMINA Optional CL, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name skip_mash Boolean A True/False option that determines if mash should be skipped in the screen task. FALSE Optional ONT, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 workflow name skip_screen Boolean A True/False option that determines if the screen task should be skipped. FALSE Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name target_organism String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. Optional CL, ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name trim_min_length Int The minimum length of each read after trimming 75 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name trim_primers Boolean A True/False option that determines if primers should be trimmed. TRUE Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name trim_quality_min_score Int The minimum quality score to keep during trimming 30 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name trim_window_size Int Specifies window size for trimming (the number of bases to average the quality across) 4 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_max_length Int Maximum length of contig allowed to run VADR Optional FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_memory Int Amount of memory/RAM (in GB) to allocate to the task 32 (RSV-A and RSV-B) and 8 (all other TheiaCoV organisms) Optional FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_options String Additional options to provide to VADR Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_opts String Additional options to provide to VADR Optional FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_skip_length Int Minimum assembly length (unambiguous) to run VADR 10000 Optional FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name variant_min_freq Float Minimum frequency for a variant to be reported in ivar outputs 0.6 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 TheiaCoV_FASTA_Batch_PHB Inputs"},{"location":"workflows/genomic_characterization/theiacov/#theiacov-fasta-batch-inputs","title":"TheiaCoV_FASTA_Batch Inputs","text":"Input Data The TheiaCoV_FASTA_Batch workflow takes in a set of assembly files in FASTA format.
Terra Task Name Variable Type Description Default Value Terra Status theiacov_fasta_batch assembly_fastas Array[File] Genome assembly files in fasta format. Example: this.sars-cov-2-samples.assembly_fasta Required theiacov_fasta_batch bucket_name String The GCP bucket for the workspace where the TheiaCoV_FASTA_Batch output files are saved. We recommend using a unique GSURI for the bucket associated with your Terra workspace. The root GSURI is accessible in the Dashboard page of your workspace in the \"Cloud Information\" section.Do not include the prefix gs:// in the stringExample: \"\"fc-c526190d-4332-409b-8086-be7e1af9a0b6/theiacov_fasta_batch-2024-04-15-seq-run-1/ Required theiacov_fasta_batch project_name String The name of the Terra project where the data can be found. Example: \"my-terra-project\" Required theiacov_fasta_batch samplenames Array[String] The names of the samples to be analyzed. Example: this.sars-cov-2-samples.sars-cov-2-sample_id Required theiacov_fasta_batch table_name String The name of the Terra table where the data can be found. Example: \"sars-cov-2-sample\" Required theiacov_fasta_batch workspace_name String The name of the Terra workspace where the data can be found. Example \"my-terra-workspace\" Required cat_files_fasta cpu Int Number of CPUs to allocate to the task 2 Optional cat_files_fasta disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional cat_files_fasta docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional cat_files_fasta memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional nextclade_v3 auspice_reference_tree_json File The phylogenetic reference tree which serves as a target for phylogenetic placement default is inherited from NextClade dataset Optional nextclade_v3 cpu Int Number of CPUs to allocate to the task 2 Optional nextclade_v3 disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional nextclade_v3 docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/nextstrain/nextclade:3.3.1 Optional nextclade_v3 gene_annotations_gff File A genome annotation to specify how to translate the nucleotide sequence to proteins (genome_annotation.gff3). specifying this enables codon-informed alignment and protein alignments. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/03-genome-annotation.html None Optional nextclade_v3 input_ref File A nucleotide sequence which serves as a reference for the pairwise alignment of all input sequences. This is also the sequence which defines the coordinate system of the genome annotation. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/02-reference-sequence.html None Optional nextclade_v3 memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional nextclade_v3 nextclade_pathogen_json File General dataset configuration file. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/05-pathogen-config.html None Optional nextclade_v3 verbosity String other options are: \"off\" , \"error\" , \"info\" , \"debug\" , and \"trace\" (highest level of verbosity) warn Optional organism_parameters flu_segment String Optional organism_parameters flu_subtype String Optional organism_parameters gene_locations_bed_file File Optional organism_parameters genome_length_input Int Optional organism_parameters hiv_primer_version String Optional organism_parameters kraken_target_organism_input String Optional organism_parameters primer_bed_file File Optional organism_parameters reference_genome File Optional organism_parameters reference_gff_file File Optional organism_parameters vadr_max_length Int Optional organism_parameters vadr_mem Int Optional organism_parameters vadr_options String Optional pangolin4 analysis_mode String Used to switch between usher and pangolearn analysis modes. Only use usher because pangolearn is no longer supported as of Pangolin v4.3 and higher versions. None Optional pangolin4 cpu Int Number of CPUs to allocate to the task 4 Optional pangolin4 disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pangolin4 expanded_lineage Boolean True/False that determines if a lineage should be expanded without aliases (e.g., BA.1 \u2192 B.1.1.529.1) TRUE Optional pangolin4 max_ambig Float The maximum proportion of Ns allowed for pangolin to attempt an assignment 0.5 Optional pangolin4 memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional pangolin4 skip_designation_cache Boolean True/False that determines if the designation cache should be used FALSE Optional pangolin4 skip_scorpio Boolean True/False that determines if scorpio should be skipped. FALSE Optional sm_theiacov_fasta_wrangling cpu Int Number of CPUs to allocate to the task 8 Optional sm_theiacov_fasta_wrangling disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional sm_theiacov_fasta_wrangling docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-08-28-v4 Optional sm_theiacov_fasta_wrangling memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional theiacov_fasta_batch nextclade_dataset_name String Nextclade organism dataset name. Options: \"nextstrain/sars-cov-2/wuhan-hu-1/orfs\" However, if organism input is set correctly, this input will be automatically assigned the corresponding dataset name. sars-cov-2 Optional theiacov_fasta_batch nextclade_dataset_tag String Nextclade dataset tag. Used for pulling up-to-date reference genomes and associated information specific to nextclade datasets (QC thresholds, organism-specific information like SARS-CoV-2 clade & lineage information, etc.) that is required for running the Nextclade tool. 2024-06-13--23-42-47Z Optional theiacov_fasta_batch organism String The organism that is being analyzed. Options: \"sars-cov-2\" sars-cov-2 Optional theiacov_fasta_batch pangolin_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.27 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/theiacov/#org-specific","title":"Organism-specific parameters and logic","text":"The organism_parameters sub-workflow is the first step in all TheiaCoV workflows. This step automatically sets the different parameters needed for each downstream tool to the appropriate value for the user-designated organism (by default, \"sars-cov-2\" is the default organism).
The following tables include the relevant organism-specific parameters; all of these default values can be overwritten by providing a value for the \"Overwrite Variable Name\" field.
SARS-CoV-2 Defaults Overwrite Variable Name Organism Default Value gene_locations_bed_file sars-cov-2\"gs://theiagen-public-files-rp/terra/sars-cov-2-files/sc2_gene_locations.bed\" genome_length_input sars-cov-2 29903 kraken_target_organism_input sars-cov-2 \"Severe acute respiratory syndrome coronavirus 2\" nextclade_dataset_name_input sars-cov-2 \"nextstrain/sars-cov-2/wuhan-hu-1/orfs\" nextclade_dataset_tag_input sars-cov-2 \"2024-11-19--14-18-53Z\" pangolin_docker_image sars-cov-2 \"us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.31 \" reference_genome sars-cov-2 \"gs://theiagen-public-files-rp/terra/augur-sars-cov-2-references/MN908947.fasta\" vadr_max_length sars-cov-2 30000 vadr_mem sars-cov-2 8 vadr_options sars-cov-2 \"--noseqnamemax --glsearch -s -r --nomisc --mkey sarscov2 --lowsim5seq 6 --lowsim3seq 6 --alt_fail lowscore,insertnn,deletinn --out_allfasta\" Mpox Defaults Overwrite Variable Name Organism Default Value gene_locations_bed_file MPXV \"gs://theiagen-public-files/terra/mpxv-files/mpox_gene_locations.bed\" genome_length_input MPXV 197200 kraken_target_organism_input MPXV \"Monkeypox virus\" nextclade_dataset_name_input MPXV \"nextstrain/mpox/lineage-b.1\" nextclade_dataset_tag_input MPXV \"2024-11-19--14-18-53Z\" primer_bed_file MPXV \"gs://theiagen-public-files/terra/mpxv-files/MPXV.primer.bed\" reference_genome MPXV \"gs://theiagen-public-files/terra/mpxv-files/MPXV.MT903345.reference.fasta\" reference_gff_file MPXV \"gs://theiagen-public-files/terra/mpxv-files/Mpox-MT903345.1.reference.gff3\" vadr_max_length MPXV 210000 vadr_mem MPXV 8 vadr_options MPXV \"--glsearch -s -r --nomisc --mkey mpxv --r_lowsimok --r_lowsimxd 100 --r_lowsimxl 2000 --alt_pass discontn,dupregin --out_allfasta --minimap2 --s_overhang 150\" WNV Defaults Overwrite Variable Name Organism Default Value Notes genome_length_input WNV 11000 kraken_target_organism_input WNV \"West Nile virus\" nextclade_dataset_name_input WNV \"NA\" TheiaCoV's Nextclade currently does not support WNV nextclade_dataset_tag_input WNV \"NA\" TheiaCoV's Nextclade currently does not support WNV primer_bed_file WNV \"gs://theiagen-public-files/terra/theiacov-files/WNV/WNV-L1_primer.bed\" reference_genome WNV \"gs://theiagen-public-files/terra/theiacov-files/WNV/NC_009942.1_wnv_L1.fasta\" vadr_max_length WNV 11000 vadr_mem WNV 8 vadr_options WNV \"--mkey flavi --mdir /opt/vadr/vadr-models-flavi/ --nomisc --noprotid --out_allfasta\" Flu Defaults Overwrite Variable Name Organism Flu Segment Flu Subtype Default Value Notes flu_segment flu all all N/A TheiaCoV will attempt to automatically assign a flu segment flu_subtype flu all all N/A TheiaCoV will attempt to automatically assign a flu subtype genome_length_input flu all all 13500 vadr_max_length flu all all 13500 vadr_mem flu all all 8 vadr_options flu all all \"--atgonly --xnocomp --nomisc --alt_fail extrant5,extrant3 --mkey flu\" nextclade_dataset_name_input flu ha h1n1 \"nextstrain/flu/h1n1pdm/ha/MW626062\" nextclade_dataset_tag_input flu ha h1n1 \"2024-11-27--02-51-00Z\" reference_genome flu ha h1n1 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h1n1pdm_ha.fasta\" nextclade_dataset_name_input flu ha h3n2 \"nextstrain/flu/h3n2/ha/EPI1857216\" nextclade_dataset_tag_input flu ha h3n2 \"2024-11-27--02-51-00Z\" reference_genome flu ha h3n2 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h3n2_ha.fasta\" nextclade_dataset_name_input flu ha victoria \"nextstrain/flu/vic/ha/KX058884\" nextclade_dataset_tag_input flu ha victoria \"2024-11-05--09-19-52Z\" reference_genome flu ha victoria \"gs://theiagen-public-files-rp/terra/flu-references/reference_vic_ha.fasta\" nextclade_dataset_name_input flu ha yamagata \"nextstrain/flu/yam/ha/JN993010\" nextclade_dataset_tag_input flu ha yamagata \"2024-01-30--16-34-55Z\" reference_genome flu ha yamagata \"gs://theiagen-public-files-rp/terra/flu-references/reference_yam_ha.fasta\" nextclade_dataset_name_input flu ha h5n1 \"community/moncla-lab/iav-h5/ha/all-clades\" nextclade_dataset_tag_input flu ha h5n1 \"2024-12-04--17-05-31Z\" reference_genome flu ha h5n1 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h5n1_ha.fasta\" nextclade_dataset_name_input flu na h1n1 \"nextstrain/flu/h1n1pdm/na/MW626056\" nextclade_dataset_tag_input flu na h1n1 \"2024-11-05--09-19-52Z\" reference_genome flu na h1n1 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h1n1pdm_na.fasta\" nextclade_dataset_name_input flu na h3n2 \"nextstrain/flu/h3n2/na/EPI1857215\" nextclade_dataset_tag_input flu na h3n2 \"2024-11-05--09-19-52Z\" reference_genome flu na h3n2 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h3n2_na.fasta\" nextclade_dataset_name_input flu na victoria \"nextstrain/flu/vic/na/CY073894\" nextclade_dataset_tag_input flu na victoria \"2024-11-05--09-19-52Z\" reference_genome flu na victoria \"gs://theiagen-public-files-rp/terra/flu-references/reference_vic_na.fasta\" nextclade_dataset_name_input flu na yamagata \"NA\" nextclade_dataset_tag_input flu na yamagata \"NA\" reference_genome flu na yamagata \"gs://theiagen-public-files-rp/terra/flu-references/reference_yam_na.fasta\" RSV-A Defaults Overwrite Variable Name Organism Default Value genome_length_input rsv_a 16000 kraken_target_organism rsv_a \"Human respiratory syncytial virus A\" nextclade_dataset_name_input rsv_a nextstrain/rsv/a/EPI_ISL_412866 nextclade_dataset_tag_input rsv_a \"2024-11-27--02-51-00Z\" reference_genome rsv_a gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_a.fasta vadr_max_length rsv_a 15500 vadr_mem rsv_a 32 vadr_options rsv_a -r --mkey rsv --xnocomp RSV-B Defaults Overwrite Variable Name Organism Default Value genome_length_input rsv_b 16000 kraken_target_organism rsv_b \"human respiratory syncytial virus\" nextclade_dataset_name_input rsv_b nextstrain/rsv/b/EPI_ISL_1653999 nextclade_dataset_tag_input rsv_b \"2024-11-27--02-51-00Z\" reference_genome rsv_b gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_b.fasta vadr_max_length rsv_b 15500 vadr_mem rsv_b 32 vadr_options rsv_b -r --mkey rsv --xnocomp HIV Defaults Overwrite Variable Name Organism Default Value Notes kraken_target_organism_input HIV Human immunodeficiency virus 1 genome_length_input HIV-v1 9181 This version of HIV originates from Oregon primer_bed_file HIV-v1 gs://theiagen-public-files/terra/hivgc-files/HIV-1_v1.0.primer.hyphen.bed This version of HIV originates from Oregon reference_genome HIV-v1 gs://theiagen-public-files/terra/hivgc-files/NC_001802.1.fasta This version of HIV originates from Oregon reference_gff_file HIV-v1 gs://theiagen-public-files/terra/hivgc-files/NC_001802.1.gff3 This version of HIV originates from Oregon genome_length_input HIV-v2 9840 This version of HIV originates from Southern Africa primer_bed_file HIV-v2 gs://theiagen-public-files/terra/hivgc-files/HIV-1_v2.0.primer.hyphen400.1.bed This version of HIV originates from Southern Africa reference_genome HIV-v2 gs://theiagen-public-files/terra/hivgc-files/AY228557.1.headerchanged.fasta This version of HIV originates from Southern Africa reference_gff_file HIV-v2 gs://theiagen-public-files/terra/hivgc-files/AY228557.1.gff3 This version of HIV originates from Southern Africa"},{"location":"workflows/genomic_characterization/theiacov/#workflow-tasks","title":"Workflow Tasks","text":"All input reads are processed through \"core tasks\" in the TheiaCoV Illumina, ONT, and ClearLabs workflows. These undertake read trimming and assembly appropriate to the input data type. TheiaCoV workflows subsequently launch default genome characterization modules for quality assessment, and additional taxa-specific characterization steps. When setting up the workflow, users may choose to use \"optional tasks\" as additions or alternatives to tasks run in the workflow by default.
"},{"location":"workflows/genomic_characterization/theiacov/#core-tasks","title":"Core tasks","text":"These tasks are performed regardless of organism, and perform read trimming and various quality control steps.
versioning: Version capture for TheiaCoV The versioning task captures the workflow version from the GitHub (code repository) version.
Version Capture Technical details
Links Task task_versioning.wdlscreen: Total Raw Read Quantification and Genome Size Estimation The screen task ensures the quantity of sequence data is sufficient to undertake genomic analysis. It uses fastq-scan and bash commands for quantification of reads and base pairs, and mash sketching to estimate the genome size and its coverage. At each step, the results are assessed relative to pass/fail criteria and thresholds that may be defined by optional user inputs. Samples that do not meet these criteria will not be processed further by the workflow:
min_reads.min_proportion basepairs are in either the reads1 or read2 files.min_basepairs basepairsmin_genome_size or bigger than max_genome_size.min_coverage.Read screening is undertaken on both the raw and cleaned reads. The task may be skipped by setting the skip_screen variable to true.
Default values vary between the PE and SE workflow. The rationale for these default values can be found below.
Variable Rationaleskip_screen Set to true to skip the read screen from running min_reads Minimum number of base pairs for 10x coverage of the Hepatitis delta (of the Deltavirus genus) virus divided by 300 (longest Illumina read length) min_basepairs Greater than 10x coverage of the Hepatitis delta (of the Deltavirus genus) virus min_genome_size Based on the Hepatitis delta (of the Deltavirus genus) genome- the smallest viral genome as of 2024-04-11 (1,700 bp) max_genome_size Based on the Pandoravirus salinus genome, the biggest viral genome, (2,673,870 bp) with 2 Mbp added min_coverage A bare-minimum coverage for genome characterization. Higher coverage would be required for high-quality phylogenetics. min_proportion Greater than 50% reads are in the read1 file; others are in the read2 file Screen Technical Details
There is a single WDL task for read screening. The screen task is run twice, once for raw reads and once for clean reads.
read_QC_trim_pe and read_QC_trim_se: Read Quality Trimming, Host and Adapter Removal, Quantification, and Identification for Illumina workflows read_QC_trim is a sub-workflow within TheiaCoV that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below. The differences between TheiaCoV PE and SE in the read_QC_trim sub-workflow lie in the default parameters, the use of two or one input read file(s), and the different output files.
All reads of human origin are removed, including their mates, by using NCBI's human read removal tool (HRRT).
HRRT is based on the SRA Taxonomy Analysis Tool and employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records with any k-mers found in non-Eukaryota RefSeq records subtracted from the database.
NCBI-Scrub Technical Details
Links Task task_ncbi_scrub.wdl Software Source Code NCBI Scrub on GitHub Software Documentation https://github.com/ncbi/sra-human-scrubber/blob/master/README.md Read quality trimmingEither trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.
If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:
Parameter Explanation -g enables polyG tail trimming -5 20 enables read end-trimming -3 20 enables read end-trimming --detect_adapter_for_pe enables adapter-trimming only for paired-end reads Adapter removalThe BBDuk task removes adapters from sequence reads. To do this:
Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.
Read QuantificationThere are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. In TheiaProk_Illumina_PE, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.
Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
Kraken2 is run on the set of raw reads, provided as input, as well as the set of clean reads that are resulted from the read_QC_trim workflow
Database-dependent
TheiaCoV automatically uses a viral-specific Kraken2 database. This database was generated in-house from RefSeq's viral sequence collection and human genome GRCh38. It's available at gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz
Kraken2 Technical Details
Links Task task_kraken2.wdl Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/wiki Original Publication(s) Improved metagenomic analysis with Kraken 2read_QC_trim Technical Details
Links Sub-workflow wf_read_QC_trim_pe.wdlwf_read_QC_trim_se.wdl Tasks task_fastp.wdltask_trimmomatic.wdltask_bbduk.wdltask_fastq_scan.wdltask_midas.wdltask_kraken2.wdl Software Source Code fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2 Software Documentation fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2 Original Publication(s) Trimmomatic: a flexible trimmer for Illumina sequence datafastp: an ultra-fast all-in-one FASTQ preprocessorAn integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeographyImproved metagenomic analysis with Kraken 2read_QC_trim_ONT: Read Quality Trimming, Host Removal, and Identification for ONT data read_QC_trim is a sub-workflow within TheiaCoV that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below.
All reads of human origin are removed, including their mates, by using NCBI's human read removal tool (HRRT).
HRRT is based on the SRA Taxonomy Analysis Tool and employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records with any k-mers found in non-Eukaryota RefSeq records subtracted from the database.
NCBI-Scrub Technical Details
Links Task task_ncbi_scrub.wdl Software Source Code NCBI Scrub on GitHub Software Documentation https://github.com/ncbi/sra-human-scrubber/blob/master/README.md Read quality filteringRead filtering is performed using artic guppyplex which performs a quality check by filtering the reads by length to remove chimeric reads.
Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
Kraken2 is run on the set of raw reads, provided as input, as well as the set of clean reads that are resulted from the read_QC_trim workflow
Database-dependent
TheiaCoV automatically uses a viral-specific Kraken2 database. This database was generated in-house from RefSeq's viral sequence collection and human genome GRCh38. It's available at gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz
Kraken2 Technical Details
Links Task task_kraken2.wdl Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/wiki Original Publication(s) Improved metagenomic analysis with Kraken 2read_QC_trim Technical Details
Each TheiaCoV workflow calls a sub-workflow listed below, which then calls the individual tasks:
Workflow TheiaCoV_ONT Sub-workflow wf_read_QC_trim_ont.wdl Tasks task_ncbi_scrub.wdl (SE subtask)task_artic_guppyplex.wdltask_kraken2.wdl Software Source Code NCBI Scrub on GitHubArtic on GitHubKraken2 on GitHub Software Documentation NCBI ScrubArtic pipelineKraken2 Original Publication(s) STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissionsImproved metagenomic analysis with Kraken 2"},{"location":"workflows/genomic_characterization/theiacov/#assembly-tasks","title":"Assembly tasks","text":"Either one of these tasks is run depending on the organism and workflow type.
ivar_consensus: Alignment, Consensus, Variant Detection, and Assembly Statistics for non-flu organisms in Illumina workflows ivar_consensus is a sub-workflow within TheiaCoV that performs reference-based consensus assembly using the iVar tool by Nathan Grubaugh from the Andersen lab.
The following steps are performed as part of this sub-workflow:
trim_primers is set to true, primers will be removed using ivar trim.ivar consensus command is run to generate a consensus assembly.iVar Consensus Technical Details
Workflow TheiaCoV_Illumina_PE & TheiaCoV_Illumina_SE Sub-workflow wf_ivar_consensus.wdl Tasks task_bwa.wdltask_ivar_primer_trim.wdltask_assembly_metrics.wdltask_ivar_variant_call.wdltask_ivar_consensus.wdl Software Source Code BWA on GitHub, iVar on GitHub Software Documentation BWA on SourceForge, iVar on GitHub Original Publication(s) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEMAn amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVarartic_consensus: Alignment, Primer Trimming, Variant Detection, and Consensus for non-flu organisms in ONT & ClearLabs workflows Briefly, input reads are aligned to the appropriate reference with\u00a0minimap2\u00a0to generate a Binary Alignment Mapping (BAM) file. Primer sequences are then removed from the BAM file and a consensus assembly file is generated using the\u00a0Artic minion Medaka argument.
Read-trimming is performed on raw read data generated on the ClearLabs instrument and thus not a required step in the TheiaCoV_ClearLabs workflow.
Availablemedaka models The medaka models available in the default docker container are as follows:
r103_fast_g507, r103_fast_snp_g507, r103_fast_variant_g507, r103_hac_g507,\nr103_hac_snp_g507, r103_hac_variant_g507, r103_min_high_g345, r103_min_high_g360,\nr103_prom_high_g360, r103_prom_snp_g3210, r103_prom_variant_g3210, r103_sup_g507,\nr103_sup_snp_g507, r103_sup_variant_g507, r1041_e82_260bps_fast_g632,\nr1041_e82_260bps_fast_variant_g632, r1041_e82_260bps_hac_g632,\nr1041_e82_260bps_hac_v4.0.0, r1041_e82_260bps_hac_v4.1.0,\nr1041_e82_260bps_hac_variant_g632, r1041_e82_260bps_hac_variant_v4.1.0,\nr1041_e82_260bps_joint_apk_ulk_v5.0.0, r1041_e82_260bps_sup_g632,\nr1041_e82_260bps_sup_v4.0.0, r1041_e82_260bps_sup_v4.1.0,\nr1041_e82_260bps_sup_variant_g632, r1041_e82_260bps_sup_variant_v4.1.0,\nr1041_e82_400bps_fast_g615, r1041_e82_400bps_fast_g632,\nr1041_e82_400bps_fast_variant_g615, r1041_e82_400bps_fast_variant_g632,\nr1041_e82_400bps_hac_g615, r1041_e82_400bps_hac_g632, r1041_e82_400bps_hac_v4.0.0,\nr1041_e82_400bps_hac_v4.1.0, r1041_e82_400bps_hac_v4.2.0, r1041_e82_400bps_hac_v4.3.0,\nr1041_e82_400bps_hac_v5.0.0, r1041_e82_400bps_hac_variant_g615,\nr1041_e82_400bps_hac_variant_g632, r1041_e82_400bps_hac_variant_v4.1.0,\nr1041_e82_400bps_hac_variant_v4.2.0, r1041_e82_400bps_hac_variant_v4.3.0,\nr1041_e82_400bps_hac_variant_v5.0.0, r1041_e82_400bps_sup_g615,\nr1041_e82_400bps_sup_v4.0.0, r1041_e82_400bps_sup_v4.1.0, r1041_e82_400bps_sup_v4.2.0,\nr1041_e82_400bps_sup_v4.3.0, r1041_e82_400bps_sup_v5.0.0,\nr1041_e82_400bps_sup_variant_g615, r1041_e82_400bps_sup_variant_v4.1.0,\nr1041_e82_400bps_sup_variant_v4.2.0, r1041_e82_400bps_sup_variant_v4.3.0,\nr1041_e82_400bps_sup_variant_v5.0.0, r104_e81_fast_g5015, r104_e81_fast_variant_g5015,\nr104_e81_hac_g5015, r104_e81_hac_variant_g5015, r104_e81_sup_g5015, r104_e81_sup_g610,\nr104_e81_sup_variant_g610, r10_min_high_g303, r10_min_high_g340, r941_e81_fast_g514,\nr941_e81_fast_variant_g514, r941_e81_hac_g514, r941_e81_hac_variant_g514,\nr941_e81_sup_g514, r941_e81_sup_variant_g514, r941_min_fast_g303, r941_min_fast_g507,\nr941_min_fast_snp_g507, r941_min_fast_variant_g507, r941_min_hac_g507,\nr941_min_hac_snp_g507, r941_min_hac_variant_g507, r941_min_high_g303, r941_min_high_g330,\nr941_min_high_g340_rle, r941_min_high_g344, r941_min_high_g351, r941_min_high_g360,\nr941_min_sup_g507, r941_min_sup_snp_g507, r941_min_sup_variant_g507, r941_prom_fast_g303,\nr941_prom_fast_g507, r941_prom_fast_snp_g507, r941_prom_fast_variant_g507,\nr941_prom_hac_g507, r941_prom_hac_snp_g507, r941_prom_hac_variant_g507,\nr941_prom_high_g303, r941_prom_high_g330, r941_prom_high_g344, r941_prom_high_g360,\nr941_prom_high_g4011, r941_prom_snp_g303, r941_prom_snp_g322, r941_prom_snp_g360,\nr941_prom_sup_g507, r941_prom_sup_snp_g507, r941_prom_sup_variant_g507,\nr941_prom_variant_g303, r941_prom_variant_g322, r941_prom_variant_g360,\nr941_sup_plant_g610, r941_sup_plant_variant_g610\nGeneral statistics about the assembly are generated with the consensus_qc task (task_assembly_metrics.wdl).
Artic Consensus Technical Details
Links Task task_artic_consensus.wdl Software Source Code Artic on GitHub Software Documentation Artic pipelineirma: Assembly and Characterization for flu in TheiaCoV_Illumina_PE & TheiaCoV_ONT Cleaned reads are assembled using irma which does not use a reference due to the rapid evolution and high variability of influenza. Assemblies produced by irma will be orderd from largest to smallest assembled flu segment. irma also performs typing and subtyping as part of the assembly process.
General statistics about the assembly are generated with the consensus_qc task (task_assembly_metrics.wdl).
IRMA Technical Details
Links Task task_irma.wdl Software Documentation IRMA website Original Publication(s) Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler"},{"location":"workflows/genomic_characterization/theiacov/#org-specific-tasks","title":"Organism-specific characterization tasks","text":"The following tasks only run for the appropriate organism designation. The following table illustrates which characterization tools are run for the indicated organism.
SARS-CoV-2 MPXV HIV WNV Influenza RSV-A RSV-B Pangolin \u2705 \u274c \u274c \u274c \u274c \u274c \u274c Nextclade \u2705 \u2705 \u274c \u274c \u2705 \u2705 \u2705 VADR \u2705 \u2705 \u274c \u2705 \u2705 \u2705 \u2705 Quasitools HyDRA \u274c \u274c \u2705 \u274c \u274c \u274c \u274c IRMA \u274c \u274c \u274c \u274c \u2705 \u274c \u274c Abricate \u274c \u274c \u274c \u274c \u2705 \u274c \u274c % Gene Coverage \u2705 \u2705 \u274c \u274c \u274c \u274c \u274c Antiviral Detection \u274c \u274c \u274c \u274c \u2705 \u274c \u274c GenoFLU \u274c \u274c \u274c \u274c \u2705 \u274c \u274cpangolin Pangolin designates SARS-CoV-2 lineage assignments.
Pangolin Technical Details
Links Task task_pangolin.wdl Software Source Code Pangolin on GitHub Software Documentation Pangolin website Original Publication(s) A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiologynextclade \"Nextclade is an open-source project for viral genome alignment, mutation calling, clade assignment, quality checks and phylogenetic placement.\"
Nextclade Technical Details
Links Task task_nextclade.wdl Software Source Code https://github.com/nextstrain/nextclade Software Documentation Nextclade Original Publication(s) Nextclade: clade assignment, mutation calling and quality control for viral genomes.vadr VADR annotates and validates completed assembly files.
VADR Technical Details
Links Task task_vadr.wdl Software Source Code https://github.com/ncbi/vadr Software Documentation https://github.com/ncbi/vadr/wiki Original Publication(s) For SARS-CoV-2: Faster SARS-CoV-2 sequence validation and annotation for GenBank using VADR For non-SARS_CoV-2: VADR: validation and annotation of virus sequence submissions to GenBankquasitools quasitools performs genome characterization for HIV.
Quasitools Technical Details
Links Task task_quasitools.wdl Software Source Code https://github.com/phac-nml/quasitools/ Software Documentation Quasitools HyDRAirma IRMA assigns types and subtype/lineages in addition to performing assembly of flu genomes. Please see the section above under \"Assembly tasks\" to find more information regarding this tool.
IRMA Technical Details
Links Task task_irma.wdl Software Documentation IRMA website Original Publication(s) Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assemblerabricate Abricate assigns types and subtype/lineages for flu samples
Abricate Technical Details
Links Task task_abricate.wdl (abricate_flu subtask) Software Source Code ABRicate on GitHub Software Documentation ABRicate on GitHubgene_coverage This task calculates the percent of the gene covered above a minimum depth. By default, it runs for SARS-CoV-2 and MPXV, but if a bed file is provided with regions of interest, this task will be run for other organisms as well.
Gene Coverage Technical Details
Links Task task_gene_coverage.wdlflu_antiviral_substitutions This sub-workflow determines which, if any, antiviral mutations are present in the sample.
The assembled HA, NA, PA, PB1 and PB2 segments are compared against a list of known amino-acid substitutions associated with resistance to the antivirals A_315675, compound_367, Favipiravir, Fludase, L_742_001, Laninamivir, Oseltamivir (tamiflu), Peramivir, Pimodivir, Xofluza, and Zanamivir. The list of known antiviral amino acid substitutions can be expanded via optional user input antiviral_aa_subs in the format \"NA:V95A,HA:I97V\", i.e. Protein:AAPositionAA.
Antiviral Substitutions Technical Details
Links Workflow wf_influenza_antiviral_substitutions.wdlgenoflu This sub-workflow determines the whole-genome genotype of an H5N1 flu sample.
GenoFLU Technical Details
Links Task task_genoflu.wdl Software Source Code GenoFLU on GitHub"},{"location":"workflows/genomic_characterization/theiacov/#outputs","title":"Outputs","text":"All TheiaCoV Workflows (not TheiaCoV_FASTA_Batch)
Variable Type Description Workflow abricate_flu_database String ABRicate database used for analysis FASTA, ONT, PE abricate_flu_results File File containing all results from ABRicate FASTA, ONT, PE abricate_flu_subtype String Flu subtype as determined by ABRicate FASTA, ONT, PE abricate_flu_type String Flu type as determined by ABRicate FASTA, ONT, PE abricate_flu_version String Version of ABRicate FASTA, ONT, PE aligned_bai File Index companion file to the bam file generated during the consensus assembly process CL, ONT, PE, SE aligned_bam File Primer-trimmed BAM file; generated during consensus assembly process CL, ONT, PE, SE artic_docker String Docker image utilized for read trimming and consensus genome assembly CL, ONT artic_version String Version of the Artic software utilized for read trimming and conesnsus genome assembly CL, ONT assembly_fasta File Consensus genome assembly; for lower quality flu samples, the output may state \"Assembly could not be generated\" when there is too little and/or too low quality data for IRMA to produce an assembly. Contigs will be ordered from largest to smallest when IRMA is used. CL, ONT, PE, SE assembly_length_unambiguous Int Number of unambiguous basecalls within the consensus assembly CL, FASTA, ONT, PE, SE assembly_mean_coverage Float Mean sequencing depth throughout the consensus assembly. Generated after performing primer trimming and calculated using the SAMtools coverage command CL, ONT, PE, SE assembly_method String Method employed to generate consensus assembly CL, FASTA, ONT, PE, SE auspice_json File Auspice-compatable JSON output generated from Nextclade analysis that includes the Nextclade default samples for clade-typing and the single sample placed on this tree CL, FASTA, ONT, PE, SE auspice_json_flu_ha File Auspice-compatable JSON output generated from Nextclade analysis on Influenza HA segment that includes the Nextclade default samples for clade-typing and the single sample placed on this tree ONT, PE auspice_json_flu_na File Auspice-compatable JSON output generated from Nextclade analysis on Influenza NA segment that includes the Nextclade default samples for clade-typing and the single sample placed on this tree ONT, PE bbduk_docker String Docker image used to run BBDuk PE, SE bwa_version String Version of BWA used to map read data to the reference genome PE, SE consensus_flagstat File Output from the SAMtools flagstat command to assess quality of the alignment file (BAM) CL, ONT, PE, SE consensus_n_variant_min_depth Int Minimum read depth to call variants for iVar consensus and iVar variants PE, SE consensus_stats File Output from the SAMtools stats command to assess quality of the alignment file (BAM) CL, ONT, PE, SE est_coverage_clean Float Estimated coverage of the clean reads ONT est_coverage_raw Float Estimated coverage of the raw reads ONT est_percent_gene_coverage_tsv File Percent coverage for each gene in the organism being analyzed (depending on the organism input) CL, ONT, PE, SE fastp_html_report File HTML report for fastp PE, SE fastp_version String Fastp version used PE, SE fastq_scan_clean1_json File JSON file output fromfastq-scan containing summary stats about clean forward read quality and length PE, SE, CL fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length PE fastq_scan_num_reads_clean_pairs String Number of paired reads after filtering as determined by fastq_scan PE fastq_scan_num_reads_clean1 Int Number of forward reads after filtering as determined by fastq_scan CL, PE, SE fastq_scan_num_reads_clean2 Int Number of reverse reads after filtering as determined by fastq_scan PE fastq_scan_num_reads_raw_pairs String Number of paired reads identified in the input fastq files as determined by fastq_scan PE fastq_scan_num_reads_raw1 Int Number of forward reads identified in the input fastq files as determined by fastq_scan CL, PE, SE fastq_scan_num_reads_raw2 Int Number of reverse reads identified in the input fastq files as determined by fastq_scan PE fastq_scan_r1_mean_q_clean Float Forward read mean quality value after quality trimming and adapter removal fastq_scan_r1_mean_q_raw Float Forward read mean quality value before quality trimming and adapter removal fastq_scan_r1_mean_readlength_clean Float Forward read mean read length value after quality trimming and adapter removal fastq_scan_r1_mean_readlength_raw Float Forward read mean read length value before quality trimming and adapter removal fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length PE, SE, CL fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length PE fastq_scan_version String Version of fastq_scan used for read QC analysis CL, PE, SE fastqc_clean1_html File Graphical visualization of clean forward read quality from fastqc to open in an internet browser PE, SE fastqc_clean2_html File Graphical visualization of clean reverse read quality from fastqc to open in an internet browser PE fastqc_docker String Docker container used for fastqc PE, SE fastqc_num_reads_clean_pairs String Number of read pairs after cleaning by fastqc PE fastqc_num_reads_clean1 Int Number of forward reads after cleaning by fastqc PE, SE fastqc_num_reads_clean2 Int Number of reverse reads after cleaning by fastqc PE fastqc_num_reads_raw_pairs Int Number of raw read pairs as computed by fastqc PE fastqc_num_reads_raw1 Int Number of raw forward/facing reads as computed by fastqc PE, SE fastqc_num_reads_raw2 Int Number of raw reverse-facing reads as computed by fastqc PE fastqc_raw1_html File Graphical visualization of raw forward read quality from fastqc to open in an internet browser PE, SE fastqc_raw2_html File Graphical visualization of raw reverse read quality from fastqc to open in an internet browser PE fastqc_version String Version of fastqc software used PE, SE flu_A_315675_resistance String resistance mutations to A_315675 ONT, PE flu_amantadine_resistance String resistance mutations to amantadine ONT, PE flu_compound_367_resistance String resistance mutations to compound_367 ONT, PE flu_favipiravir_resistance String resistance mutations to favipiravir ONT, PE flu_fludase_resistance String resistance mutations to fludase ONT, PE flu_L_742_001_resistance String resistance mutations to L_742_001 ONT, PE flu_laninamivir_resistance String resistance mutations to laninamivir ONT, PE flu_oseltamivir_resistance String resistance mutations to oseltamivir (Tamiflu\u00ae) ONT, PE flu_peramivir_resistance String resistance mutations to peramivir (Rapivab\u00ae) ONT, PE flu_pimodivir_resistance String resistance mutations to pimodivir ONT, PE flu_rimantadine_resistance String resistance mutations to rimantadine ONT, PE flu_xofluza_resistance String resistance mutations to xofluza (Baloxavir marboxil) ONT, PE flu_zanamivir_resistance String resistance mutations to zanamivir (Relenza\u00ae) ONT, PE genoflu_all_segments String The genotypes for each individual flu segment FASTA, ONT, PE genoflu_genotype String The genotype of the whole genome, based off of the individual segments types FASTA, ONT, PE genoflu_output_tsv File The output file from GenoFLU FASTA, ONT, PE genoflu_version String The version of GenoFLU used FASTA, ONT, PE irma_docker String Docker image used to run IRMA ONT, PE irma_ha_segment_fasta File HA (Haemagglutinin) assembly fasta file ONT, PE irma_mp_segment_fasta File MP (Matrix Protein) assembly fasta file ONT, PE irma_na_segment_fasta File NA (Neuraminidase) assembly fasta file ONT, PE irma_np_segment_fasta File NP (Nucleoprotein) assembly fasta file ONT, PE irma_ns_segment_fasta File NS (Nonstructural) assembly fasta file ONT, PE irma_pa_segment_fasta File PA (Polymerase acidic) assembly fasta file ONT, PE irma_pb1_segment_fasta File PB1 (Polymerase basic 1) assembly fasta file ONT, PE irma_pb2_segment_fasta File PB2 (Polymerase basic 2) assembly fasta file ONT, PE irma_subtype String Flu subtype as determined by IRMA ONT, PE irma_subtype_notes String Helpful note to user about Flu B subtypes. Output will be blank for Flu A samples. For Flu B samples it will state: \"IRMA does not differentiate Victoria and Yamagata Flu B lineages. See abricate_flu_subtype output column\" ONT, PE irma_type String Flu type as determined by IRMA ONT, PE irma_version String Version of IRMA used ONT, PE ivar_tsv File Variant descriptor file generated by iVar variants PE, SE ivar_variant_proportion_intermediate String The proportion of variants of intermediate frequency PE, SE ivar_variant_version String Version of iVar for running the iVar variants command PE, SE ivar_vcf File iVar tsv output converted to VCF format PE, SE ivar_version_consensus String Version of iVar for running the iVar consensus command PE, SE ivar_version_primtrim String Version of iVar for running the iVar trim command PE, SE kraken_human Float Percent of human read data detected using the Kraken2 software CL, ONT, PE, SE kraken_human_dehosted Float Percent of human read data detected using the Kraken2 software after host removal CL, ONT, PE kraken_report File Full Kraken report CL, ONT, PE, SE kraken_report_dehosted File Full Kraken report after host removal CL, ONT, PE kraken_sc2 String Percent of SARS-CoV-2 read data detected using the Kraken2 software CL, ONT, PE, SE kraken_sc2_dehosted String Percent of SARS-CoV-2 read data detected using the Kraken2 software after host removal CL, ONT, PE kraken_target_organism String Percent of target organism read data detected using the Kraken2 software CL, ONT, PE, SE kraken_target_organism_dehosted String Percent of target organism read data detected using the Kraken2 software after host removal CL, ONT, PE kraken_target_organism_name String The name of the target organism; e.g., \"Monkeypox\" or \"Human immunodeficiency virus\" CL, ONT, PE, SE kraken_version String Version of Kraken software used CL, ONT, PE, SE meanbaseq_trim Float Mean quality of the nucleotide basecalls aligned to the reference genome after primer trimming CL, ONT, PE, SE meanmapq_trim Float Mean quality of the mapped reads to the reference genome after primer trimming CL, ONT, PE, SE medaka_reference String Reference sequence used in medaka task CL, ONT medaka_vcf File A VCF file containing the identified variants ONT nanoplot_docker String Docker image used to run Nanoplot ONT nanoplot_html_clean File An HTML report describing the clean reads ONT nanoplot_html_raw File An HTML report describing the raw reads ONT nanoplot_num_reads_clean1 Float Number of clean reads ONT nanoplot_num_reads_raw1 Float Number of raw reads ONT nanoplot_r1_est_coverage_clean Float Estimated coverage on the clean reads by nanoplot ONT nanoplot_r1_est_coverage_raw Float Estimated coverage on the raw reads by nanoplot ONT nanoplot_r1_mean_q_clean Float Mean quality score of clean forward reads ONT nanoplot_r1_mean_q_raw Float Mean quality score of raw forward reads ONT nanoplot_r1_mean_readlength_clean Float Mean read length of clean forward reads ONT nanoplot_r1_mean_readlength_raw Float Mean read length of raw forward reads ONT nanoplot_r1_median_q_clean Float Median quality score of clean forward reads ONT nanoplot_r1_median_q_raw Float Median quality score of raw forward reads ONT nanoplot_r1_median_readlength_clean Float Median read length of clean forward reads ONT nanoplot_r1_median_readlength_raw Float Median read length of raw forward reads ONT nanoplot_r1_n50_clean Float N50 of clean forward reads ONT nanoplot_r1_n50_raw Float N50 of raw forward reads ONT nanoplot_r1_stdev_readlength_clean Float Standard deviation read length of clean forward reads ONT nanoplot_r1_stdev_readlength_raw Float Standard deviation read length of raw forward reads ONT nanoplot_tsv_clean File A TSV report describing the clean reads ONT nanoplot_tsv_raw File A TSV report describing the raw reads ONT nanoplot_version String Version of nanoplot tool used ONT nextclade_aa_dels String Amino-acid deletions as detected by NextClade. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_aa_dels_flu_ha String Amino-acid deletions as detected by NextClade. Specific to flu; it includes deletions for HA segment ONT, PE nextclade_aa_dels_flu_na String Amino-acid deletions as detected by NextClade. Specific to Flu; it includes deletions for NA segment ONT, PE nextclade_aa_subs String Amino-acid substitutions as detected by Nextclade. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_aa_subs_flu_ha String Amino-acid substitutions as detected by Nextclade. Specific to Flu; it includes substitutions for NA segment ONT, PE nextclade_aa_subs_flu_na String Amino-acid substitutions as detected by Nextclade. Specific to Flu; it includes substitutions for NA segment ONT, PE nextclade_clade String Nextclade clade designation, will be blank for Flu. CL, FASTA, ONT, PE, SE nextclade_clade_flu_ha String Nextclade clade designation, specific to Flu NA segment ONT, PE nextclade_clade_flu_na String Nextclade clade designation, specific to Flu HA segment ONT, PE nextclade_docker String Docker image used to run Nextclade CL, FASTA, ONT, PE, SE nextclade_ds_tag String Dataset tag used to run Nextclade. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_ds_tag_flu_ha String Dataset tag used to run Nextclade, specific to Flu HA segment ONT, PE nextclade_ds_tag_flu_na String Dataset tag used to run Nextclade, specific to Flu NA segment ONT, PE nextclade_json File Nextclade output in JSON file format. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_json_flu_ha File Nextclade output in JSON file format, specific to Flu HA segment ONT, PE nextclade_json_flu_na File Nextclade output in JSON file format, specific to Flu NA segment ONT, PE nextclade_lineage String Nextclade lineage designation CL, FASTA, ONT, PE, SE nextclade_qc String QC metric as determined by Nextclade. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_qc_flu_ha String QC metric as determined by Nextclade, specific to Flu HA segment ONT, PE nextclade_qc_flu_na String QC metric as determined by Nextclade, specific to Flu NA segment ONT, PE nextclade_tsv File Nextclade output in TSV file format. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_tsv_flu_ha File Nextclade output in TSV file format, specific to Flu HA segment ONT, PE nextclade_tsv_flu_na File Nextclade output in TSV file format, specific to Flu NA segment ONT, PE nextclade_version String The version of Nextclade software used CL, FASTA, ONT, PE, SE number_Degenerate Int Number of degenerate basecalls within the consensus assembly CL, FASTA, ONT, PE, SE number_N Int Number of fully ambiguous basecalls within the consensus assembly CL, FASTA, ONT, PE, SE number_Total Int Total number of nucleotides within the consensus assembly CL, FASTA, ONT, PE, SE pango_lineage String Pango lineage as determined by Pangolin CL, FASTA, ONT, PE, SE pango_lineage_expanded String Pango lineage without use of aliases; e.g., \"BA.1\" \u2192 \"B.1.1.529.1\" CL, FASTA, ONT, PE, SE pango_lineage_report File Full Pango lineage report generated by Pangolin CL, FASTA, ONT, PE, SE pangolin_assignment_version String The version of the pangolin software (e.g. PANGO or PUSHER) used for lineage assignment CL, FASTA, ONT, PE, SE pangolin_conflicts String Number of lineage conflicts as determined by Pangolin CL, FASTA, ONT, PE, SE pangolin_docker String Docker image used to run Pangolin CL, FASTA, ONT, PE, SE pangolin_notes String Lineage notes as determined by Pangolin CL, FASTA, ONT, PE, SE pangolin_versions String All Pangolin software and database versions CL, FASTA, ONT, PE, SE percent_reference_coverage Float Percent coverage of the reference genome after performing primer trimming; calculated as assembly_length_unambiguous / length of the reference genome (SC2: 29903) x 100 CL, FASTA, ONT, PE, SE percentage_mapped_reads String Percentage of reads that successfully aligned to the reference genome. This value is calculated by number of mapped reads / total number of reads x 100. ONT, PE, SE primer_bed_name String Name of the primer bed files used for primer trimming CL, ONT, PE, SE primer_trimmed_read_percent Float Percentage of read data with primers trimmed as determined by iVar trim PE, SE qc_check String The results of the QC Check task CL, FASTA, ONT, PE, SE qc_standard File The file used in the QC Check task containing the QC thresholds. CL, FASTA, ONT, PE, SE quasitools_coverage_file File The coverage report created by Quasitools HyDRA ONT, PE quasitools_date String Date of Quasitools analysis ONT, PE quasitools_dr_report File Drug resistance report created by Quasitools HyDRA ONT, PE quasitools_hydra_vcf File The VCF created by Quasitools HyDRA ONT, PE quasitools_mutations_report File The mutation report created by Quasitools HyDRA ONT, PE quasitools_version String Version of Quasitools used ONT, PE read_screen_clean String A PASS or FAIL flag for input reads after cleaning ONT, PE, SE read_screen_raw String A PASS or FAIL flag for input reads ONT, PE, SE read1_aligned File Forward read file of only aligned reads CL, ONT, PE, SE read1_clean File Forward read file after quality trimming and adapter removal PE, SE read1_dehosted File Dehosted forward reads; suggested read file for SRA submission CL, ONT, PE read1_trimmed File Forward read file after quality trimming and adapter removal ONT read1_unaligned File Forward read file of unaligned reads PE, SE read2_aligned File Reverse read file of only aligned reads PE read2_clean File Reverse read file after quality trimming and adapter removal PE read2_dehosted File Dehosted reverse reads; suggested read file for SRA submission PE read2_unaligned File Reverse read file of unaligned reads PE samtools_version String The version of SAMtools used to sort and index the alignment file ONT, PE, SE samtools_version_consensus String The version of SAMtools used to create the pileup before running iVar consensus PE, SE samtools_version_primtrim String The version of SAMtools used to create the pileup before running iVar trim PE, SE samtools_version_stats String The version of SAMtools used to assess the quality of read mapping CL, PE, SE sc2_s_gene_mean_coverage Float Mean read depth for the S gene in SARS-CoV-2 CL, ONT, PE, SE sc2_s_gene_percent_coverage Float Percent coverage of the S gene in SARS-CoV-2 CL, ONT, PE, SE seq_platform String Description of the sequencing methodology used to generate the input read data CL, FASTA, ONT, PE, SE sorted_bam_unaligned File A BAM file that only contains reads that did not align to the reference PE, SE sorted_bam_unaligned_bai File Index companion file to a BAM file that only contains reads that did not align to the reference PE, SE theiacov_clearlabs_analysis_date String Date of analysis CL theiacov_clearlabs_version String Version of PHB used for running the workflow CL theiacov_fasta_analysis_date String Date of analysis FASTA theiacov_fasta_version String Version of PHB used for running the workflow FASTA theiacov_illumina_pe_analysis_date String Date of analysis PE theiacov_illumina_pe_version String Version of PHB used for running the workflow PE theiacov_illumina_se_analysis_date String Date of analysis SE theiacov_illumina_se_version String Version of PHB used for running the workflow SE theiacov_ont_analysis_date String Date of analysis ONT theiacov_ont_version String Version of PHB used for running the workflow ONT trimmomatic_docker String Docker container used with trimmomatic PE, SE trimmomatic_version String The version of Trimmomatic used PE, SE vadr_alerts_list File A file containing all of the fatal alerts as determined by VADR CL, FASTA, ONT, PE, SE vadr_all_outputs_tar_gz File A .tar.gz file (gzip-compressed tar archive file) containing all outputs from the VADR command v-annotate.pl. This file must be uncompressed & extracted to see the many files within. See https://github.com/ncbi/vadr/blob/master/documentation/formats.md#format-of-v-annotatepl-output-filesfor more complete description of all files present within the archive. Useful when deeply investigating a sample's genome & annotations. CL, FASTA, ONT, PE, SE vadr_classification_summary_file File Per-sequence tabular classification file. See https://github.com/ncbi/vadr/blob/master/documentation/formats.md#explanation-of-sqc-suffixed-output-files for more complete description. CL, FASTA, ONT, PE, SE vadr_docker String Docker image used to run VADR CL, FASTA, ONT, PE, SE vadr_fastas_zip_archive File Zip archive containing all fasta files created during VADR analysis CL, FASTA, ONT, PE, SE vadr_feature_tbl_fail File 5 column feature table output for failing sequences. See https://github.com/ncbi/vadr/blob/master/documentation/formats.md#format-of-v-annotatepl-output-files for more complete description. CL, FASTA, ONT, PE, SE vadr_feature_tbl_pass File 5 column feature table output for passing sequences. See https://github.com/ncbi/vadr/blob/master/documentation/formats.md#format-of-v-annotatepl-output-files for more complete description. CL, FASTA, ONT, PE, SE vadr_num_alerts String Number of fatal alerts as determined by VADR CL, FASTA, ONT, PE, SE variants_from_ref_vcf File Number of variants relative to the reference genome CL TheiaCoV_FASTA_Batch_PHB Outputs"},{"location":"workflows/genomic_characterization/theiacov/#theiacov-fasta-batch-outputs","title":"TheiaCoV_FASTA_Batch Outputs","text":"Overwrite Warning
TheiaCoV_FASTA_Batch_PHB workflow will output results to the set-level data table in addition to overwriting the Pangolin & Nextclade output columns in the sample-level data table. Users can view the set-level workflow output TSV file called \"Datatable\" to view exactly which columns were overwritten in the sample-level data table.
The TheiaEuk_Illumina_PE workflow is for the assembly, quality assessment, and characterization of fungal genomes. It is designed to accept Illumina paired-end sequencing data as the primary input. It is currently intended only for haploid fungal genomes like Candida auris. Analyzing diploid genomes using TheiaEuk should be attempted only with expert attention to the resulting genome quality.
All input reads are processed through \"core tasks\" in each workflow. The core tasks include raw read quality assessment, read cleaning (quality trimming and adapter removal), de novo assembly, assembly quality assessment, and species taxon identification. For some taxa identified, taxa-specific sub-workflows will be automatically activated, undertaking additional taxa-specific characterization steps, including clade-typing and/or antifungal resistance detection.
TheiaEuk Workflow Diagram
"},{"location":"workflows/genomic_characterization/theiaeuk/#inputs","title":"Inputs","text":"Input read data
The TheiaEuk_Illumina_PE workflow takes in Illumina paired-end read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip prior to Terra upload to minimize data upload time.
By default, the workflow anticipates\u00a02 x 150bp\u00a0reads (i.e. the input reads were generated using a 300-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate shorter read data, such as the 2 x 75bp reads generated using a 150-cycle sequencing kit.
All input reads are processed through \"core tasks\" in the TheiaEuk workflows. These undertake read trimming and assembly appropriate to the input data type, currently only Illumina paired-end data. TheiaEuk workflow subsequently launch default genome characterization modules for quality assessment, and additional taxa-specific characterization steps. When setting up the workflow, users may choose to use \"optional tasks\" or alternatives to tasks run in the workflow by default.
"},{"location":"workflows/genomic_characterization/theiaeuk/#core-tasks","title":"Core tasks","text":"These tasks are performed regardless of organism. They perform read trimming and various quality control steps.
versioning: Version capture for TheiaEuk The versioning task captures the workflow version from the GitHub (code repository) version.
Version Capture Technical details
Links Task task_versioning.wdlscreen: Total Raw Read Quantification and Genome Size Estimation (optional, on by default) The screen task ensures the quantity of sequence data is sufficient to undertake genomic analysis. It uses fastq-scan and bash commands for quantification of reads and base pairs, and mash sketching to estimate the genome size and its coverage. At each step, the results are assessed relative to pass/fail criteria and thresholds that may be defined by optional user inputs. Samples that do not meet these criteria will not be processed further by the workflow:
min_reads.min_proportion basepairs are in either the reads1 or read2 files.min_basepairs basepairsmin_genome_size or bigger than max_genome_size.min_coverage.Read screening is undertaken on both the raw and cleaned reads. The task may be skipped by setting the skip_screen variable to true.
Default values vary between the PE and SE workflow. The rationale for these default values can be found below.
Variable Rationaleskip_screen Prevent the read screen from running min_reads Minimum number of base pairs for 20x coverage of Hansenula polymorpha divided by 300 (longest Illumina read length) min_basepairs Greater than 10x coverage of Hansenula polymorpha min_genome_size Based on the Hansenula polymorpha genome - the smallest fungal genome as of 2015-04-02 (8.97 Mbp) max_genome_size Based on the Cenococcum geophilum genome, the biggest pathogenic fungal genome, (177.57 Mbp) min_coverage A bare-minimum coverage for genome characterization. Higher coverage would be required for high-quality phylogenetics. min_proportion Greater than 50% reads are in the read1 file; others are in the read2 file Screen Technical Details
There is a single WDL task for read screening. The screen task is run twice, once for raw reads and once for clean reads.
Rasusa: Read subsampling (optional, on by default) The Rasusa task performs subsampling of the raw reads. By default, this task will subsample reads to a depth of 150X using the estimated genome length produced during the preceding raw read screen. The user can prevent the task from being launched by setting the call_rasusavariable to false.
The user can also provide an estimated genome length for the task to use for subsampling using the genome_size variable. In addition, the read depth can be modified using the subsample_coverage variable.
Rasusa Technical Details
Links Task task_rasusa.wdl Software Source Code Rasusa on GitHub Software Documentation Rasusa on GitHub Original Publication(s) Rasusa: Randomly subsample sequencing reads to a specified coverageread_QC_trim: Read Quality Trimming, Adapter Removal, Quantification, and Identification read_QC_trim is a sub-workflow within TheiaEuk that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below.
Read quality trimming
Either trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.
If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:
Parameter Explanation -g enables polyG tail trimming -5 20 enables read end-trimming -3 20 enables read end-trimming --detect_adapter_for_pe enables adapter-trimming only for paired-end readsAdapter removal
The BBDuk task removes adapters from sequence reads. To do this:
Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.
Read Quantification
There are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. In TheiaProk_Illumina_PE, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.
Read Identification (optional)
The MIDAS task is for the identification of reads to detect contamination with non-target taxa. This task is optional and turned off by default. It can be used by setting the call_midas input variable to true.
The MIDAS tool was originally designed for metagenomic sequencing data but has been co-opted for use with bacterial isolate WGS methods. It can be used to detect contamination present in raw sequencing data by estimating bacterial species abundance in bacterial isolate WGS data. If a secondary genus is detected above a relative frequency of 0.01 (1%), then the sample should fail QC and be investigated further for potential contamination.
This task is similar to those used in commercial software, BioNumerics, for estimating secondary species abundance.
How are the MIDAS output columns determined?Example MIDAS report in the ****midas_report column:
MIDAS report column descriptions:
The value in the midas_primary_genus column is derived by ordering the rows in order of \"relative_abundance\" and identifying the genus of top species in the \"species_id\" column (Salmonella). The value in the midas_secondary_genus column is derived from the genus of the second-most prevalent genus in the \"species_id\" column (Citrobacter). The midas_secondary_genus_abundance column is the \"relative_abundance\" of the second-most prevalent genus (0.009477003). The midas_secondary_genus_coverage is the \"coverage\" of the second-most prevalent genus (0.995216227).
read_QC_trim Technical Details
Links Sub-workflow wf_read_QC_trim_pe.wdl Tasks task_fastp.wdltask_trimmomatic.wdltask_bbduk.wdltask_fastq_scan.wdltask_midas.wdltask_kraken2.wdl Software Source Code fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2 Software Documentation fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2 Original Publication(s) Trimmomatic: a flexible trimmer for Illumina sequence datafastp: an ultra-fast all-in-one FASTQ preprocessorAn integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeographyImproved metagenomic analysis with Kraken 2"},{"location":"workflows/genomic_characterization/theiaeuk/#assembly-tasks","title":"Assembly tasks","text":"These tasks assemble the reads into a de novo assembly and assess the quality of the assembly.
shovill: De novo Assembly De Novo assembly will be undertaken only for samples that have sufficient read quantity and quality, as determined by the screen task assessment of clean reads.
In TheiaEuk, assembly is performed using the Shovill pipeline. This undertakes the assembly with one of four assemblers (SKESA (default), SPAdes, Velvet, Megahit), but also performs a number of pre- and post-processing steps to improve the resulting genome assembly. Shovill uses an estimated genome size (see here). If this is not provided by the user as an optional input, Shovill will estimate the genome size using mash. Adaptor trimming can be undertaken with Shovill by setting the trim option to \"true\", but this is set to \"false\" by default as alternative adapter trimming is undertaken in the TheiaEuk workflow.
De novo assembly is the process or product of attempting to reconstruct a genome from scratch (without prior knowledge of the genome) using sequence reads. Assembly of fungal genomes from short-reads will produce multiple contigs per chromosome rather than a single contiguous sequence for each chromosome.
Shovill Technical Details
Links TheiaEuk WDL Task task_shovill.wdl Software Source Code Shovill on GitHub Software Documentation Shovill on GitHubQUAST: Assembly Quality Assessment QUAST (QUality ASsessment Tool) evaluates genome assemblies by computing several metrics that describe the assembly quality, including the total number of bases in the assembly, the length of the largest contig in the assembly, and the assembly percentage GC content.
QUAST Technical Details
Links Task task_quast.wdl Software Source Code QUAST on GitHub Software Documentation https://quast.sourceforge.net/docs/manual.html Orginal publication QUAST: quality assessment tool for genome assembliesCG-Pipeline: Assessment of Read Quality, and Estimation of Genome Coverage Thecg_pipeline task generates metrics about read quality and estimates the coverage of the genome using the \"run_assembly_readMetrics.pl\" script from CG-Pipeline. The genome coverage estimates are calculated using both using raw and cleaned reads, using either a user-provided genome_size or the estimated genome length generated by QUAST.
CG-Pipeline Technical Details
The cg_pipeline task is run twice in TheiaEuk, once with raw reads, and once with clean reads.
These tasks are performed regardless of the organism and provide quality control and taxonomic assignment.
GAMBIT: Taxon Assignment GAMBIT determines the taxon of the genome assembly using a k-mer based approach to match the assembly sequence to the closest complete genome in a database, thereby predicting its identity. Sometimes, GAMBIT can confidently designate the organism to the species level. Other times, it is more conservative and assigns it to a higher taxonomic rank.
For additional details regarding the GAMBIT tool and a list of available GAMBIT databases for analysis, please consult the GAMBIT tool documentation.
GAMBIT Technical Details
Links Task task_gambit.wdl Software Source Code GAMBIT on GitHub Software Documentation GAMBIT ReadTheDocs Original Publication(s) GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identificationBUSCO: Assembly Quality Assessment BUSCO (Benchmarking Universal Single-Copy Orthologue) attempts to quantify the completeness and contamination of an assembly to generate quality assessment metrics. It uses taxa-specific databases containing genes that are all expected to occur in the given taxa, each in a single copy. BUSCO examines the presence or absence of these genes, whether they are fragmented, and whether they are duplicated (suggestive that additional copies came from contaminants).
BUSCO notation
Here is an example of BUSCO notation: C:99.1%[S:98.9%,D:0.2%],F:0.0%,M:0.9%,n:440. There are several abbreviations used in this output:
A high equity assembly will use the appropriate database for the taxa, have high complete (C) and single-copy (S) percentages, and low duplicated (D), fragmented (F) and missing (M) percentages.
BUSCO Technical Details
Links Task task_busco.wdl Software Source Code BUSCO on GitLab Software Documentation https://busco.ezlab.org/ Orginal publication BUSCO: assessing genome assembly and annotation completeness with single-copy orthologsQC_check: Check QC Metrics Against User-Defined Thresholds (optional) The qc_check task compares generated QC metrics against user-defined thresholds for each metric. This task will run if the user provides a qc_check_table .tsv file. If all QC metrics meet the threshold, the qc_check output variable will read QC_PASS. Otherwise, the output will read QC_NA if the task could not proceed or QC_ALERT followed by a string indicating what metric failed.
The qc_check task applies quality thresholds according to the sample taxa. The sample taxa is taken from the gambit_predicted_taxon value inferred by the GAMBIT module OR can be manually provided by the user using the expected_taxon workflow input.
TheiaEuk_Illumina_PE_PHB: theiaeuk_qc_check_template.tsv
Example Purposes Only
QC threshold values shown are for example purposes only and should not be presumed to be sufficient for every dataset.
QC_Check Technical Details
Links Task task_qc_check_phb.wdl"},{"location":"workflows/genomic_characterization/theiaeuk/#organism-specific-characterization","title":"Organism-specific characterization","text":"The TheiaEuk workflow automatically activates taxa-specific tasks after identification of the relevant taxa using GAMBIT. Many of these taxa-specific tasks do not require any additional inputs from the user.
Two tools are deployed when Candida auris is identified.
Cladetyping: clade determinationGAMBIT is used to determine the clade of the specimen by comparing the sequence to five clade-specific reference files. The output of the clade typing task will be used to specify the reference genome for the antifungal resistance detection tool.
Default reference genomes used for clade typing and antimicrobial resistance gene detection of C. auris Clade Genome Accession Assembly Name Strain NCBI Submitter Included mutations in AMR genes (not comprehensive) Candida auris Clade I GCA_002759435.2 Cand_auris_B8441_V2 B8441 Centers for Disease Control and Prevention Candida auris Clade II GCA_003013715.2 ASM301371v2 B11220 Centers for Disease Control and Prevention Candida auris Clade III GCA_002775015.1 Cand_auris_B11221_V1 B11221 Centers for Disease Control and Prevention ERG11 V125A/F126L Candida auris Clade IV GCA_003014415.1 Cand_auris_B11243 B11243 Centers for Disease Control and Prevention ERG11 Y132F Candida auris Clade V GCA_016809505.1 ASM1680950v1 IFRC2087 Centers for Disease Control and PreventionCladetyping Technical Details
Links Task task_cauris_cladetyping.wdl Software Source Code GAMBIT on GitHub Software Documentation GAMBIT Overview Original Publication(s) GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification TheiaEuk: a species-agnostic bioinformatics workflow for fungal genomic characterization Snippy Variants: antifungal resistance detectionTo detect mutations that may confer antifungal resistance, Snippy is used to find all variants relative to the clade-specific reference, then these variants are queried for product names associated with resistance.
The genes in which there are known resistance-conferring mutations for this pathogen are:
We query Snippy results to see if any mutations were identified in those genes. By default, we automatically check for the following loci (which can be overwritten by the user). You will find the mutations next to the locus tag in the theiaeuk_snippy_variants_hits column corresponding gene name (see below):
For example, one sample may have the following output for the theiaeuk_snippy_variants_hits column:
lanosterol.14-alpha.demethylase: lanosterol 14-alpha demethylase (missense_variant c.428A>G p.Lys143Arg; C:266 T:0),B9J08_000401: hypothetical protein (stop_gained c.424C>T p.Gln142*; A:70 G:0)\nBased on this, we can tell that ERG11 has a missense variant at position 143 (Lysine to Arginine) and B9J08_000401 (which is FLO8) has a stop-gained variant at position 142 (Glutamine to Stop).
Known resistance-conferring mutations for Candida aurisMutations in these genes that are known to confer resistance are shown below
Organism Found in Gene name Gene locus AA mutation Drug Reference Candida auris Human ERG11 Y132F Fluconazole Simultaneous Emergence of Multidrug-Resistant Candida auris on 3 Continents Confirmed by Whole-Genome Sequencing and Epidemiological Analyses Candida auris Human ERG11 K143R Fluconazole Simultaneous Emergence of Multidrug-Resistant Candida auris on 3 Continents Confirmed by Whole-Genome Sequencing and Epidemiological Analyses Candida auris Human ERG11 F126T Fluconazole Simultaneous Emergence of Multidrug-Resistant Candida auris on 3 Continents Confirmed by Whole-Genome Sequencing and Epidemiological Analyses Candida auris Human FKS1 S639P Micafungin Activity of CD101, a long-acting echinocandin, against clinical isolates of Candida auris Candida auris Human FKS1 S639P Caspofungin Activity of CD101, a long-acting echinocandin, against clinical isolates of Candida auris Candida auris Human FKS1 S639P Anidulafungin Activity of CD101, a long-acting echinocandin, against clinical isolates of Candida auris Candida auris Human FKS1 S639F Micafungin A multicentre study of antifungal susceptibility patterns among 350 Candida auris isolates (2009\u201317) in India: role of the ERG11 and FKS1 genes in azole and echinocandin resistance Candida auris Human FKS1 S639F Caspofungin A multicentre study of antifungal susceptibility patterns among 350 Candida auris isolates (2009\u201317) in India: role of the ERG11 and FKS1 genes in azole and echinocandin resistance Candida auris Human FKS1 S639F Anidulafungin A multicentre study of antifungal susceptibility patterns among 350 Candida auris isolates (2009\u201317) in India: role of the ERG11 and FKS1 genes in azole and echinocandin resistance Candida auris Human FUR1 CAMJ_004922 F211I 5-flucytosine Genomic epidemiology of the UK outbreak of the emerging human fungal pathogen Candida aurisSnippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub Candida albicansWhen this species is detected by the taxon ID tool, an antifungal resistance detection task is deployed.
Snippy Variants: antifungal resistance detectionTo detect mutations that may confer antifungal resistance, Snippy is used to find all variants relative to the clade-specific reference, and these variants are queried for product names associated with resistance.
The genes in which there are known resistance-conferring mutations for this pathogen are:
We query Snippy results to see if any mutations were identified in those genes. By default, we automatically check for the following loci (which can be overwritten by the user). You will find the mutations next to the locus tag in the theiaeuk_snippy_variants_hits column corresponding gene name (see below):
Snippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub Aspergillus fumigatusWhen this species is detected by the taxon ID tool an antifungal resistance detection task is deployed.
Snippy Variants: antifungal resistance detectionTo detect mutations that may confer antifungal resistance, Snippy is used to find all variants relative to the clade-specific reference, and these variants are queried for product names associated with resistance.
The genes in which there are known resistance-conferring mutations for this pathogen are:
We query Snippy results to see if any mutations were identified in those genes. By default, we automatically check for the following loci (which can be overwritten by the user). You will find the mutations next to the locus tag in the theiaeuk_snippy_variants_hits column corresponding gene name (see below):
Snippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub Cryptococcus neoformansWhen this species is detected by the taxon ID tool an antifungal resistance detection task is deployed.
Snippy Variants: antifungal resistance detectionTo detect mutations that may confer antifungal resistance, Snippy is used to find all variants relative to the clade-specific reference, and these variants are queried for product names associated with resistance.
The genes in which there are known resistance-conferring mutations for this pathogen are:
We query Snippy results to see if any mutations were identified in those genes. By default, we automatically check for the following loci (which can be overwritten by the user). You will find the mutations next to the locus tag in the theiaeuk_snippy_variants_hits column corresponding gene name (see below):
Snippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/genomic_characterization/theiaeuk/#outputs","title":"Outputs","text":"Variable Type Description cg_pipeline_docker String Docker file used for running CG-Pipeline on cleaned reads cg_pipeline_report File TSV file of read metrics from raw reads, including average read length, number of reads, and estimated genome coverage est_coverage_clean Float Estimated coverage calculated from clean reads and genome length est_coverage_raw Float Estimated coverage calculated from raw reads and genome length fastq_scan_clean1_json File JSON file output fromfastq-scan containing summary stats about clean forward read quality and length fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length r1_mean_q_clean Float Mean quality score of clean forward reads r1_mean_q_raw Float Mean quality score of raw forward reads r2_mean_q_clean Float Mean quality score of clean reverse reads r2_mean_q_raw Float Mean quality score of raw reverse reads fastq_scan_version String Version of fastq-scan software used gambit_closest_genomes File CSV file listing genomes in the GAMBIT database that are most similar to the query assembly gambit_db_version String Version of GAMBIT used gambit_docker String GAMBIT docker file used gambit_predicted_taxon String Taxon predicted by GAMBIT gambit_predicted_taxon_rank String Taxon rank of GAMBIT taxon prediction gambit_report File GAMBIT report in a machine-readable format gambit_version String Version of GAMBIT software used assembly_length Int Length of assembly (total contig length) as determined by QUAST n50_value Int N50 of assembly calculated by QUAST number_contigs Int Total number of contigs in assembly quast_report File TSV report from QUAST quast_version String Software version of QUAST used rasusa_version String Version of rasusa used read1_subsampled File Subsampled read1 file read2_subsampled File Subsampled read2 file bbduk_docker String BBDuk docker image used fastp_version String Version of fastp software used read1_clean File Clean forward reads file read2_clean File Clean reverse reads file num_reads_clean_pairs String Number of read pairs after cleaning num_reads_clean1 Int Number of forward reads after cleaning num_reads_clean2 Int Number of reverse reads after cleaning num_reads_raw_pairs String Number of input read pairs num_reads_raw1 Int Number of input forward reads num_reads_raw2 Int Number of input reverse reads trimmomatic_version String Version of trimmomatic used clean_read_screen String PASS or FAIL result from clean read screening; FAIL accompanied by the reason for failure raw_read_screen String PASS or FAIL result from raw read screening; FAIL accompanied by thereason for failure assembly_fasta File https://github.com/tseemann/shovill#contigsfa contigs_fastg File Assembly graph if megahit used for genome assembly contigs_gfa File Assembly graph if spades used for genome assembly contigs_lastgraph File Assembly graph if velvet used for genome assembly shovill_pe_version String Shovill version used theiaeuk_snippy_variants_bam File BAM file produced by the snippy module theiaeuk_snippy_variants_gene_query_results File File containing all lines from variants file matching gene query terms theiaeuk_snippy_variants_hits String String of all variant file entries matching gene query term theiaeuk_snippy_variants_outdir_tarball File Tar compressed file containing full snippy output directory theiaeuk_snippy_variants_query String The gene query term(s) used to search variant theiaeuk_snippy_variants_query_check String Were the gene query terms present in the refence annotated genome file theiaeuk_snippy_variants_reference_genome File The reference genome used in the alignment and variant calling theiaeuk_snippy_variants_results File The variants file produced by snippy theiaeuk_snippy_variants_summary File A file summarizing the variants detected by snippy theiaeuk_snippy_variants_version String The version of the snippy_variants module being used seq_platform String Sequencing platform inout by the user theiaeuk_illumina_pe_analysis_date String Date of TheiaProk workflow execution theiaeuk_illumina_pe_version String TheiaProk workflow version used"},{"location":"workflows/genomic_characterization/theiameta/","title":"TheiaMeta","text":""},{"location":"workflows/genomic_characterization/theiameta/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Any Taxa PHB v2.2.0 Yes Sample-level"},{"location":"workflows/genomic_characterization/theiameta/#theiameta-workflows","title":"TheiaMeta Workflows","text":"Genomic characterization of pathogens is an increasing priority for public health laboratories globally. The workflows in the TheiaMeta Genomic Characterization Series make the analysis of pathogens from metagenomic samples easy by taking raw next-generation sequencing (NGS) data and generating metagenome-assembled genomes (MAGs), either using a reference-genome or not.
TheiaMeta can use one of two distinct methods for generating and processing the final assembly:
TheiaMeta Workflow Diagram
"},{"location":"workflows/genomic_characterization/theiameta/#inputs","title":"Inputs","text":"The\u00a0TheiaMeta_Illumina_PE workflow\u00a0processes Illumina paired-end (PE) reads generated for metagenomic characterization (typically by shotgun). By default, this workflow will assume that input reads were generated using a 300-cycle sequencing kit (i.e. 2 x 150 bp reads). Modifications to the optional parameter for trim_minlen may be required to accommodate shorter read data, such as 2 x 75bp reads generated using a 150-cycle sequencing kit.
versioning: Version Capture for TheiaMeta The versioning task captures the workflow version from the GitHub (code repository) version.
Version Capture Technical details
Links Task task_versioning.wdl"},{"location":"workflows/genomic_characterization/theiameta/#read-cleaning-and-qc","title":"Read Cleaning and QC","text":"HRRT: Human Host Sequence Removal All reads of human origin are removed, including their mates, by using NCBI's human read removal tool (HRRT).
HRRT is based on the SRA Taxonomy Analysis Tool and employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records with any k-mers found in non-Eukaryota RefSeq records subtracted from the database.
NCBI-Scrub Technical Details
Links Task task_ncbi_scrub.wdl Software Source Code NCBI Scrub on GitHub Software Documentation https://github.com/ncbi/sra-human-scrubber/blob/master/README.mdread_QC_trim: Read Quality Trimming, Adapter Removal, Quantification, and Identification read_QC_trim is a sub-workflow within TheiaMeta that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below.
Read quality trimming
Either trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.
If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:
Parameter Explanation -g enables polyG tail trimming -5 20 enables read end-trimming -3 20 enables read end-trimming --detect_adapter_for_pe enables adapter-trimming only for paired-end readsAdapter removal
The BBDuk task removes adapters from sequence reads. To do this:
Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.
Read Quantification
There are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. In TheiaProk_Illumina_PE, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.
Read Identification (optional)
The MIDAS task is for the identification of reads to detect contamination with non-target taxa. This task is optional and turned off by default. It can be used by setting the call_midas input variable to true.
The MIDAS reference database, located at gs://theiagen-large-public-files-rp/terra/theiaprok-files/midas/midas_db_v1.2.tar.gz, is provided as the default. It is possible to provide a custom database. More information is available here.
Example MIDAS report in the ****midas_report column:
MIDAS report column descriptions:
read_QC_trim Technical Details
Links Sub-workflow wf_read_QC_trim_pe.wdlwf_read_QC_trim_se.wdl Tasks task_fastp.wdltask_trimmomatic.wdltask_bbduk.wdltask_fastq_scan.wdltask_midas.wdltask_kraken2.wdl Software Source Code fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2 Software Documentation fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2 Original Publication(s) Trimmomatic: a flexible trimmer for Illumina sequence datafastp: an ultra-fast all-in-one FASTQ preprocessorAn integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeographyImproved metagenomic analysis with Kraken 2kraken: Taxonomic Classification Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
Kraken2 is run on the set of raw reads, provided as input, as well as the set of clean reads that are resulted from the read_QC_trim workflow
Database-dependent
The Kraken2 software is database-dependent and taxonomic assignments are highly sensitive to the database used. An appropriate database should contain the expected organism(s) (e.g. Escherichia coli) and other taxa that may be present in the reads (e.g. Citrobacter freundii, a common contaminant).
Kraken2 Technical Details
Links Task task_kraken2.wdl Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/wiki Original Publication(s) Improved metagenomic analysis with Kraken 2"},{"location":"workflows/genomic_characterization/theiameta/#assembly","title":"Assembly","text":"metaspades: De Novo Metagenomic Assembly While metagenomics has emerged as a technology of choice for analyzing bacterial populations, the assembly of metagenomic data remains challenging. A dedicated metagenomic assembly algorithm is necessary to circumvent the challenge of interpreting variation. metaSPAdes addresses various challenges of metagenomic assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes.
metaspades is a de novo assembler that first constructs a de Bruijn graph of all the reads using the SPAdes algorithm. Through various graph simplification procedures, paths in the assembly graph are reconstructed that correspond to long genomic fragments within the metagenome. For more details, please see the original publication.
MetaSPAdes Technical Details
Links Task task_metaspades.wdl Software Source Code SPAdes on GitHub Software Documentation SPAdes Manual Original Publication(s) metaSPAdes: a new versatile metagenomic assemblerminimap2: Assembly Alignment and Contig Filtering If a reference genome is provided through the reference optional input, the assembly produced with metaspades will be mapped to the reference genome with minimap2. The contigs which align to the reference are retrieved and returned in the assembly_fasta output.
minimap2 is a popular aligner that is used for correcting the assembly produced by metaSPAdes. This is done by aligning the reads back to the generated assembly or a reference genome.
In minimap2, \"modes\" are a group of preset options. Two different modes are used in this task depending on whether a reference genome is provided.
If a reference genome is not provided, the only mode used in this task is sr which is intended for \"short single-end reads without splicing\". The sr mode indicates the following parameters should be used: -k21 -w11 --sr --frag=yes -A2 -B8 -O12,32 -E2,1 -b0 -r100 -p.5 -N20 -f1000,5000 -n2 -m20 -s40 -g100 -2K50m --heap-sort=yes --secondary=no. The output file is in SAM format.
If a reference genome is provided, then after the draft assembly polishing with pilon, this task runs again with the mode set to asm20 which is intended for \"long assembly to reference mapping\". The asm20 mode indicates the following parameters should be used: -k19 -w10 -U50,500 --rmq -r100k -g10k -A1 -B4 -O6,26 -E2,1 -s200 -z200 -N50. The output file is in PAF format.
For more information, please see the minimap2 manpage
minimap2 Technical Details
Links Task task_minimap2.wdl Software Source Code minimap2 on GitHub Software Documentation minimap2 Original Publication(s) Minimap2: pairwise alignment for nucleotide sequencessamtools: SAM File Conversion This task converts the output SAM file from minimap2 and converts it to a BAM file. It then sorts the BAM based on the read names, and then generates an index file.
samtools Technical Details
Links Task task_samtools.wdl Software Source Code samtools on GitHub Software Documentation samtools Original Publication(s) The Sequence Alignment/Map format and SAMtoolsTwelve Years of SAMtools and BCFtoolspilon: Assembly Polishing pilon is a tool that uses read alignment to correct errors in an assembly. It is used to polish the assembly produced by metaSPAdes. The input to Pilon is the sorted BAM file produced by samtools, and the original draft assembly produced by metaspades.
pilon Technical Details
Links Task task_pilon.wdl Software Source Code Pilon on GitHub Software Documentation Pilon Wiki Original Publication(s) Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement"},{"location":"workflows/genomic_characterization/theiameta/#assembly-qc","title":"Assembly QC","text":"quast: Assembly Quality Assessment QUAST stands for QUality ASsessment Tool. It evaluates genome/metagenome assemblies by computing various metrics without a reference being necessary. It includes useful metrics such as number of contigs, length of the largest contig and N50.
QUAST Technical Details
Links Task task_quast.wdl Software Source Code QUAST on GitHub Software Documentation https://quast.sourceforge.net/ Original Publication(s) QUAST: quality assessment tool for genome assemblies"},{"location":"workflows/genomic_characterization/theiameta/#binning","title":"Binning","text":"semibin2: Metagenomic binning (if a reference is NOT provided) If no reference genome is provided through the reference optional input, the assembly produced with metaspades will be binned with semibin2, a a command tool for metagenomic binning with deep learning.
fastq-scan containing summary stats about clean forward read quality and length fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length fastq_scan_num_reads_clean_pairs String Number of read pairs after cleaning as calculated by fastq_scan fastq_scan_num_reads_clean1 Int Number of forward reads after cleaning as calculated by fastq_scan fastq_scan_num_reads_clean2 Int Number of reverse reads after cleaning as calculated by fastq_scan fastq_scan_num_reads_raw_pairs String Number of input read pairs as calculated by fastq_scan fastq_scan_num_reads_raw1 Int Number of input forward reads as calculated by fastq_scan fastq_scan_num_reads_raw2 Int Number of input reserve reads as calculated by fastq_scan fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length fastq_scan_version String fastq_scan version fastqc_clean1_html File Graphical visualization of clean forward read quality from fastqc to open in an internet browser fastqc_clean2_html File Graphical visualization of clean reverse read quality from fastqc to open in an internet browser fastqc_docker String Docker container used for fastqc fastqc_num_reads_clean_pairs String Number of read pairs after cleaning by fastqc fastqc_num_reads_clean1 Int Number of forward reads after cleaning by fastqc fastqc_num_reads_clean2 Int Number of reverse reads after cleaning by fastqc fastqc_num_reads_raw_pairs String Number of input read pairs by fastqc fastqc_num_reads_raw1 Int Number of input forward reads by fastqc fastqc_num_reads_raw2 Int Number of input reverse reads by fastqc fastqc_raw1_html File Graphical visualization of raw forward read quality from fastqc to open in an internet browser fastqc_raw2_html File Graphical visualization of raw reverse read qualityfrom fastqc to open in an internet browser fastqc_version String Version of fastqc software used kraken2_docker String Docker image of kraken2 kraken2_percent_human_clean Float Percentage of human-classified reads in the sample's clean reads kraken2_percent_human_raw Float Percentage of human-classified reads in the sample's raw reads kraken2_report_clean File Full Kraken report for the sample's clean reads kraken2_report_raw File Full Kraken report for the sample's raw reads kraken2_version String Version of kraken krona_docker String Docker image of Krona krona_html_clean File The KronaPlot after reads are cleaned krona_html_raw File The KronaPlot before reads are cleaned krona_version String Version of Krona largest_contig Int Largest contig size metaspades_docker String Docker image of metaspades metaspades_version String Version of metaspades midas_primary_genus String Primary genus detected by MIDAS midas_report File MIDAS report file tsv file minimap2_docker String Docker image of minimap2 minimap2_version String Version of minimap2 ncbi_scrub_docker String Docker image for NCBI's HRRT percent_coverage Float Percentage coverage of the reference genome provided percentage_mapped_reads Float Percentage of mapped reads to the assembly pilon_docker String Docker image for pilon pilon_version String Version of pilon quast_docker String Docker image of QUAST quast_version String Version of QUAST read1_clean File Clean forward reads file read1_dehosted File Dehosted forward reads file read1_mapped File Mapped forward reads to the assembly read1_unmapped File Unmapped forwards reads to the assembly read2_clean File Clean reverse reads file read2_dehosted File Dehosted reverse reads file read2_mapped File Mapped reverse reads to the assembly read2_unmapped File Unmapped reverse reads to the assembly samtools_docker String Docker image of samtools samtools_version String Version of samtools semibin_bins Array[File] Array of binned metagenomic assembled genome files semibin_docker String Docker image of semibin semibin_version String Semibin version used theiameta_illumina_pe_analysis_date String Date of analysis theiameta_illumina_pe_version String Version of workflow trimmomatic_docker String Docker image of trimmomatic trimmomatic_version String Version of trimmomatic used"},{"location":"workflows/genomic_characterization/theiameta/#references","title":"References","text":"Human read removal tool (HRRT): https://github.com/ncbi/sra-human-scrubber
Trimmomatic: Anthony M. Bolger\u00a0and others, Trimmomatic: a flexible trimmer for Illumina sequence data,\u00a0Bioinformatics, Volume 30, Issue 15, August 2014, Pages 2114\u20132120,\u00a0https://doi.org/10.1093/bioinformatics/btu170
Fastq-Scan: https://github.com/rpetit3/fastq-scan
metaSPAdes: Sergey Nurk and others, metaSPAdes: a new versatile metagenomic assembler,\u00a0Genome Res. 2017 May; 27(5): 824\u2013834.,\u00a0https://doi.org/10.1101%2Fgr.213959.116
Pilon: Bruce J. Walker and others. Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement. Plos One. November 19, 2014. https://doi.org/10.1371/journal.pone.0112963
Minimap2: Heng Li, Minimap2: pairwise alignment for nucleotide sequences,\u00a0Bioinformatics, Volume 34, Issue 18, September 2018, Pages 3094\u20133100,\u00a0https://doi.org/10.1093/bioinformatics/bty191
QUAST: Alexey Gurevich\u00a0and others, QUAST: quality assessment tool for genome assemblies,\u00a0Bioinformatics, Volume 29, Issue 8, April 2013, Pages 1072\u20131075,\u00a0https://doi.org/10.1093/bioinformatics/btt086
Samtools: Li, Heng, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, Richard Durbin, and 1000 Genome Project Data Processing Subgroup. 2009. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25(16): 2078-2079.
Bcftools: Petr Danecek, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, Thomas Keane, Shane A McCarthy, Robert M Davies, Heng Li. Twelve years of SAMtools and BCFtools. GigaScience, Volume 10, Issue 2, February 2021, giab008, https://doi.org/10.1093/gigascience/giab008
Semibin2: Shaojun Pan, Xing-Ming Zhao, Luis Pedro Coelho, SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing,\u00a0Bioinformatics, Volume 39, Issue Supplement_1, June 2023, Pages i21\u2013i29,\u00a0https://doi.org/10.1093/bioinformatics/btad209
"},{"location":"workflows/genomic_characterization/theiaprok/","title":"TheiaProk Workflow Series","text":""},{"location":"workflows/genomic_characterization/theiaprok/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Bacteria PHB v2.2.0 Yes, some optional features incompatible Sample-level"},{"location":"workflows/genomic_characterization/theiaprok/#theiaprok-workflows","title":"TheiaProk Workflows","text":"The TheiaProk workflows are for the assembly, quality assessment, and characterization of bacterial genomes. There are currently four TheiaProk workflows designed to accommodate different kinds of input data:
TheiaProk Workflow Diagram
All input reads are processed through \"core tasks\" in the TheiaProk Illumina and ONT workflows. These undertake read trimming and assembly appropriate to the input data type. TheiaProk workflows subsequently launch default genome characterization modules for quality assessment, species identification, antimicrobial resistance gene detection, sequence typing, and more. For some taxa identified, \"taxa-specific sub-workflows\" will be automatically activated, undertaking additional taxa-specific characterization steps. When setting up each workflow, users may choose to use \"optional tasks\" as additions or alternatives to tasks run in the workflow by default.
"},{"location":"workflows/genomic_characterization/theiaprok/#inputs","title":"Inputs","text":"TheiaProk_Illumina_PE Input Read DataThe TheiaProk_Illumina_PE workflow takes in Illumina paired-end read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip before Terra uploads to minimize data upload time.
By default, the workflow anticipates\u00a02 x 150bp\u00a0reads (i.e. the input reads were generated using a 300-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate shorter read data, such as the 2 x 75bp reads generated using a 150-cycle sequencing kit.
TheiaProk_Illumina_SE takes in Illumina single-end reads. Read file names should end with .fastq or .fq, with the optional addition of .gz. Theiagen highly recommends zipping files with gzip before uploading to Terra to minimize data upload time & save on storage costs.
By default, the workflow anticipates 1 x 35 bp reads (i.e. the input reads were generated using a 70-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate longer read data.
The TheiaProk_ONT workflow takes in base-called ONT read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip before uploading to Terra to minimize data upload time.
The ONT sequencing kit and base-calling approach can produce substantial variability in the amount and quality of read data. Genome assemblies produced by the TheiaProk_ONT workflow must be quality assessed before reporting results.
TheiaProk_FASTA Input Assembly DataThe TheiaProk_FASTA workflow takes in assembly files in FASTA format.
Terra Task name Variable Type Description Default value Terra Status Workflow *workflow name samplename String Name of sample to be analyzed Required FASTA, ONT, PE, SE theiaprok_fasta assembly_fasta File Assembly file in fasta format Required FASTA theiaprok_illumina_pe read1 File Illumina forward read file in FASTQ file format (compression optional) Required PE theiaprok_illumina_pe read2 File Illumina reverse read file in FASTQ file format (compression optional) Required PE theiaprok_illumina_se read1 File Illumina forward read file in FASTQ file format (compression optional) Required SE theiaprok_ont read1 File Base-called ONT read file in FASTQ file format (compression optional) Required ONT *workflow name abricate_db String Database to use with the Abricate tool. Options: NCBI, CARD, ARG-ANNOT, Resfinder, MEGARES, EcOH, PlasmidFinder, Ecoli_VF and VFDB vfdb Optional FASTA, ONT, PE, SE *workflow name call_abricate Boolean Set to true to enable the Abricate task FALSE Optional FASTA, ONT, PE, SE *workflow name call_ani Boolean Set to true to enable the ANI task FALSE Optional FASTA, ONT, PE, SE *workflow name call_kmerfinder Boolean Set to true to enable the kmerfinder task FALSE Optional FASTA, ONT, PE, SE *workflow name call_plasmidfinder Boolean Set to true to enable the plasmidfinder task TRUE Optional FASTA, ONT, PE, SE *workflow name call_resfinder Boolean Set to true to enable the ResFinder task FALSE Optional FASTA, ONT, PE, SE *workflow name city String Will be used in the \"city\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name collection_date String Will be used in the \"collection_date\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name county String Will be used in the \"county\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name expected_taxon String If provided, this input will override the taxonomic assignment made by GAMBIT and launch the relevant taxon-specific submodules. It will also modify the organism flag used by AMRFinderPlus. Example format: \"Salmonella enterica\" Optional FASTA, ONT, PE, SE *workflow name genome_annotation String If set to \"bakta\", TheiaProk will use Bakta rather than Prokka to annotate the genome prokka Optional FASTA, ONT, PE, SE *workflow name genome_length Int User-specified expected genome length to be used in genome statistics calculations Optional ONT, PE, SE *workflow name max_genome_length Int Maximum genome length able to pass read screening. For TheiaProk_ONT, screening using max_genome_length is skipped by default. 18040666 Optional ONT, PE, SE *workflow name min_basepairs Int Minimum number of base pairs able to pass read screening 2241820 Optional ONT, PE, SE *workflow name min_coverage Int Minimum genome coverage able to pass read screening. Screening using min_coverage is skipped by default. 5 Optional ONT *workflow name min_coverage Int Minimum genome coverage able to pass read screening 10 Optional PE, SE *workflow name min_genome_length Int Minimum genome length able to pass read screening. For TheiaProk_ONT, screening using min_genome_length is skipped by default. 100000 Optional ONT, PE, SE *workflow name min_proportion Int Minimum proportion of total reads in each read file to pass read screening 40 Optional PE *workflow name min_reads Int Minimum number of reads to pass read screening 5000 Optional ONT *workflow name min_reads Int Minimum number of reads to pass read screening 7472 Optional PE, SE *workflow name originating_lab String Will be used in the \"originating_lab\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name perform_characterization Boolean Set to \"false\" if you want to only generate an assembly and relevant QC metrics and skip all characterization tasks TRUE Optional FASTA, ONT, PE, SE *workflow name qc_check_table File TSV value with taxons for rows and QC values for columns; internal cells represent user-determined QC thresholds; if provided, turns on the QC Check task.Click on the variable name for an example QC Check table Optional FASTA, ONT, PE, SE *workflow name run_id String Will be used in the \"run_id\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name seq_method String Will be used in the \"seq_id\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name skip_mash Boolean If true, skips estimation of genome size and coverage in read screening steps. As a result, providing true also prevents screening using these parameters. TRUE Optional ONT, SE *workflow name skip_screen Boolean Option to skip the read screening prior to analysis FALSE Optional ONT, PE, SE *workflow name taxon_tables File File indicating data table names to copy samples of a particular taxon to Optional FASTA, ONT, PE, SE *workflow name terra_project String The name of the Terra Project where you want the taxon tables written to Optional FASTA, ONT, PE, SE *workflow name terra_workspace String The name of the Terra Workspace where you want the taxon tables written to Optional FASTA, ONT, PE, SE *workflow name trim_min_length Int Specifies minimum length of each read after trimming to be kept 25 Optional SE *workflow name trim_min_length Int Specifies minimum length of each read after trimming to be kept 75 Optional PE *workflow name trim_quality_min_score Int Specifies the minimum average quality of bases in a sliding window to be kept 20 Optional PE *workflow name trim_quality_trim_score Int Specifies the average quality of bases in a sliding window to be kept 30 Optional SE *workflow name trim_window_size Int Specifies window size for trimming (the number of bases to average the quality across) 4 Optional SE *workflow name trim_window_size Int Specifies window size for trimming (the number of bases to average the quality across) 4 Optional PE *workflow name zip String Will be used in the \"zip\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE abricate cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE abricate disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE abricate docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/abricate:1.0.1-abaum-plasmid Optional FASTA, ONT, PE, SE abricate memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE abricate mincov Int Minimum DNA %coverage for the Abricate task 80 Optional FASTA, ONT, PE, SE abricate minid Int Minimum DNA %identity for the Abricate task 80 Optional FASTA, ONT, PE, SE amrfinderplus_task cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE amrfinderplus_task detailed_drug_class Boolean If set to true, amrfinderplus_amr_classes and amrfinderplus_amr_subclasses outputs will be created FALSE Optional FASTA, ONT, PE, SE amrfinderplus_task disk_size Boolean Amount of storage (in GB) to allocate to the AMRFinderPlus task 50 Optional FASTA, ONT, PE, SE amrfinderplus_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ncbi-amrfinderplus:3.12.8-2024-07-22.1 Optional FASTA, ONT, PE, SE amrfinderplus_task hide_point_mutations Boolean If set to true, point mutations are not reported FALSE Optional FASTA, ONT, PE, SE amrfinderplus_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE amrfinderplus_task mincov Float Minimum proportion of reference gene covered for a BLAST-based hit (Methods BLAST or PARTIAL).\" Attribute should be a float ranging from 0-1, such as 0.6 (equal to 60% coverage) 0.5 Optional FASTA, ONT, PE, SE amrfinderplus_task minid Float \"Minimum identity for a blast-based hit hit (Methods BLAST or PARTIAL). -1 means use a curated threshold if it exists and 0.9 otherwise. Setting this value to something other than -1 will override any curated similarity cutoffs.\" Attribute should be a float ranging from 0-1, such as 0.95 (equal to 95% identity) 0.9 Optional FASTA, ONT, PE, SE amrfinderplus_task separate_betalactam_genes Boolean Report beta-Lactam AMR genes separated out by all beta-lactam and the respective beta-lactam subclasses FALSE Optional FASTA, ONT, PE, SE ani ani_threshold Float ANI value threshold must be surpassed in order to output the ani_top_species_match. If a genome does not surpass this threshold (and the percent_bases_aligned_threshold) then the ani_top_species_match output String will show a warning instead of a genus & species. 80 Optional FASTA, ONT, PE, SE ani cpu Int Number of CPUs to allocate to the task 4 Optional FASTA, ONT, PE, SE ani disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE ani docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/mummer:4.0.0-rgdv2 Optional FASTA, ONT, PE, SE ani mash_filter Float Mash distance threshold over which ANI is not calculated 0.9 Optional FASTA, ONT, PE, SE ani memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE ani percent_bases_aligned_threshold Float Threshold regarding the proportion of bases aligned between the query genome and reference genome. If a genome does not surpass this threshold (and the ani_threshold) then the ani_top_species_match output String will show a warning instead of a genus & species. 70 Optional FASTA, ONT, PE, SE ani ref_genome File If not set, uses all 43 genomes in RGDv2 Optional FASTA, ONT, PE, SE bakta bakta_db File Database of reference annotations (seehttps://github.com/oschwengers/bakta#database) gs://theiagen-public-files-rp/terra/theiaprok-files/bakta_db_2022-08-29.tar.gz Optional FASTA, ONT, PE, SE bakta bakta_opts String Parameters to pass to bakta from https://github.com/oschwengers/bakta#usage Optional FASTA, ONT, PE, SE bakta compliant Boolean If true, forces Genbank/ENA/DDJB compliance FALSE Optional FASTA, ONT, PE, SE bakta cpu Int Number of CPUs to allocate to the task 8 Optional FASTA, ONT, PE, SE bakta disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE bakta docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/bakta:1.5.1--pyhdfd78af_0 Optional FASTA, ONT, PE, SE bakta memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional FASTA, ONT, PE, SE bakta prodigal_tf File Prodigal training file to use for CDS prediction by bakta Optional FASTA, ONT, PE, SE bakta proteins Boolean FALSE Optional FASTA, ONT, PE, SE busco cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE busco disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE busco docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/ezlabgva/busco:v5.7.1_cv1 Optional FASTA, ONT, PE, SE busco eukaryote Boolean Assesses eukaryotic organisms, rather than prokaryotic organisms FALSE Optional FASTA, ONT, PE, SE busco memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE cg_pipeline_clean cg_pipe_opts String Options to pass to CG-Pipeline for clean read assessment --fast Optional PE, SE cg_pipeline_clean cpu Int Number of CPUs to allocate to the task 4 Optional PE, SE cg_pipeline_clean disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE, SE cg_pipeline_clean docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/lyveset:1.1.4f Optional PE, SE cg_pipeline_clean memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE cg_pipeline_clean read2 File Internal component, do not modify Do not modify, Optional SE cg_pipeline_raw cg_pipe_opts String Options to pass to CG-Pipeline for raw read assessment --fast Optional PE, SE cg_pipeline_raw cpu Int Number of CPUs to allocate to the task 4 Optional PE, SE cg_pipeline_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE, SE cg_pipeline_raw docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/lyveset:1.1.4f Optional PE, SE cg_pipeline_raw memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE cg_pipeline_raw read2 File Internal component, do not modify Do not modify, Optional SE clean_check_reads cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE, SE clean_check_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE, SE clean_check_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/bactopia/gather_samples:2.0.2 Optional ONT, PE, SE clean_check_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE, SE clean_check_reads organism String Internal component, do not modify Do not modify, Optional ONT, PE, SE clean_check_reads workflow_series String Internal component, do not modify Do not modify, Optional ONT, PE, SE dragonflye assembler String The assembler to use in dragonflye. Three options: raven, miniasm, flye flye Optional ONT dragonflye assembler_options String Enables extra assembler options in quote Optional ONT dragonflye cpu Int Number of CPUs to allocate to the task 4 Optional ONT dragonflye disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT dragonflye docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/dragonflye:1.0.14--hdfd78af_0 Optional ONT dragonflye illumina_polishing_rounds Int Number of polishing rounds to conduct with Illumina data 1 Optional ONT dragonflye illumina_read1 File If Illumina reads are provided, Dragonflye will perform Illumina polishing Optional ONT dragonflye illumina_read2 File If Illumina reads are provided, Dragonflye will perform Illumina polishing Optional ONT dragonflye medaka_model String The model of medaka to use for assembly r941_min_hac_g507 Optional ONT dragonflye memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional ONT dragonflye polishing_rounds Int The number of polishing rounds to conduct (without Illumina) 1 Optional ONT dragonflye use_pilon_illumina_polisher Boolean Set to true to use Pilon to polish Illumina reads FALSE Optional ONT dragonflye use_racon Boolean Set to true to use Racon to polish instead of Medaka FALSE Optional ONT export_taxon_tables asembly_fasta File Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables bbduk_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT export_taxon_tables cg_pipeline_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT export_taxon_tables cg_pipeline_report_clean File Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables cg_pipeline_report_raw File Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables combined_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables combined_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables combined_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables combined_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables contigs_gfa File Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables cpu Int Number of CPUs to allocate to the task 1 Optional FASTA, ONT, PE, SE export_taxon_tables disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE export_taxon_tables docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16 Optional FASTA, ONT, PE, SE export_taxon_tables dragonflye_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables emmtypingtool_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT, SE export_taxon_tables emmtypingtool_emm_type String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables emmtypingtool_results_xml File Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables emmtypingtool_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables est_coverage_clean Float Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables est_coverage_raw Float Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables fastp_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables fastq_scan_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables hicap_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT export_taxon_tables hicap_genes String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables hicap_results_tsv File Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables hicap_serotype String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables hicap_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables kmc_est_genome_length String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables kmc_kmer_stats File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables kmc_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables kraken2_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT, PE export_taxon_tables kraken2_report String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables kraken2_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE export_taxon_tables midas_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT export_taxon_tables midas_primary_genus String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables midas_report File Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables midas_secondary_genus String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables midas_secondary_genus_abundance Float Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables midas_secondary_genus_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables nanoplot_docker String The Docker container to use for the task Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_html_clean File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_html_raw File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_num_reads_clean1 Int Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_num_reads_raw1 Int Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_est_coverage_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_est_coverage_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_median_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_median_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_median_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_median_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_n50_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_n50_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_stdev_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_stdev_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_tsv_clean File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_tsv_raw File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoq_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables num_reads_clean_pairs String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables num_reads_clean1 Int Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables num_reads_clean2 Int Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables num_reads_raw_pairs String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables num_reads_raw1 Int Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables num_reads_raw2 Int Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables r1_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables r1_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables r1_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables r1_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables r2_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables r2_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables rasusa_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables read1 File Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables read1_clean File Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables read2 File Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables read2_clean File Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables seroba_ariba_identity String Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables seroba_ariba_serotype String Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables seroba_details File Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables seroba_docker String The Docker container to use for the task Do not modify, Optional ONT, SE export_taxon_tables seroba_serotype String Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables seroba_version String Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables shigeifinder_cluster_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_docker_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_H_antigen_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_ipaH_presence_absence_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_notes_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_num_virulence_plasmid_genes String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_O_antigen_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_report_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_serotype_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_version_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shovill_pe_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables shovill_se_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables srst2_vibrio_biotype String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_ctxA String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_detailed_tsv String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_ompW String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_serogroup String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_toxR String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables theiaprok_fasta_analysis_date String Internal component, do not modify Do not modify, Optional ONT, PE, SE export_taxon_tables theiaprok_fasta_version String Internal component, do not modify Do not modify, Optional ONT, PE, SE export_taxon_tables theiaprok_illumina_pe_analysis_date String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables theiaprok_illumina_pe_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables theiaprok_illumina_se_analysis_date String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables theiaprok_illumina_se_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables theiaprok_ont_analysis_date String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables theiaprok_ont_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables tiptoft_plasmid_replicon_fastq File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables tiptoft_plasmid_replicon_genes String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables tiptoft_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables trimmomatic_version String Internal component, do not modify Do not modify, Optional FASTA, ONT gambit cpu Int Number of CPUs to allocate to the task 8 Optional FASTA, ONT, PE, SE gambit disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE gambit docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/gambit:1.0.0 Optional FASTA, ONT, PE, SE gambit gambit_db_genomes File User-provided database of assembled query genomes; requires complementary signatures file. If not provided, uses default database, \"/gambit-db\" gs://gambit-databases-rp/2.0.0/gambit-metadata-2.0.0-20240628.gdb Optional FASTA, ONT, PE, SE gambit gambit_db_signatures File User-provided signatures file; requires complementary genomes file. If not specified, the file from the docker container will be used. gs://gambit-databases-rp/2.0.0/gambit-signatures-2.0.0-20240628.gs Optional FASTA, ONT, PE, SE gambit memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional FASTA, ONT, PE, SE kmerfinder cpu Int Number of CPUs to allocate to the task 4 Optional FASTA, ONT, PE, SE kmerfinder disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE kmerfinder docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/kmerfinder:3.0.2--hdfd78af_0 Optional FASTA, ONT, PE, SE kmerfinder kmerfinder_args String Kmerfinder additional arguments Optional FASTA, ONT, PE, SE kmerfinder kmerfinder_db String Bacterial database for KmerFinder gs://theiagen-public-files-rp/terra/theiaprok-files/kmerfinder_bacteria_20230911.tar.gz Optional FASTA, ONT, PE, SE kmerfinder memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional FASTA, ONT, PE, SE merlin_magic abricate_abaum_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/abricate:1.0.1-abaum-plasmid Optional FASTA, ONT, PE, SE merlin_magic abricate_abaum_mincov Int Minimum DNA percent coverage Optional FASTA, ONT, PE, SE merlin_magic abricate_abaum_minid Int Minimum DNA percent identity; set to 95 because there is a strict threshold of 95% identity for typing purposes 95 Optional FASTA, ONT, PE, SE merlin_magic abricate_vibrio_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/abricate:1.0.1-abaum-plasmid Optional FASTA, ONT, PE, SE merlin_magic abricate_vibrio_mincov Int Minimum DNA percent coverage 80 Optional FASTA, ONT, PE, SE merlin_magic abricate_vibrio_minid Int Minimum DNA percent identity 80 Optional FASTA, ONT, PE, SE merlin_magic agrvate_agr_typing_only Boolean Set to true to skip agr operon extraction and frameshift detection False Optional FASTA, ONT, PE, SE merlin_magic agrvate_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/agrvate:1.0.2--hdfd78af_0 Optional FASTA, ONT, PE, SE merlin_magic assembly_only Boolean Internal component, do not modify Do not modify, Optional ONT, PE, SE merlin_magic call_poppunk Boolean If \"true\", runs PopPUNK for GPSC cluster designation for S. pneumoniae TRUE Optional FASTA, ONT, PE, SE merlin_magic call_shigeifinder_reads_input Boolean If set to \"true\", the ShigEiFinder task will run again but using read files as input instead of the assembly file. Input is shown but not used for TheiaProk_FASTA. FALSE Optional FASTA, ONT, PE, SE merlin_magic call_stxtyper Boolean If set to \"true\", the StxTyper task will run on all samples regardless of thegambit_predicted_taxon output. Useful if you suspect a non-E.coli or non-Shigella sample contains stx genes. FALSE Optional FASTA, ONT, PE, SE merlin_magic cauris_cladetyper_docker_image String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_kmer_size Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade1 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade1_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade2 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade2_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade3 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade3_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade4 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade4_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade5 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade5_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic clockwork_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/cdcgov/varpipe_wgs_with_refs:2bc7234074bd53d9e92a1048b0485763cd9bbf6f4d12d5a1cc82bfec8ca7d75e Optional FASTA, ONT, PE, SE merlin_magic ectyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/ectyper:1.0.0--pyhdfd78af_1 Optional FASTA, ONT, PE, SE merlin_magic ectyper_hpcov Int Minumum percent coverage required for an H antigen allele match 50 Optional FASTA, ONT, PE, SE merlin_magic ectyper_hpid Int Percent identity required for an H antigen allele match 95 Optional FASTA, ONT, PE, SE merlin_magic ectyper_opcov Int Minumum percent coverage required for an O antigen allele match 90 Optional FASTA, ONT, PE, SE merlin_magic ectyper_opid Int Percent identity required for an O antigen allele match 90 Optional FASTA, ONT, PE, SE merlin_magic ectyper_print_alleles Boolean Set to true to print the allele sequences as the final column False Optional FASTA, ONT, PE, SE merlin_magic ectyper_verify Boolean Set to true to enable E. coli species verification False Optional FASTA, ONT, PE, SE merlin_magic emmtypingtool_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/emmtypingtool:0.0.1 Optional FASTA, ONT, PE, SE merlin_magic genotyphi_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.11.0 Optional FASTA, ONT, PE, SE merlin_magic hicap_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/hicap:1.0.3--py_0 Optional FASTA, ONT, PE, SE merlin_magic kaptive_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/kaptive:2.0.3 Optional FASTA, ONT, PE, SE merlin_magic kaptive_low_gene_id Float Percent identity threshold for what counts as a low identity match in the gene BLAST search 95 Optional FASTA, ONT, PE, SE merlin_magic kaptive_min_coverage Float Minimum required percent identity for the gene BLAST search via tBLASTn 80 Optional FASTA, ONT, PE, SE merlin_magic kaptive_min_identity Float Minimum required percent coverage for the gene BLAST search via tBLASTn 90 Optional FASTA, ONT, PE, SE merlin_magic kaptive_start_end_margin Int Determines flexibility in identifying the start and end of a locus - if this value is 10, a locus match that is missing the first 8 base pairs will still count as capturing the start of the locus 10 Optional FASTA, ONT, PE, SE merlin_magic kleborate_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/kleborate:2.2.0 Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_coverage Float Minimum alignment percent coverage for main results 80 Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_identity Float Minimum alignment percent identity for main results 90 Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_kaptive_confidence String {None,Low,Good,High,Very_high,Perfect} Minimum Kaptive confidence to call K/O loci - confidence levels below this will be reported as unknown Good Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_spurious_coverage Float Minimum alignment percent coverage for spurious results 40 Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_spurious_identity Float Minimum alignment percent identity for spurious results 80 Optional FASTA, ONT, PE, SE merlin_magic kleborate_skip_kaptive Boolean Equivalent to --kaptive_k --kaptive_ False Optional FASTA, ONT, PE, SE merlin_magic kleborate_skip_resistance Boolean Set to true to turn on resistance genes screening (default: no resistance gene screening) False Optional FASTA, ONT, PE, SE merlin_magic legsta_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/legsta:0.5.1--hdfd78af_2 Optional FASTA, ONT, PE, SE merlin_magic lissero_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/lissero:0.4.9--py_0 Optional FASTA, ONT, PE, SE merlin_magic lissero_min_cov Float Minimum coverage of the gene to accept a match 95 Optional FASTA, ONT, PE, SE merlin_magic lissero_min_id Float Minimum percent identity to accept a match 95 Optional FASTA, ONT, PE, SE merlin_magic meningotype_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/meningotype:0.8.5--pyhdfd78af_0 Optional FASTA, ONT, PE, SE merlin_magic ngmaster_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ngmaster:1.0.0 Optional FASTA, ONT, PE, SE merlin_magic ont_data Boolean Internal component, do not modify Do not modify, Optional FASTA, PE, SE merlin_magic paired_end Boolean Internal component, do not modify Do not modify, Optional ONT, PE merlin_magic pasty_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pasty:1.0.3 Optional FASTA, ONT, PE, SE merlin_magic pasty_min_coverage Int Minimum coverage of a O-antigen to be considered for serogrouping by pasty 95 Optional FASTA, ONT, PE, SE merlin_magic pasty_min_pident Int Minimum percent identity for a blast hit to be considered for serogrouping 95 Optional FASTA, ONT, PE, SE merlin_magic pbptyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pbptyper:1.0.4 Optional FASTA, ONT, PE, SE merlin_magic pbptyper_min_coverage Int Minimum percent coverage to count a hit 90 Optional FASTA, ONT, PE, SE merlin_magic pbptyper_min_pident Int Minimum percent identity to count a hit 90 Optional FASTA, ONT, PE, SE merlin_magic poppunk_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/poppunk:2.4.0 Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_clusters_csv File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_clusters.csv Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_dists_npy File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.dists.npy Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_dists_pkl File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.dists.pkl Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_external_clusters_csv File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_external_clusters.csv Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_fit_npz File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_fit.npz Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_fit_pkl File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_fit.pkl Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_graph_gt File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_graph.gt Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_h5 File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.h5 Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_qcreport_txt File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_qcreport.txt Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.refs Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs_dists_npy File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.refs.dists.npy Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs_dists_pkl File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.refs.dists.pkl Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs_graph_gt File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6refs_graph.gt Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs_h5 File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.refs.h5 Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_unword_clusters_csv File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_unword_clusters.csv Optional FASTA, ONT, PE, SE merlin_magic read1 File Internal component, do not modify Do not modify, Optional FASTA merlin_magic read2 File Internal component, do not modify Do not modify, Optional FASTA, ONT, SE merlin_magic seqsero2_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/seqsero2:1.2.1 Optional FASTA, ONT, PE, SE merlin_magic seroba_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/seroba:1.0.2 Optional FASTA, ONT, PE, SE merlin_magic serotypefinder_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/serotypefinder:2.0.1 Optional FASTA, ONT, PE, SE merlin_magic shigatyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/shigatyper:2.0.5 Optional FASTA, ONT, PE, SE merlin_magic shigeifinder_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/shigeifinder:1.3.5 Optional FASTA, ONT, PE, SE merlin_magic sistr_cpu Int The number of CPU cores to allocate for the task 8 Optional FASTA, ONT, PE, SE merlin_magic sistr_disk_size Int The disk size (in GB) to allocate for the task 100 Optional FASTA, ONT, PE, SE merlin_magic sistr_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/sistr_cmd:1.1.1--pyh864c0ab_2 Optional FASTA, ONT, PE, SE merlin_magic sistr_memory Int The amount of memory (in GB) to allocate for the task. 32 Optional FASTA, ONT, PE, SE merlin_magic sistr_use_full_cgmlst_db Boolean Set to true to use the full set of cgMLST alleles which can include highly similar alleles. By default the smaller \"centroid\" alleles or representative alleles are used for each marker False Optional FASTA, ONT, PE, SE merlin_magic snippy_base_quality Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_gene_query_docker_image String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_map_qual Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_maxsoft Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_min_coverage Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_min_frac Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_min_quality Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_query_gene String Internal component, do not modify Do not modify, Optional FASTA, PE, SE merlin_magic snippy_reference_afumigatus File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_reference_calbicans File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_reference_cryptoneo File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_variants_docker_image String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic sonneityping_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional FASTA, ONT, PE, SE merlin_magic sonneityping_mykrobe_opts String Additional options for mykrobe in sonneityping Optional FASTA, ONT, PE, SE merlin_magic spatyper_do_enrich Boolean Set to true to enable PCR product enrichment False Optional FASTA, ONT, PE, SE merlin_magic spatyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/spatyper:0.3.3--pyhdfd78af_3 Optional FASTA, ONT, PE, SE merlin_magic srst2_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/srst2:0.2.0-vcholerae Optional FASTA, ONT, PE, SE merlin_magic srst2_gene_max_mismatch Int Maximum number of mismatches for SRST2 to call a gene as present 2000 Optional FASTA, ONT, PE, SE merlin_magic srst2_max_divergence Int Maximum divergence, in percentage, for SRST2 to call a gene as present 20 Optional FASTA, ONT, PE, SE merlin_magic srst2_min_cov Int Minimum breadth of coverage for SRST2 to call a gene as present 80 Optional FASTA, ONT, PE, SE merlin_magic srst2_min_depth Int Minimum depth of coverage for SRST2 to call a gene as present 5 Optional FASTA, ONT, PE, SE merlin_magic srst2_min_edge_depth Int Minimum edge depth for SRST2 to call a gene as present 2 Optional FASTA, ONT, PE, SE merlin_magic stxtyper_cpu Int The number of CPU cores to allocate for the task. 1 Optional FASTA, ONT, PE, SE merlin_magic stxtyper_disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional FASTA, ONT, PE, SE merlin_magic stxtyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/stxtyper:1.0.24 Optional FASTA, ONT, PE, SE merlin_magic stxtyper_enable_debug Boolean When enabled, additional messages are printed and files in $TMPDIR are not removed after running FALSE Optional FASTA, ONT, PE, SE merlin_magic stxtyper_memory Int Amount of memory (in GB) to allocate to the task 4 Optional FASTA, ONT, PE, SE merlin_magic staphopia_sccmec_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/staphopia-sccmec:1.0.0--hdfd78af_0 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_coverage_regions_bed File A bed file that lists the regions to be considered for QC Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_coverage_threshold Int The minimum coverage for a region to pass QC in tbp_parser 100 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_debug Boolean Activate the debug mode on tbp_parser; increases logging outputs FALSE Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/tbp-parser:1.6.0 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/tbp-parser:1.4.0 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_min_depth Int Minimum depth for a variant to pass QC in tbp_parser 10 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_min_frequency Int The minimum frequency for a mutation to pass QC 0.1 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_min_read_support Int The minimum read support for a mutation to pass QC 10 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_operator String Fills the \"operator\" field in the tbp_parser output files Operator not provided Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_output_seq_method_type String Fills out the \"seq_method\" field in the tbp_parser output files Sequencing method not provided Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_additional_outputs Boolean If set to \"true\", activates the tbp_parser module and results in more outputs, including\u00a0tbp_parser_looker_report_csv, tbp_parser_laboratorian_report_csv, tbp_parser_lims_report_csv, tbp_parser_coverage_report, and tbp_parser_genome_percent_coverage FALSE Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_cov_frac_threshold Int A cutoff used to calculate the fraction of the region covered by \u2264 this value 1 Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_custom_db File TBProfiler uses by default the TBDB database; if you have a custom database you wish to use, you must provide a custom database in this field and set tbprofiler_run_custom_db to true Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/tbprofiler:4.4.2 Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_mapper String The mapping tool used in TBProfiler to align the reads to the reference genome; see TBProfiler\u2019s original documentation for available options. bwa Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_min_af Float The minimum allele frequency to call a variant 0.1 Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_min_af_pred Float The minimum allele frequency to use a variant for resistance prediction 0.1 Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_min_depth Int The minimum depth for a variant to be called. 10 Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_run_custom_db Boolean TBProfiler uses by default the TBDB database; if you have a custom database you wish to use, you must set this value to true and provide a custom database in the tbprofiler_custom_db field FALSE Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_variant_caller String Select a different variant caller for TBProfiler to use by writing it in this block; see TBProfiler\u2019s original documentation for available options. freebayes Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_variant_calling_params String Enter additional variant calling parameters in this free text input to customize how the variant caller works in TBProfiler None Optional FASTA, ONT, PE, SE merlin_magic theiaeuk Boolean Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic virulencefinder_coverage_threshold Float The threshold for minimum coverage Optional FASTA, ONT, PE, SE merlin_magic virulencefinder_database String The specific database to use virulence_ecoli Optional FASTA, ONT, PE, SE merlin_magic virulencefinder_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/virulencefinder:2.0.4 Optional FASTA, ONT, PE, SE merlin_magic virulencefinder_identity_threshold Float The threshold for minimum blast identity Optional FASTA, ONT, PE, SE nanoplot_clean cpu Int Number of CPUs to allocate to the task 4 Optional ONT nanoplot_clean disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT nanoplot_clean docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0 Optional ONT nanoplot_clean max_length Int Maximum read length for nanoplot 100000 Optional ONT nanoplot_clean memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT nanoplot_raw cpu Int Number of CPUs to allocate to the task 4 Optional ONT nanoplot_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT nanoplot_raw docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0 Optional ONT nanoplot_raw max_length Int Maximum read length for nanoplot 100000 Optional ONT nanoplot_raw memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT plasmidfinder cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE plasmidfinder database String User-specified database Optional FASTA, ONT, PE, SE plasmidfinder database_path String Path to user-specified database Optional FASTA, ONT, PE, SE plasmidfinder disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional FASTA, ONT, PE, SE plasmidfinder docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/plasmidfinder:2.1.6 Optional FASTA, ONT, PE, SE plasmidfinder memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE plasmidfinder method_path String Path to files for a user-specified method to use (blast or kma) Optional FASTA, ONT, PE, SE plasmidfinder min_cov Float Threshold for minimum coverage, default threshold from PlasmidFinder CLI tool is used (0.60) 0.6 Optional FASTA, ONT, PE, SE plasmidfinder threshold Float Threshold for mininum blast identity, default threshold from PlasmidFinder CLI tool is used (0.90). This default differs from the default of the PlasmidFinder webtool (0.95) 0.9 Optional FASTA, ONT, PE, SE prokka compliant Boolean Forces Genbank/ENA/DDJB compliant headers in Prokka output files TRUE Optional FASTA, ONT, PE, SE prokka cpu Int Number of CPUs to allocate to the task 8 Optional FASTA, ONT, PE, SE prokka disk_size String Amount of storage (in GB) to allocate to the PlasmidFinder task 100 Optional FASTA, ONT, PE, SE prokka docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/prokka:1.14.5 Optional FASTA, ONT, PE, SE prokka memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional FASTA, ONT, PE, SE prokka prodigal_tf File https://github.com/tseemann/prokka#option---prodigaltf Optional FASTA, ONT, PE, SE prokka prokka_arguments String Any additional https://github.com/tseemann/prokka#command-line-options Optional FASTA, ONT, PE, SE prokka proteins Boolean FASTA file of trusted proteins for Prokka to first use for annotations FALSE Optional FASTA, ONT, PE, SE qc_check_task assembly_length_unambiguous Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task assembly_mean_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task combined_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task combined_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task combined_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task combined_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task cpu Int Number of CPUs to allocate to the task 4 Optional FASTA, ONT, PE, SE qc_check_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE qc_check_task docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16\" Optional FASTA, ONT, PE, SE qc_check_task est_coverage_clean Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task est_coverage_raw Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task kraken_human Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_human_dehosted Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_sc2 Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_sc2_dehosted Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_target_organism Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_target_organism_dehosted Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task meanbaseq_trim String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE qc_check_task midas_secondary_genus_abundance Int Internal component, do not modify Do not modify, Optional FASTA, ONT qc_check_task midas_secondary_genus_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT qc_check_task num_reads_clean1 Int Internal component, do not modify Do not modify, Optional FASTA qc_check_task num_reads_clean2 Int Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task num_reads_raw1 Int Internal component, do not modify Do not modify, Optional FASTA qc_check_task num_reads_raw2 Int Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task number_Degenerate Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task number_N Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task percent_reference_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task r1_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task r1_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task r1_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task r1_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task r2_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task r2_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task r2_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task r2_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task sc2_s_gene_mean_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task sc2_s_gene_percent_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task vadr_num_alerts String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE quast cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE quast disk_size String Amount of storage (in GB) to allocate to the Quast task 100 Optional FASTA, ONT, PE, SE quast docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/quast:5.0.2 Optional FASTA, ONT, PE, SE quast memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional FASTA, ONT, PE, SE quast min_contig_length Int Lower threshold for a contig length in bp. Shorter contigs won\u2019t be taken into account 500 Optional FASTA, ONT, PE, SE raw_check_reads cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE, SE raw_check_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE, SE raw_check_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/bactopia/gather_samples:2.0.2 Optional ONT, PE, SE raw_check_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE, SE raw_check_reads organism String Internal component, do not modify Do not modify, Optional ONT, PE, SE raw_check_reads workflow_series String Internal component, do not modify Do not modify, Optional ONT, PE, SE read_QC_trim adapters File A file containing the sequence of the adapters used during library preparation, used in the BBDuk task Optional PE, SE read_QC_trim bbduk_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE read_QC_trim call_kraken Boolean Set to true to launch Kraken2; if true, you must provide a kraken_db FALSE Optional ONT, PE, SE read_QC_trim call_midas Boolean Set to true to launch Midas TRUE Optional PE, SE read_QC_trim downsampling_coverage Float The depth to downsample to with Rasusa 150 Optional ONT read_QC_trim fastp_args String Additional arguments to pass to fastp -g -5 20 -3 20 Optional SE read_QC_trim fastp_args String Additional arguments to pass to fastp \"--detect_adapter_for_pe -g -5 20 -3 20 Optional PE read_QC_trim kraken_cpu Int Number of CPUs to allocate to the task 4 Optional ONT, PE, SE read_QC_trim kraken_db File Kraken2 database file; must be provided in call_kraken is true Optional ONT, PE, SE read_QC_trim kraken_disk_size Int GB of storage to request for VM used to run the kraken2 task. Increase this when using large (>30GB kraken2 databases such as the \"k2_standard\" database) 100 Optional ONT, PE, SE read_QC_trim kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional ONT, PE, SE read_QC_trim max_length Int Internal component, do not modify Do not modify, Optional ONT read_QC_trim midas_db File Midas database file gs://theiagen-large-public-files-rp/terra/theiaprok-files/midas/midas_db_v1.2.tar.gz Optional PE, SE read_QC_trim min_length Int Internal component, do not modify Do not modify, Optional ONT read_QC_trim phix File A file containing the phix used during Illumina sequencing; used in the BBDuk task Optional PE, SE read_QC_trim read_processing String Read trimming software to use, either \"trimmomatic\" or \"fastp\" trimmomatic Optional PE, SE read_QC_trim read_qc String Allows the user to decide between fastq_scan (default) and fastqc for the evaluation of read quality. fastq_scan Optional PE, SE read_QC_trim run_prefix String Internal component, do not modify Do not modify, Optional ONT read_QC_trim target_organism String This string is searched for in the kraken2 outputs to extract the read percentage Optional ONT, PE, SE read_QC_trim trimmomatic_args String Additional arguments to pass to trimmomatic. \"-phred33\" specifies the Phred Q score encoding which is almost always phred33 with modern sequence data. -phred33 Optional PE, SE resfinder_task acquired Boolean Set to true to tell ResFinder to identify acquired resistance genes TRUE Optional FASTA, ONT, PE, SE resfinder_task call_pointfinder Boolean Set to true to enable detection of point mutations. FALSE Optional FASTA, ONT, PE, SE resfinder_task cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE resfinder_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE resfinder_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/resfinder:4.1.11 Optional FASTA, ONT, PE, SE resfinder_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE resfinder_task min_cov Float Minimum coverage breadth of a gene for it to be identified 0.5 Optional FASTA, ONT, PE, SE resfinder_task min_id Float Minimum identity for ResFinder to identify a gene 0.9 Optional FASTA, ONT, PE, SE shovill_pe assembler String Assembler to use (spades, skesa, velvet or megahit), see https://github.com/tseemann/shovill#--assembler skesa Optional PE shovill_pe assembler_options String Assembler-specific options that you might choose, see https://github.com/tseemann/shovill#--opts Optional PE shovill_pe cpu Int Number of CPUs to allocate to the task 4 Optional PE shovill_pe depth Int User specified depth of coverage for downsampling (see https://github.com/tseemann/shovill#--depth and https://github.com/tseemann/shovill#main-steps) 150 Optional PE shovill_pe disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE shovill_pe docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/shovill:1.1.0 Optional PE shovill_pe kmers String User-specified Kmer length to override choice made by Shovill, see https://github.com/tseemann/shovill#--kmers Auto Optional PE shovill_pe memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional PE shovill_pe min_contig_length Int Minimum contig length to keep in final assembly 200 Optional PE shovill_pe min_coverage Float Minimum contig coverage to keep in final assembly 2 Optional PE shovill_pe nocorr Boolean Disable correction of minor assembly errors by Shovill (see https://github.com/tseemann/shovill#main-steps) FALSE Optional PE shovill_pe noreadcorr Boolean Disable correction of sequencing errors in reads by Shovill (seehttps://github.com/tseemann/shovill#main-steps) FALSE Optional PE shovill_pe nostitch Boolean Disable read stitching by Shovill (see https://github.com/tseemann/shovill#main-steps) FALSE Optional PE shovill_pe trim Boolean Enable adaptor trimming (see https://github.com/tseemann/shovill#main-steps) FALSE Optional PE shovill_se assembler String Assembler to use (spades, skesa, velvet or megahit), see https://github.com/tseemann/shovill#--assembler skesa Optional SE shovill_se assembler_options String Assembler-specific options that you might choose, see https://github.com/tseemann/shovill#--opts Optional SE shovill_se cpu Int Number of CPUs to allocate to the task 4 Optional SE shovill_se depth Int User specified depth of coverage for downsampling (see https://github.com/tseemann/shovill#--depth and https://github.com/tseemann/shovill#main-steps) 150 Optional SE shovill_se disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE shovill_se docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/shovill:1.1.0 Optional SE shovill_se kmers String User-specified Kmer length to override choice made by Shovill, see https://github.com/tseemann/shovill#--kmers auto Optional SE shovill_se memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional SE shovill_se min_contig_length Int Minimum contig length to keep in final assembly 200 Optional SE shovill_se min_coverage Float Minimum contig coverage to keep in final assembly 2 Optional SE shovill_se nocorr Boolean Disable correction of minor assembly errors by Shovill (see https://github.com/tseemann/shovill#main-steps) FALSE Optional SE shovill_se noreadcorr Boolean Disable correction of sequencing errors in reads by Shovill (seehttps://github.com/tseemann/shovill#main-steps) FALSE Optional SE shovill_se trim Boolean Enable adaptor trimming (see https://github.com/tseemann/shovill#main-steps) FALSE Optional SE ts_mlst cpu Int Number of CPUs to allocate to the task 1 Optional FASTA, ONT, PE, SE ts_mlst disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional FASTA, ONT, PE, SE ts_mlst docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mlst:2.23.0-2024-08-01 Optional FASTA, ONT, PE, SE ts_mlst memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional FASTA, ONT, PE, SE ts_mlst mincov Float Minimum % breadth of coverage to report an MLST allele 10 Optional FASTA, ONT, PE, SE ts_mlst minid Float Minimum % identity to known MLST gene to report an MLST allele 95 Optional FASTA, ONT, PE, SE ts_mlst minscore Float Minimum https://github.com/tseemann/mlst#scoring-system to assign an MLST profile 50 Optional FASTA, ONT, PE, SE ts_mlst nopath Boolean true = use mlst --nopath. If set to false, filename paths are not stripped from FILE column in output TSV TRUE Optional FASTA, ONT, PE, SE ts_mlst scheme String Don\u2019t autodetect the MLST scheme; force this scheme on all inputs (see https://www.notion.so/TheiaProk-Workflow-Series-68c34aca2a0240ef94fef0acd33651b9?pvs=21 for accepted strings) None Optional FASTA, ONT, PE, SE version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional FASTA, ONT, PE, SE version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) FASTA, ONT, PE, SE Skip Characterization
Ever wanted to skip characterization? Now you can! Set the optional input perform_characterization to false to only generate an assembly and run assembly QC.
versioning: Version Capture for TheiaProk The versioning task captures the workflow version from the GitHub (code repository) version.
Version Capture Technical details
Links Task task_versioning.wdlscreen: Total Raw Read Quantification and Genome Size Estimation The screen task ensures the quantity of sequence data is sufficient to undertake genomic analysis. It uses fastq-scan and bash commands for quantification of reads and base pairs, and mash sketching to estimate the genome size and its coverage. At each step, the results are assessed relative to pass/fail criteria and thresholds that may be defined by optional user inputs. Samples that do not meet these criteria will not be processed further by the workflow:
min_reads.min_proportion basepairs are in either the reads1 or read2 files.min_basepairs basepairsmin_genome_size or bigger than max_genome_size.min_coverage.Read screening is undertaken on both the raw and cleaned reads. The task may be skipped by setting the skip_screen variable to true.
Default values vary between the PE and SE workflow. The rationale for these default values can be found below. If two default values are shown, the first is for Illumina workflows and the second is for ONT.
Variable Default Value Rationaleskip_screen false Set to false to avoid waste of compute resources processing insufficient data min_reads 7472 or 5000 Calculated from the minimum number of base pairs required for 20x coverage of Nasuia deltocephalinicola genome, the smallest known bacterial genome as of 2019-08-07 (112,091 bp), divided by 300 (the longest Illumina read length) or 5000 (estimate of ONT read length) min_basepairs 2241820 Should be greater than 20x coverage of Nasuia deltocephalinicola, the smallest known bacterial genome (112,091 bp) min_genome_length 100000 Based on the Nasuia deltocephalinicola genome - the smallest known bacterial genome (112,091 bp) max_genome_length 18040666 Based on the Minicystis rosea genome, the biggest known bacterial genome (16,040,666 bp), plus an additional 2 Mbp to cater for potential extra genomic material min_coverage 10 or 5 A bare-minimum average per base coverage across the genome required for genome characterization. Note, a higher per base coverage coverage would be required for high-quality phylogenetics. min_proportion 40 Neither read1 nor read2 files should have less than 40% of the total number of reads. For paired-end data only Screen Technical Details
There is a single WDL task for read screening that contains two separate sub-tasks, one used for PE data and the other for SE data. The screen task is run twice, once for raw reads and once for clean reads.
read_QC_trim: Read Quality Trimming, Adapter Removal, Quantification, and Identification read_QC_trim is a sub-workflow within TheiaMeta that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below.
Read quality trimming
Either trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.
If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:
Parameter Explanation -g enables polyG tail trimming -5 20 enables read end-trimming -3 20 enables read end-trimming --detect_adapter_for_pe enables adapter-trimming only for paired-end readsAdapter removal
The BBDuk task removes adapters from sequence reads. To do this:
Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.
Read Quantification
There are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. In TheiaProk_Illumina_PE, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.
Read Identification (optional)
The MIDAS task is for the identification of reads to detect contamination with non-target taxa. This task is optional and turned off by default. It can be used by setting the call_midas input variable to true.
The MIDAS tool was originally designed for metagenomic sequencing data but has been co-opted for use with bacterial isolate WGS methods. It can be used to detect contamination present in raw sequencing data by estimating bacterial species abundance in bacterial isolate WGS data. If a secondary genus is detected above a relative frequency of 0.01 (1%), then the sample should fail QC and be investigated further for potential contamination.
This task is similar to those used in commercial software, BioNumerics, for estimating secondary species abundance.
How are the MIDAS output columns determined?Example MIDAS report in the midas_report column:
MIDAS report column descriptions:
The value in the midas_primary_genus column is derived by ordering the rows in order of \"relative_abundance\" and identifying the genus of top species in the \"species_id\" column (Salmonella). The value in the midas_secondary_genus column is derived from the genus of the second-most prevalent genus in the \"species_id\" column (Citrobacter). The midas_secondary_genus_abundance column is the \"relative_abundance\" of the second-most prevalent genus (0.009477003). The midas_secondary_genus_coverage is the \"coverage\" of the second-most prevalent genus (0.995216227).
MIDAS Reference Database Overview
The MIDAS reference database is a comprehensive tool for identifying bacterial species in metagenomic and bacterial isolate WGS data. It includes several layers of genomic data, helping detect species abundance and potential contaminants.
Key Components of the MIDAS Database
MIDAS clusters bacterial genomes based on 96.5% sequence identity, forming over 5,950 species groups from 31,007 genomes. These groups align with the gold-standard species definition (95% ANI), ensuring highly accurate species identification.
Genomic Data Structure:
Pan-genome: The database includes clusters of non-redundant genes, with options for multi-level clustering (e.g., 99%, 95%, 90% identity), enabling MIDAS to identify gene content within strains at various clustering thresholds.
Taxonomic Annotation:
Using the Default MIDAS Database
TheiaProk uses the pre-loaded MIDAS database in Terra (see input table for current version) by default for bacterial species detection in metagenomic data, requiring no additional setup.
How to Set Up the Default MIDAS Database
Users can also build their own custom MIDAS database if they want to include specific genomes or configurations. This custom database can replace the default MIDAS database used in Terra. To build a custom MIDAS database, follow the MIDAS GitHub guide on building a custom database. Once the database is built, users can upload it to a Google Cloud Storage bucket or Terra workkspace and provide the link to the database in the midas_db input variable.
Alternatively to MIDAS, the Kraken2 task can also be turned on through setting the call_kraken input variable as true for the identification of reads to detect contamination with non-target taxa.
Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate) whole genome sequence data. A database must be provided if this optional module is activated, through the kraken_db optional input. A list of suggested databases can be found on Kraken2 standalone documentation.
read_QC_trim Technical Details
Links Sub-workflow wf_read_QC_trim_pe.wdlwf_read_QC_trim_se.wdl Tasks task_fastp.wdltask_trimmomatic.wdltask_bbduk.wdltask_fastq_scan.wdltask_midas.wdltask_kraken2.wdl Software Source Code fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2 Software Documentation fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2 Original Publication(s) Trimmomatic: a flexible trimmer for Illumina sequence datafastp: an ultra-fast all-in-one FASTQ preprocessorAn integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeographyImproved metagenomic analysis with Kraken 2CG-Pipeline: Assessment of Read Quality, and Estimation of Genome Coverage Thecg_pipeline task generates metrics about read quality and estimates the coverage of the genome using the \"run_assembly_readMetrics.pl\" script from CG-Pipeline. The genome coverage estimates are calculated using both using raw and cleaned reads, using either a user-provided genome_size or the estimated genome length generated by QUAST.
CG-Pipeline Technical Details
The cg_pipeline task is run twice in TheiaProk, once with raw reads, and once with clean reads.
shovill: De novo Assembly De Novo assembly will be undertaken only for samples that have sufficient read quantity and quality, as determined by the screen task assessment of clean reads.
In TheiaProk, assembly is performed using the Shovill pipeline. This undertakes the assembly with one of four assemblers (SKESA (default), SPAdes, Velvet, Megahit), but also performs a number of pre- and post-processing steps to improve the resulting genome assembly. Shovill uses an estimated genome size (see here). If this is not provided by the user as an optional input, Shovill will estimate the genome size using mash. Adaptor trimming can be undertaken with Shovill by setting the trim option to \"true\", but this is set to \"false\" by default as alternative adapter trimming is undertaken in the TheiaEuk workflow.
De novo assembly is the process or product of attempting to reconstruct a genome from scratch (without prior knowledge of the genome) using sequence reads. Assembly of fungal genomes from short-reads will produce multiple contigs per chromosome rather than a single contiguous sequence for each chromosome.
Shovill Technical Details
Links TheiaEuk WDL Task task_shovill.wdl Software Source Code Shovill on GitHub Software Documentation Shovill on GitHub"},{"location":"workflows/genomic_characterization/theiaprok/#ont-data-core-tasks","title":"ONT Data Core Tasks","text":"read_QC_trim_ont: Read Quality Trimming, Quantification, and Identification read_QC_trim_ont is a sub-workflow within TheiaProk_ONT that filters low-quality reads and trims low-quality regions of reads. It uses several tasks, described below.
Estimated genome length:
By default, an estimated genome length is set to 5 Mb, which is around 0.7 Mb higher than the average bacterial genome length, according to the information collated here. This estimate can be overwritten by the user, and is used by Rasusa and dragonflye.
Plotting and quantifying long-read sequencing data: nanoplot
Nanoplot is used for the determination of mean quality scores, read lengths, and number of reads. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads.
Read subsampling: Samples are automatically randomly subsampled to 150X coverage using RASUSA.
Plasmid prediction: tiptoft is used to predict plasmid sequences directly from uncorrected long-read data. Plasmids are identified using replicon sequences used for typing from PlasmidFinder.
Read filtering: Reads are filtered by length and quality using nanoq. By default, sequences with less than 500 basepairs and quality score lower than 10 are filtered out to improve assembly accuracy.
read_QC_trim_ont Technical Details
TheiaProk_ONT calls a sub-workflow listed below, which then calls the individual tasks:
Workflow TheiaProk_ONT Sub-workflow wf_read_QC_trim_ont.wdl Tasks task_nanoplot.wdl task_fastq_scan.wdl task_rasusa.wdl task_nanoq.wdl task_tiptoft.wdl Software Source Code fastq-scan, NanoPlot, RASUSA, tiptoft, nanoq Original Publication(s) NanoPlot paperRASUSA paperNanoq PaperTiptoft paperdragonflye: De novo Assembly dragonflye Technical Details
Links Task task_dragonflye.wdl Software Source Code dragonflye on GitHub Software Documentation dragonflye on GitHub"},{"location":"workflows/genomic_characterization/theiaprok/#post-assembly-tasks-performed-for-all-taxa","title":"Post-Assembly Tasks (performed for all taxa)","text":"quast: Assembly Quality Assessment QUAST stands for QUality ASsessment Tool. It evaluates genome/metagenome assemblies by computing various metrics without a reference being necessary. It includes useful metrics such as number of contigs, length of the largest contig and N50.
QUAST Technical Details
Links Task task_quast.wdl Software Source Code QUAST on GitHub Software Documentation https://quast.sourceforge.net/ Original Publication(s) QUAST: quality assessment tool for genome assembliesBUSCO: Assembly Quality Assessment BUSCO (Benchmarking Universal Single-Copy Orthologue) attempts to quantify the completeness and contamination of an assembly to generate quality assessment metrics. It uses taxa-specific databases containing genes that are all expected to occur in the given taxa, each in a single copy. BUSCO examines the presence or absence of these genes, whether they are fragmented, and whether they are duplicated (suggestive that additional copies came from contaminants).
BUSCO notation
Here is an example of BUSCO notation: C:99.1%[S:98.9%,D:0.2%],F:0.0%,M:0.9%,n:440. There are several abbreviations used in this output:
A high equity assembly will use the appropriate database for the taxa, have high complete (C) and single-copy (S) percentages, and low duplicated (D), fragmented (F) and missing (M) percentages.
BUSCO Technical Details
Links Task task_busco.wdl Software Source Code BUSCO on GitLab Software Documentation https://busco.ezlab.org/ Orginal publication BUSCO: assessing genome assembly and annotation completeness with single-copy orthologsMUMmer_ANI: Average Nucleotide Identity (optional) Average Nucleotide Identity (ANI) is a useful approach for taxonomic identification. The higher the percentage ANI of a query sequence to a given reference genome, the more likely the sequence is the same taxa as the reference.
ANI is calculated in TheiaProk using a perl script written by Lee Katz (ani-m.pl). This uses MUMmer to rapidly align entire query assemblies to one or more reference genomes. By default, TheiaProk uses a set of 43 reference genomes in RGDv2, a database containing genomes of enteric pathogens commonly sequenced by CDC EDLB & PulseNet participating laboratories. The user may also provide their own reference genome. After genome alignment with MUMmer, ani-m.pl calculates the average nucleotide identity and percent bases aligned between 2 genomes (query and reference genomes)
The default database of reference genomes used is called \"Reference Genome Database version 2\" AKA \"RGDv2\". This database is composed of 43 enteric bacteria representing 32 species and is intended for identification of enteric pathogens and common contaminants. It contains six Campylobacter spp., three Escherichia/Shigella spp., one Grimontia hollisae, six Listeria spp., one Photobacterium damselae, two Salmonella spp., and thirteen Vibrio spp.
2 Thresholds are utilized to prevent false positive hits. The ani_top_species_match will only report a genus & species match if both thresholds are surpassed. Both of these thresholds are set to match those used in BioNumerics for PulseNet organisms.
ani_threshold default value of 80.0percent_bases_aligned_threshold default value of 70.0For more information on RGDv2 database of reference genomes, please see the publication here.
MUMmer_ANI Technical Details
Links Task task_mummer_ani.wdl Software Source Code ani-m, MUMmer Software Documentation ani-m, MUMmer Original Publication(s) MUMmer4: A fast and versatile genome alignment system Publication about RGDv2 database https://www.frontiersin.org/articles/10.3389/fmicb.2023.1225207/fullGAMBIT: Taxon Assignment GAMBIT determines the taxon of the genome assembly using a k-mer based approach to match the assembly sequence to the closest complete genome in a database, thereby predicting its identity. Sometimes, GAMBIT can confidently designate the organism to the species level. Other times, it is more conservative and assigns it to a higher taxonomic rank.
For additional details regarding the GAMBIT tool and a list of available GAMBIT databases for analysis, please consult the GAMBIT tool documentation.
GAMBIT Technical Details
Links Task task_gambit.wdl Software Source Code GAMBIT on GitHub Software Documentation GAMBIT ReadTheDocs Original Publication(s) GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identificationKmerFinder: Taxon Assignment (optional) The KmerFinder method predicts prokaryotic species based on the number of overlapping (co-occurring)\u00a0k-mers, i.e., 16-mers, between the query genome and genomes in a reference database.
KmerFinder Technical Details
Links Task task_kmerfinder.wdl Software Source Code https://bitbucket.org/genomicepidemiology/kmerfinder Software Documentation https://cge.food.dtu.dk/services/KmerFinder/instructions.php Original Publication(s) Benchmarking of Methods for Genomic TaxonomyAMRFinderPlus: AMR Genotyping (default) NCBI's AMRFinderPlus is the default antimicrobial resistance (AMR) detection tool used in TheiaProk. ResFinder may be used alternatively and if so, AMRFinderPlus is not run.
AMRFinderPlus identifies acquired antimicrobial resistance (AMR) genes, virulence genes, and stress genes. Such AMR genes confer resistance to antibiotics, metals, biocides, heat, or acid. For some taxa (see here), AMRFinderPlus will provide taxa-specific results including filtering out genes that are almost ubiquitous in the taxa (intrinsic genes) and identifying resistance-associated point mutations. In TheiaProk, the taxon used by AMRFinderPlus is specified based on the gambit_predicted_taxon or a user-provided expected_taxon.
You can check if a gene or point mutation is in the AMRFinderPlus database here, find the sequences of reference genes here, and search the query Hidden Markov Models (HMMs) used by AMRFinderPlus to identify AMR genes and some stress and virulence proteins (here). The AMRFinderPlus database is updated frequently. You can ensure you are using the most up-to-date version by specifying the docker image as a workflow input. You might like to save this docker image as a workspace data element to make this easier.
AMRFinderPlus Technical Details
Links Task task_amrfinderplus.wdl Software Source Code amr on GitHub Software Documentation https://github.com/ncbi/amr/wiki Original Publication(s) AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulenceResFinder: AMR Genotyping & Shigella XDR phenotype prediction (alternative) The ResFinder task is an alternative to using AMRFinderPlus for detection and identification of AMR genes and resistance-associated mutations.
This task runs the Centre for Genomic Epidemiology (CGE) ResFinder tool to identify acquired antimicrobial resistance. It can also run the CGE PointFinder tool if the call_pointfinder variable is set with to true. The databases underlying the task are different to those used by AMRFinderPlus.
The default thresholds for calling AMR genes are 90% identity and 50% coverage of the reference genes (expressed as a fraction in workflow inputs: 0.9 & 0.5). These are the same thresholds utilized in BioNumerics for calling AMR genes.
Organisms currently support by PointFinder for mutational-based predicted resistance:
XDR Shigella prediction
The ResFinder Task also has the ability to predict whether or not a sample meets the CDC's definition for extensively drug-resistant (XDR) Shigella.
CDC defines XDR Shigella bacteria as strains that are resistant to all commonly recommended empiric and alternative antibiotics \u2014 azithromycin, ciprofloxacin, ceftriaxone, trimethoprim-sulfamethoxazole (TMP-SMX), and ampicillin. Link to CDC HAN where this definition is found.
A sample is required to meet all 7 criteria in order to be predicted as XDR Shigella
Shigella OR the user must input the word Shigella somewhere within the input String variable called expected_taxon. This requirement serves as the identification of a sample to be of the Shigella genus.There are 3 potential outputs for the resfinder_predicted_xdr_shigella output string:
Not Shigella based on gambit_predicted_taxon or user inputNot XDR Shigella\u00a0for samples identified as Shigella by GAMBIT or user input BUT does ResFinder did not predict resistance to all 6 drugs in XDR definitionXDR Shigella\u00a0meaning the sample was identified as Shigella and ResFinder/PointFinder did predict resistance to ceftriazone, azithromycin, ciprofloxacin, trimethoprim, sulfamethoxazole, and ampicillin.ResFinder Technical Details
Links Task task_resfinder.wdl Software Source Code https://bitbucket.org/genomicepidemiology/resfinder/src/master/ Software Documentation https://bitbucket.org/genomicepidemiology/resfinder/src/master/ ResFinder database https://bitbucket.org/genomicepidemiology/resfinder_db/src/master/ PointFinder database https://bitbucket.org/genomicepidemiology/pointfinder_db/src/master/ Web-server https://cge.food.dtu.dk/services/ResFinder/ Original Publication(s) ResFinder 4.0 for predictions of phenotypes from genotypesTS_MLST: MLST Profiling Multilocus sequence typing (MLST) is a typing method reflecting population structure. It was developed as a portable, unambiguous method for global epidemiology using PCR, but can be applied to whole-genome sequences in silico. MLST is commonly used for pathogen surveillance, ruling out transmission, and grouping related genomes for comparative analysis.
MLST schemes are taxa-specific. Each scheme uses fragments of typically 7 housekeeping genes (\"loci\") and has a database associating an arbitrary number with each distinct allele of each locus. Each unique combination of alleles (\"allelic profile\") is assigned a numbered sequence type (ST). Significant diversification of genomes is captured by changes to the MLST loci via mutational events creating new alleles and STs, or recombinational events replacing the allele and changing the ST. Relationships between STs are based on the number of alleles they share. Clonal complexes share a scheme-specific number of alleles (usually for five of the seven loci).
MLST Limitations
Some taxa have multiple MLST schemes, and some MLST schemes are insufficiently robust.
TheiaProk uses the MLST tool developed by Torsten Seeman to assess MLST using traditional PubMLST typing schemes.
Interpretation of MLST resultsEach MLST results file returns the ST and allele results for one sample. If the alleles and ST are correctly assigned, only a single integer value will be present for each. If an ST cannot be assigned, multiple integers or additional characters will be shown, representing the issues with assignment as described here.
Identifying novel alleles and STsThe MLST schemes used in TheiaProk are curated on the PubMLST website.If you identify novel alleles or allelic profiles in your data using TheiaProk's MLST task, you can get these assigned via PubMLST:
As default, the MLST tool automatically detects the genome's taxa to select the MLST scheme.
Some taxa have multiple MLST schemes, e.g. the Escherichia and Leptospira genera, Acinetobacter baumannii, Clostridium difficile and Streptococcus thermophilus. Only one scheme will be used by default.
Users may specify the scheme as an optional workflow input using the scheme variable of the \"ts_mlst\" task. Available schemes are listed here and the scheme name should be provided in quotation marks (\"\u2026.\").
If results from multiple MLST schemes are required for the same sample, TheiaProk can be run multiple times specifying non-default schemes. After the first run, output attributes for the workflow (i.e. output column names) must be amended to prevent results from being overwritten. Despite re-running the whole workflow, unmodified tasks will return cached outputs, preventing redundant computation.
TS_MLST Technical Details
Links Task task_ts_mlst.wdl Software Source Code mlst Software Documentation mlstProkka: Assembly Annotation (default) Assembly annotation is available via Prokka as default, or alternatively via Bakta. When Prokka annotation is used, Bakta is not.
Prokka is a prokaryotic genome annotation tool used to identify and describe features of interest within the genome sequence. Prokka annotates there genome by querying databases described here.
Prokka Technical Details
Links Task task_prokka.wdl Software Source Code prokka Software Documentation prokka Original Publication(s) Prokka: rapid prokaryotic genome annotationBakta: Assembly Annotation (alternative) Assembly annotation is available via Bakta as an alternative to Prokka. When Bakta annotation is used, Prokka is not.
Bakta is intended for annotation of Bacteria and plasmids only, and is best described here!
Bakta Technical Details
Links Task task_bakta.wdl Software Source Code bakta Software Documentation https://github.com/oschwengers/bakta Original Publication(s) Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identificationPlasmidFinder: Plasmid Identification PlasmidFinder detects plasmids in totally- or partially-sequenced genomes, and identifies the closest plasmid type in the database for typing purposes.
Plasmids are double-stranded circular or linear DNA molecules that are capable of replication independently of the chromosome and may be transferred between different species and clones. Many plasmids contain resistance or virulence genes, though some do not clearly confer an advantage to their host bacterium.
PlasmidFinder Technical Details
Links Task task_plasmidfinder.wdl Software Source Code https://bitbucket.org/genomicepidemiology/plasmidfinder/src/master/ Software Documentation https://bitbucket.org/genomicepidemiology/plasmidfinder/src/master/ Original Publication(s) In Silico Detection and Typing of Plasmids using PlasmidFinder and Plasmid Multilocus Sequence TypingQC_check: Check QC Metrics Against User-Defined Thresholds (optional) The qc_check task compares generated QC metrics against user-defined thresholds for each metric. This task will run if the user provides a qc_check_table .tsv file. If all QC metrics meet the threshold, the qc_check output variable will read QC_PASS. Otherwise, the output will read QC_NA if the task could not proceed or QC_ALERT followed by a string indicating what metric failed.
The qc_check task applies quality thresholds according to the sample taxa. The sample taxa is taken from the gambit_predicted_taxon value inferred by the GAMBIT module OR can be manually provided by the user using the expected_taxon workflow input.
Example Purposes Only
QC threshold values shown are for example purposes only and should not be presumed to be sufficient for every dataset.
QC_Check Technical Details
Links Task task_qc_check_phb.wdlTaxon Tables: Copy outputs to new data tables based on taxonomic assignment (optional) The taxon_tables module, if enabled, will copy sample data to a different data table based on the taxonomic assignment. For example, if an E. coli sample is analyzed, the module will copy the sample data to a new table for E. coli samples or add the sample data to an existing table.
To implement the taxon_tables module, provide a file indicating data table names to copy samples of each taxa to in the taxon_tables input variable. No other input variables are needed.
Formatting the taxon_tables file
The taxon_tables file must be uploaded a Google storage bucket that is accessible by Terra and should be in the format below. Briefly, the bacterial genera or species should be listed in the leftmost column with the name of the data table to copy samples of that taxon to in the rightmost column.
There are no output columns for the taxon table task. The only output of the task is that additional data tables will appear for in the Terra workspace for samples matching a taxa in the taxon_tables file.
Abricate: Mass screening of contigs for antimicrobial and virulence genes (optional) The abricate module, if enabled, will run abricate with the database defined in abricate_db to perform mass screening of contigs for antimicrobial resistance or virulence genes. It comes bundled with multiple databases: NCBI, CARD, ARG-ANNOT, Resfinder, MEGARES, EcOH, PlasmidFinder, Ecoli_VF and VFDB. It only detects acquired resistance genes,\u00a0NOT\u00a0point mutations
The TheiaProk workflows automatically activate taxa-specific sub-workflows after the identification of relevant taxa using GAMBIT. Alternatively, the user can provide the expected taxa in the expected_taxon workflow input to override the taxonomic assignment made by GAMBIT. Modules are launched for all TheiaProk workflows unless otherwise indicated.
A number of approaches are available in TheiaProk for A. baumannii characterization.
Kaptive: Capsule and lipooligosaccharide outer core typing The cell-surface capsular polysaccharide (CPS) of Acinetobacter baumannii can be used as an epidemiological marker. CPS varies in its composition and structure and is a key determinant in virulence and a target for non-antibiotic therapeutics. Specificity for non-antibiotic therapeutics (e.g. phage therapy) bear particular significance given the extent of antibiotic resistance found in this ESKAPE pathogen.
Biosynthesis and export of CPS is encoded by genes clustering at the K locus (KL). Additional genes associated with CPS biosynthesis and export are sometimes found in other chromosomal locations. The full combination of these genes is summarized as a \"K type\", described as a \"predicted serotype associated with the best match locus\". You can read more about this here.
Previously, serotyping of A. baumannii focused on a major immunogenic polysaccharide which was considered the O antigen for the species. This serotyping approach appears to no longer be used and the serotyping scheme has not been updated in over 20 years. Nonetheless, the O-antigen polysaccharide is attached to lipooligosaccharide, and the outer core (OC) of this lipooligosaccharide varies. Biosynthesis of the outer core lipooligosaccharide is encoded by a cluster of genes at the outer core (OC) locus.
Variation in the KL and OCL can be characterized with the Kaptive tool and its associated databases of numbered A. baumannii K and OC locus variants. Kaptive takes in a genome assembly file (fasta), and assigns the K and OC locus to their numbered variants, provides K type and a description of genes in the K or OC loci and elsewhere in the chromosome, alongside metrics for quality of locus match. A description of how Kaptive works, explanations of the full output reports which are provided in the Terra data table by TheiaProk and resources for interpreting outputs are available on the Kaptive Wiki page.
Kaptive Technical Details
Links Task task_kaptive.wdl Software Source Code Kaptive on GitHub Software Documentation https://github.com/katholt/Kaptive/wiki Orginal publications Identification of Acinetobacter baumannii loci for capsular polysaccharide (KL) and lipooligosaccharide outer core (OCL) synthesis in genome assemblies using curated reference databases compatible with KaptiveAn update to the database for Acinetobacter baumannii capsular polysaccharide locus typing extends the extensive and diverse repertoire of genes found at and outside the K locusAcinetobacterPlasmidTyping: Acinetobacter plasmid detection Acinetobacter plasmids are not included in the PlasmidFinder database. Instead, the AcinetobacterPlasmidTyping database contains variants of the plasmid rep gene for A. baumannii plasmid identification. When matched with >/= 95 % identity, this represents a typing scheme for Acinetobacter baumannii plasmids. In TheiaProk, we use the tool abricate to query our assemblies against this database.
The bioinformatics software for querying sample assemblies against the AcinetobacterPlasmidTyping database is Abricate. The WDL task simply runs abricate, and the Acinetobacter Plasmid database and default setting of 95% minimum identity are set in the merlin magic sub-workflow.
AcinetobacterPlasmidTyping Technical Details
Links Task task_abricate.wdl Database and documentation https://github.com/MehradHamidian/AcinetobacterPlasmidTyping Software Source Code and documentation abricate on GitHub Original Publication(s) Detection and Typing of Plasmids in\u00a0Acinetobacter baumannii\u00a0Using\u00a0rep\u00a0Genes Encoding Replication Initiation Proteins Acinetobacter MLSTTwo MLST schemes are available for Acinetobacter. The Pasteur scheme is run by default, given significant problems with the Oxford scheme have been described. Should users with to alternatively or additionally use the Oxford MLST scheme, see the section above on MLST. The Oxford scheme is activated in TheiaProk with the MLST scheme input as \"abaumannii\".
The blaOXA-51-like genes, also known as oxaAB, are considered intrinsic to Acinetobacter baumannii but are not found in other Acinetobacter species. Identification of a blaOXA-51-like gene is therefore considered to confirm the species' identity as A. baumannii.
NCBI's AMRFinderPlus, which is implemented as a core module in TheiaProk, detects the blaOXA-51-like genes. This may be used to confirm the species, in addition to the GAMBIT taxon identification. The blaOXA-51-like genes act as carbapenemases when an ISAba1 is found 7 bp upstream of the gene. Detection of this IS is not currently undertaken in TheiaProk.
"},{"location":"workflows/genomic_characterization/theiaprok/#escherichia-or-shigella","title":"Escherichia or Shigella spp.","text":"The Escherichia and Shigella genera are difficult to differentiate as they do not comply with genomic definitions of genera and species. Consequently, when either Escherichia or Shigella are identified by GAMBIT, all tools intended for these taxa are used.
SerotypeFinder and ECTyper are intended for analysis of E. coli. Both tools are used as there are occasional discrepancies between the serotypes predicted. This primarily arises due to differences in the databases used by each tool.
SerotypeFinder: Serotyping SerotypeFinder, from the Centre for Genomic Epidemiology (CGE), identifies the serotype of total or partially-sequenced isolates of E. coli.
SerotypeFinder Technical Details
Links Task task_serotypefinder.wdl Software Source Code https://bitbucket.org/genomicepidemiology/serotypefinder/src/master/ Software Documentation https://bitbucket.org/genomicepidemiology/serotypefinder/src/master/ Original Publication(s) Rapid and Easy In Silico Serotyping of Escherichia coli Isolates by Use of Whole-Genome Sequencing DataECTyper: Serotyping ECTyper is a serotyping module for E. coli. In TheiaProk, we are using assembly files as input.
ECTyper Technical Details
Links Task task_ectyper.wdl Software Source Code ECTyper on GitHub Software Documentation ECTyper on GitHub Orginal publication ECTyper: in silico Escherichia coli serotype and species prediction from raw and assembled whole-genome sequence dataVirulenceFinder identifies virulence genes in total or partial sequenced isolates of bacteria. Currently, only E. coli is supported in TheiaProk workflows.
VirulenceFinder: Virulence gene identification VirulenceFinder in TheiaProk is only run on assembly files due to issues regarding discordant results when using read files on the web application versus the command-line.
VirulenceFinder Technical Details
Links Task task_virulencefinder.wdl Software Source Code VirulenceFinder Software Documentation VirulenceFinder Original Publication(s) Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic Escherichia coShigaTyper and ShigEiFinder are intended for differentiation and serotype prediction for any Shigella species and Enteroinvasive Escherichia coli (EIEC). You can read about differences between these here and here. ShigEiFinder can be run using either the assembly (default) or reads. These tasks will report if the samples are neither Shigella nor EIEC.
ShigaTyper: Shigella/EIEC differentiation and serotyping for Illumina and ONT only ShigaTyper predicts Shigella spp. serotypes from Illumina or ONT read data. If the genome is not Shigella or EIEC, the results from this tool will state this. In the notes it provides, it also reports on the presence of ipaB which is suggestive of the presence of the \"virulent invasion plasmid\".
ShigaTyper Technical Details
Links Task task_shigatyper.wdl Software Source Code ShigaTyper on GitHub Software Documentation https://github.com/CFSAN-Biostatistics/shigatyper Origin publication In Silico Serotyping Based on Whole-Genome Sequencing Improves the Accuracy of Shigella IdentificationShigEiFinder: Shigella/EIEC differentiation and serotyping using the assembly file as input ShigEiFinder differentiates\u00a0Shigella and enteroinvasive E. coli (EIEC) using cluster-specific genes, identifies some serotypes based on the presence of O-antigen and H-antigen genes, and predicts the number of virulence plasmids. The shigeifinder task operates on assembly files.
ShigEiFinder Technical Details
Links Task task_shigeifinder.wdl Software Source Code ShigEiFinder on GitHub Software Documentation ShigEiFinder on GitHub Origin publication Cluster-specific gene markers enhance Shigella and enteroinvasive Escherichia coli in silico serotypingShigEiFinder_reads: Shigella/EIEC differentiation and serotyping using Illumina read files as input (optional) for Illumina data only ShigEiFinder differentiates\u00a0Shigella and enteroinvasive E. coli (EIEC) using cluster-specific genes, identifies some serotypes based on the presence of O-antigen and H-antigen genes, and predicts the number of virulence plasmids. The shigeifinder_reads task performs on read files.
ShigEiFinder_reads Technical Details
Links Task task_shigeifinder.wdl Software Source Code ShigEiFinder on GitHub Software Documentation ShigEiFinder on GitHub Origin publication Cluster-specific gene markers enhance Shigella and enteroinvasive Escherichia coli in silico serotypingSonneiTyper is run only when GAMBIT predicts the S. sonnei species. This is the most common Shigella species in the United States.
SonneiTyper: Shigella sonnei identification, genotyping, and resistance mutation identification for Illumina and ONT data only SonneiTyper identifies Shigella sonnei, and uses single-nucleotide variants for genotyping and prediction of quinolone resistance in gyrA (S83L, D87G, D87Y) and parC (S80I). Outputs are provided in this format.
SonneiTyper is a wrapper script around another tool, Mykrobe, that analyses the S. sonnei genomes.
SonneiTyper Technical Details
Links Task task_sonneityping.wdl Software Source Code Mykrobe, sonneityping Software Documentation https://github.com/Mykrobe-tools/mykrobe/wiki, sonneityping Original Publication(s) Global population structure and genotyping framework for genomic surveillance of the major dysentery pathogen,\u00a0Shigella sonneiShigella XDR prediction. Please see the documentation section above for ResFinder for details regarding this taxa-specific analysis.
StxTyper: Identification and typing of Shiga toxin (Stx) genes using the assembly file as input StxTyper screens bacterial genome assemblies for shiga toxin genes and subtypes them into known subtypes and also looks for novel subtypes in cases where the detected sequences diverge from the reference sequences.
Shiga toxin is the main virulence factor of Shiga-toxin-producing E. coli (STEC), though these genes are also found in Shigella species as well as some other genera more rarely, such as Klebsiella. Please see this review paper that describes shiga toxins in great detail.
Running StxTyper via the TheiaProk workflows
The TheiaProk workflow will automatically run stxtyper on all E. coli and Shigella spp. samples, but the user can opt-in to running the tool on any sample by setting the optional input variable call_stxtyper to true when configuring the workflow.
Generally, stxtyper looks for stxA and stxB subunits that compose a complete operon. The A subunit is longer (in amino acid length) than the B subunit. Stxtyper attempts to detect these, compare them to a database of known sequences, and type them based on amino acid composition. There typing algorithm and rules defining how to type these genes & operons will be described more completely in a publication that will be available in the future.
The stxtyper_report output TSV is provided in this output format.
Eventually this tool will be incorporated into AMRFinderPlus and will run behind-the-scenes when the user (or in this case, the TheiaProk workflow) provides the amrfinder --organism Escherichia option.
StxTyper Technical Details
Links Task task_stxtyper.wdl Software Source Code ncbi/stxtyper GitHub repository Software Documentation ncbi/stxtyper GitHub repository Original Publication(s) No publication currently available, as this is a new tool. One will be available in the future."},{"location":"workflows/genomic_characterization/theiaprok/#haemophilus-influenzae","title":"Haemophilus influenzae","text":"hicap: Sequence typing Identification of\u00a0cap\u00a0locus serotype in\u00a0Haemophilus influenzae\u00a0assemblies with hicap.
The\u00a0cap\u00a0locus of\u00a0H. influenzae\u00a0is categorised into 6 different groups based on serology (a-f). There are three functionally distinct regions of the\u00a0cap\u00a0locus, designated\u00a0region I,\u00a0region II, and\u00a0region III. Genes within\u00a0region I\u00a0(bexABCD) and\u00a0region III\u00a0(hcsAB) are associated with transport and post-translation modification. The\u00a0region II\u00a0genes encode serotype-specific proteins, with each serotype (a-f) having a distinct set of genes.\u00a0cap\u00a0loci are often subject to structural changes (e.g. duplication, deletion) making the process of\u00a0in silico\u00a0typing and characterisation of loci difficult.
hicap\u00a0automates the identification of the\u00a0cap\u00a0locus, describes the structural layout, and performs\u00a0in silico\u00a0serotyping.
hicap Technical Details
Links Task task_hicap.wdl Software Source Code hicap on GitHub Software Documentation hicap on GitHub Original Publication(s) hicap: In Silico Serotyping of the Haemophilus influenzae Capsule Locus"},{"location":"workflows/genomic_characterization/theiaprok/#klebsiella","title":"Klebsiella spp.","text":"Kleborate: Species identification, MLST, serotyping, AMR and virulence characterization Kleborate is a tool to identify the Klebsiella species, MLST sequence type, serotype, virulence factors (ICEKp and plasmid associated), and AMR genes and mutations. Serotyping is based on the capsular (K antigen) and lipopolysaccharide (LPS) (O antigen) genes. The resistance genes identified by Kleborate are described here.
Kleborate Technical Details
Links Task task_kleborate.wdl Software Source Code kleborate on GitHub Software Documentation https://github.com/katholt/Kleborate/wiki Orginal publication A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complexIdentification of Klebsiella capsule synthesis loci from whole genome data"},{"location":"workflows/genomic_characterization/theiaprok/#legionella-pneumophila","title":"Legionella pneumophila","text":"Legsta: Sequence-based typing Legsta performs a sequence-based typing of Legionella pneumophila, with the intention of being used for outbreak investigations.
Legsta Technical Details
Links Task task_legsta.wdl Software Source Code Legsta Software Documentation Legsta"},{"location":"workflows/genomic_characterization/theiaprok/#listeria-monocytogenes","title":"Listeria monocytogenes","text":"LisSero: Serogroup prediction LisSero performs serogroup prediction (1/2a, 1/2b, 1/2c, or 4b) for Listeria monocytogenes based on the presence or absence of five genes, lmo1118, lmo0737, ORF2110, ORF2819, and prs. These do not predict somatic (O) or flagellar (H) biosynthesis.
LisSero Technical Details
Links Task task_lissero.wdl Software Source Code LisSero Software Documentation LisSero"},{"location":"workflows/genomic_characterization/theiaprok/#mycobacterium-tuberculosis","title":"Mycobacterium tuberculosis","text":"TBProfiler: Lineage and drug susceptibility prediction for Illumina and ONT only TBProfiler identifies Mycobacterium tuberculosis complex species, lineages, sub-lineages and drug resistance-associated mutations.
TBProfiler Technical Details
Links Task task_tbprofiler.wdl Software Source Code TBProfiler on GitHub Software Documentation https://jodyphelan.gitbook.io/tb-profiler/ Original Publication(s) Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugstbp-parser: Interpretation and Parsing of TBProfiler JSON outputs; requires TBProfiler and tbprofiler_additonal_outputs = true tbp-parser adds useful drug resistance interpretation by applying expert rules and organizing the outputs from TBProfiler. Please note that this tool has not been tested on ONT data and although it is available, result accuracy should be considered carefully. To understand this module and its functions, please examine the README found with the source code here.
tbp-parser Technical Details
Links Task task_tbp_parser.wdl Software Source Code tbp-parser Software Documentation tbp-parserClockwork: Decontamination of input read files for Illumina PE only Clockwork decontaminates paired-end data by removing all reads that do not match the H37Rv genome or are unmapped.
Clockwork Technical Details
Links Task task_clockwork.wdl Software Source Code clockwork Software Documentation https://github.com/iqbal-lab-org/clockwork/wiki"},{"location":"workflows/genomic_characterization/theiaprok/#neisseria","title":"Neisseria spp.","text":"ngmaster: Neisseria gonorrhoeae sequence typing NG-MAST is currently the most widely used method for epidemiological surveillance of\u00a0Neisseria gonorrhoea. This tool is targeted at clinical and research microbiology laboratories that have performed WGS of\u00a0N. gonorrhoeae isolates and wish to understand the molecular context of their data in comparison to previously published epidemiological studies. As WGS becomes more routinely performed,\u00a0NGMASTER \u00a0has been developed to completely replace PCR-based NG-MAST, reducing time and labour costs.
The NG-STAR offers a standardized method of classifying seven well-characterized genes associated antimicrobial resistance in N. gonorrhoeae (penA, mtrR, porB, ponA, gyrA, parC and 23S rRNA) to three classes of antibiotics (cephalosporins, macrolides and fluoroquinolones).
ngmaster combines two tools: NG-MAST (in silico multi-antigen sequencing typing) and NG-STAR (sequencing typing for antimicrobial resistance).
ngmaster Technical Details
Links Task task_ngmaster.wdl Software Source Code ngmaster Software Documentation ngmaster Original Publication(s) NGMASTER: in silico multi-antigen sequence typing for Neisseria gonorrhoeaemeningotype: Neisseria meningitidis serotyping This tool performs serotyping, MLST, finetyping (of porA, fetA, and porB), and Bexsero Antigen Sequencing Typing (BAST).
meningotype Technical Details
Links Task task_meningotype.wdl Software Source Code meningotype Software Documentation meningotype"},{"location":"workflows/genomic_characterization/theiaprok/#pseudomonas-aeruginosa","title":"Pseudomonas aeruginosa","text":"pasty: Serotyping pasty is a tool for in silico serogrouping of Pseudomonas aeruginosa isolates. pasty was developed by Robert Petit, based on the PAst tool from the Centre for Genomic Epidemiology.
pasty Technical Details
Links Task task_pasty.wdl Software Source Code pasty Software Documentation pasty Original Publication(s) Application of Whole-Genome Sequencing Data for O-Specific Antigen Analysis and In Silico Serotyping of Pseudomonas aeruginosa Isolates."},{"location":"workflows/genomic_characterization/theiaprok/#salmonella","title":"Salmonella spp.","text":"Both SISTR and SeqSero2 are used for serotyping all Salmonella spp. Occasionally, the predicted serotypes may differ between SISTR and SeqSero2. When this occurs, differences are typically small and analogous, and are likely as a result of differing source databases. More information about Salmonella serovar nomenclature can be found here. For Salmonella Typhi, genotyphi is additionally run for further typing.
SISTR: Salmonella serovar prediction SISTR performs Salmonella spp. serotype prediction using antigen gene and cgMLST gene alleles. In TheiaProk. SISTR is run on genome assemblies, and uses the default database setting (smaller \"centroid\" alleles or representative alleles instead of the full set of cgMLST alleles). It also runs a QC mode to determine the level of confidence in the serovar prediction (see here).
SISTR Technical Details
Links Task task_sistr.wdl Software Source Code SISTR Software Documentation SISTR Original Publication(s) The Salmonella In Silico Typing Resource (SISTR): an open web-accessible tool for rapidly typing and subtyping draft Salmonella genome assemblies.SeqSero2: Serotyping SeqSero2 is a tool for Salmonella serotype prediction. In the TheiaProk Illumina and ONT workflows, SeqSero2 takes in raw sequencing reads and performs targeted assembly of serotype determinant alleles, which can be used to predict serotypes including contamination between serotypes. Optionally, SeqSero2 can take the genome assembly as input.
SeqSero2 Technical Details
Links Task task_seqsero2.wdl Software Source Code SeqSero2 Software Documentation SeqSero2 Original Publication(s) Salmonella serotype determination utilizing high-throughput genome sequencing data.SeqSero2: rapid and improved Salmonella serotype determination using whole genome sequencing data.genotyphi: Salmonella Typhi lineage, clade, subclade and plasmid typing, AMR prediction for Illumina and ONT only genotyphi is activated upon identification of the \"Typhi\" serotype by SISTR or SeqSero2. genotyphi divides the Salmonella enterica serovar Typhi population into detailed lineages, clades, and subclades. It also detects mutations in the quinolone-resistance determining regions, acquired antimicrobial resistance genes, plasmid replicons, and subtypes of the IncHI1 plasmid which is associated with multidrug resistance.
TheiaProk uses the Mykrobe implementation of genotyphi that takes raw sequencing reads as input.
genotyphi Technical Details
Links Task task_genotyphi.wdl Software Source Code genotyphi Software Documentation https://github.com/katholt/genotyphi/blob/main/README.md#mykrobe-implementation Orginal publication An extended genotyping framework for Salmonella enterica serovar Typhi, the cause of human typhoidFive Years of GenoTyphi: Updates to the Global Salmonella Typhi Genotyping Framework"},{"location":"workflows/genomic_characterization/theiaprok/#staphyloccocus-aureus","title":"Staphyloccocus aureus","text":"spatyper: Sequence typing Given a fasta file or multiple fasta files, this script identifies the repeats and the order and generates a spa type. The repeat sequences and repeat orders found on\u00a0http://spaserver2.ridom.de/ are used to identify the spa type of each enriched sequence. Ridom spa type and the genomics repeat sequence are then reported back to the user.
spatyper Technical Details
Links Task task_spatyper.wdl Software Source Code spatyper Software Documentation spatyperstaphopia-sccmec: Sequence typing This tool assigns a SCCmec type by BLAST the SCCmec primers against an assembly. staphopia-sccmecreports\u00a0True for exact primer matches and\u00a0False for at least 1 base pair difference. The Hamming Distance is also reported.
staphopia-sccmec Technical Details
Links Task task_staphopiasccmec.wdl Software Source Code staphopia-sccmec Software Documentation staphopia-sccmec Original Publication(s) Staphylococcus aureus viewed from the perspective of 40,000+ genomesagrvate: Sequence typing This tool identifies the agr locus type and reports possible variants in the agr operon. AgrVATE accepts a\u00a0S. aureus genome assembly as input and performs a kmer search using an Agr-group specific kmer database to assign the Agr-group. The\u00a0agr operon is then extracted using\u00a0in-silico PCR and variants are called using an Agr-group specific reference operon.
agrvate Technical Details
Links Task task_agrvate.wdl Software Source Code agrVATE Software Documentation agrVATE Original Publication(s) Species-Wide Phylogenomics of the Staphylococcus aureus Agr Operon Revealed Convergent Evolution of Frameshift Mutations"},{"location":"workflows/genomic_characterization/theiaprok/#streptococcus-pneumoniae","title":"Streptococcus pneumoniae","text":"PopPUNK: Global Pneumococcal Sequence Cluster typing Global Pneumococcal Sequence Clusters (GPSC) define and name pneumococcal strains. GPSC designation is undertaken using the PopPUNK software and GPSC database as described in the file below, obtained from here.
:file: GPSC_README_PopPUNK2.txt
Interpreting GPSC results
*_external_clusters.csv novel clusters are assigned NA. For isolates that are assigned a novel cluster and pass QC, you can email\u00a0globalpneumoseq@gmail.com\u00a0to have these novel clusters added to the database.PopPUNK Technical Details
Links Task task_poppunk_streppneumo.wdl GPSC database https://www.pneumogen.net/gps/#/training#command-line Software Source Code PopPunk Software Documentation https://poppunk.readthedocs.io/en/latest/ Original Publication(s) Fast and flexible bacterial genomic epidemiology with PopPUNKSeroBA: Serotyping for Illumina_PE only Streptococcus pneumoniae serotyping is performed with SeroBA.
SeroBA Technical Details
Links Task task_seroba.wdl Software Source Code SeroBA Software Documentation https://sanger-pathogens.github.io/seroba/ Original Publication(s) SeroBA: rapid high-throughput serotyping of Streptococcus pneumoniae from whole genome sequence datapbptyper: Penicillin-binding protein genotyping The Penicillin-binding proteins (PBP) are responsible for the minimum inhibitory concentration phenotype for beta-lactam antibiotic. In Streptococcus pneumoniae, these PBP genes can be identified and typed with PBPTyper.
pbptyper Technical Details
Links Task task_pbptyper.wdl Software Source Code pbptyper Software Documentation pbptyper Original Publication(s) Penicillin-binding protein transpeptidase signatures for tracking and predicting \u03b2-lactam resistance levels in Streptococcus pneumoniae"},{"location":"workflows/genomic_characterization/theiaprok/#streptococcus-pyogenes","title":"Streptococcus pyogenes","text":"emm-typing-tool: Sequence typing for Illumina_PE only emm-typing of Streptococcus pyogenes raw reads. Assign emm type and subtype by querying the CDC M-type specific database.
emm-typing-tool Technical Details
Links Task task_emmtypingtool.wdl Software Source Code emm-typing-tool Software Documentation emm-typing-tool"},{"location":"workflows/genomic_characterization/theiaprok/#vibrio","title":"Vibrio spp.","text":"SRST2: Vibrio characterization for Illumina only The SRST2 Vibrio characterization task detects sequences for Vibrio spp. characterization using Illumina sequence reads and a database of target sequence that are traditionally used in PCR methods. The sequences included in the database are as follows:
SRST2 Technical Details
Links Task task_srst2_vibrio.wdl Software Source Code srst2 Software Documentation srst2 Database Description Docker containerAbricate: Vibrio characterization The Abricate Vibrio characterization task detects sequences for Vibrio spp. characterization using genome assemblies and the abricate \"vibrio\" database. The sequences included in the database are as follows:
Abricate Technical Details
Links Task task_abricate_vibrio.wdl Software Source Code abricate Software Documentation abricate Database Description Docker container"},{"location":"workflows/genomic_characterization/theiaprok/#outputs","title":"Outputs","text":"Variable Type Description Workflow abricate_abaum_database String Database of reference A. baumannii plasmid typing genes used for plasmid typing FASTA, ONT, PE, SE abricate_abaum_docker String Docker file used for running abricate FASTA, ONT, PE, SE abricate_abaum_plasmid_tsv File https://github.com/tseemann/abricate#output containing a row for each A. baumannii plasmid type gene found in the sample FASTA, ONT, PE, SE abricate_abaum_plasmid_type_genes String A. baumannii Plasmid typing genes found in the sample; from GENE column in https://github.com/tseemann/abricate#output FASTA, ONT, PE, SE abricate_abaum_version String Version of abricate used for A. baumannii plasmid typing FASTA, ONT, PE, SE abricate_database String Database of reference used with Abricate FASTA, ONT, PE, SE abricate_docker String Docker file used for running abricate FASTA, ONT, PE, SE abricate_genes String Genes found in the sample; from GENE column in https://github.com/tseemann/abricate#output FASTA, ONT, PE, SE abricate_results_tsv File https://github.com/tseemann/abricate#output containing a row for each gene found in the sample FASTA, ONT, PE, SE abricate_version String Version of abricate used for A. baumannii plasmid typing FASTA, ONT, PE, SE abricate_vibrio_biotype String Biotype classification according to tcpA gene sequence (Classical or ElTor) FASTA, ONT, PE, SE abricate_vibrio_ctxA String Presence or absence of the ctxA gene FASTA, ONT, PE, SE abricate_vibrio_detailed_tsv File Detailed ABRicate output file FASTA, ONT, PE, SE abricate_vibrio_ompW String Presence or absence of the ompW gene FASTA, ONT, PE, SE abricate_vibrio_serogroup String Serotype classification as O1 (wbeN gene), O139 (wbfR gene) or not detected. FASTA, ONT, PE, SE abricate_vibrio_toxR String Presence or absence of the toxR gene FASTA, ONT, PE, SE abricate_vibrio_version String The abricate version run FASTA, ONT, PE, SE agrvate_agr_canonical String Canonical or non-canonical agrD FASTA, ONT, PE, SE agrvate_agr_group String Agr group FASTA, ONT, PE, SE agrvate_agr_match_score String Match score for agr group FASTA, ONT, PE, SE agrvate_agr_multiple String If multiple agr groups were found FASTA, ONT, PE, SE agrvate_agr_num_frameshifts String Number of frameshifts found in CDS of extracted agr operon FASTA, ONT, PE, SE agrvate_docker String The docker used for AgrVATE FASTA, ONT, PE, SE agrvate_results File A gzipped tarball of all results FASTA, ONT, PE, SE agrvate_summary File The summary file produced FASTA, ONT, PE, SE agrvate_version String The version of AgrVATE used FASTA, ONT, PE, SE amrfinderplus_all_report File Output TSV file from AMRFinderPlus (described https://github.com/ncbi/amr/wiki/Running-AMRFinderPlus#fields) FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_betalactam_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to beta-lactams FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_carbapenem_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to carbapenem FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_cephalosporin_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to cephalosporin FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_cephalothin_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to cephalothin FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_genes String Beta-lactam AMR genes identified by AMRFinderPlus FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_methicillin_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to methicilin FASTA, ONT, PE, SE amrfinderplus_amr_classes String AMRFinderPlus predictions for classes of drugs that genes found in the reads are known to confer resistance to FASTA, ONT, PE, SE amrfinderplus_amr_core_genes String AMR genes identified by AMRFinderPlus where the scope is \"core\" FASTA, ONT, PE, SE amrfinderplus_amr_plus_genes String AMR genes identified by AMRFinderPlus where the scope is \"plus\" FASTA, ONT, PE, SE amrfinderplus_amr_report File TSV file detailing AMR genes only, from the amrfinderplus_all_report FASTA, ONT, PE, SE amrfinderplus_amr_subclasses String More specificity about the drugs that genes identified in the reads confer resistance to FASTA, ONT, PE, SE amrfinderplus_db_version String AMRFinderPlus database version used FASTA, ONT, PE, SE amrfinderplus_stress_genes String Stress genes identified by AMRFinderPlus FASTA, ONT, PE, SE amrfinderplus_stress_report File TSV file detailing stress genes only, from the amrfinderplus_all_report FASTA, ONT, PE, SE amrfinderplus_version String AMRFinderPlus version used FASTA, ONT, PE, SE amrfinderplus_virulence_genes String Virulence genes identified by AMRFinderPlus FASTA, ONT, PE, SE amrfinderplus_virulence_report File TSV file detailing virulence genes only, from the amrfinderplus_all_report FASTA, ONT, PE, SE ani_highest_percent Float Highest ANI between query and any given reference genome (top species match) FASTA, ONT, PE, SE ani_highest_percent_bases_aligned Float Percentage of bases aligned between query genome and top species match FASTA, ONT, PE, SE ani_mummer_docker String Docker image used to run the ANI_mummer task FASTA, ONT, PE, SE ani_mummer_version String Version of MUMmer used FASTA, ONT, PE, SE ani_output_tsv File Full output TSV from ani-m FASTA, ONT, PE, SE ani_top_species_match String Species of genome with highest ANI to query FASTA FASTA, ONT, PE, SE assembly_fasta File https://github.com/tseemann/shovill#contigsfa ONT, PE, SE assembly_length Int Length of assembly (total contig length) as determined by QUAST FASTA, ONT, PE, SE bakta_gbff File Genomic GenBank format annotation file FASTA, ONT, PE, SE bakta_gff3 File Generic Feature Format Version 3 file FASTA, ONT, PE, SE bakta_summary File Bakta summary output TXT file FASTA, ONT, PE, SE bakta_tsv File Annotations as simple human readable TSV FASTA, ONT, PE, SE bakta_version String Bakta version used FASTA, ONT, PE, SE bbduk_docker String BBDuk docker image used PE, SE busco_database String BUSCO database used FASTA, ONT, PE, SE busco_docker String BUSCO docker image used FASTA, ONT, PE, SE busco_report File A plain text summary of the results in BUSCO notation FASTA, ONT, PE, SE busco_results String BUSCO results (see https://www.notion.so/TheiaProk-Workflow-Series-68c34aca2a0240ef94fef0acd33651b9?pvs=21) FASTA, ONT, PE, SE busco_version String BUSCO software version used FASTA, ONT, PE, SE cg_pipeline_docker String Docker file used for running CG-Pipeline on cleaned reads PE, SE cg_pipeline_report_clean File TSV file of read metrics from clean reads, including average read length, number of reads, and estimated genome coverage PE, SE cg_pipeline_report_raw File TSV file of read metrics from raw reads, including average read length, number of reads, and estimated genome coverage PE, SE clockwork_decontaminated_read1 File Decontaminated forward reads by Clockwork PE clockwork_decontaminated_read2 File Decontaminated reverse reads by Clockwork PE combined_mean_q_clean Float Mean quality score for the combined clean reads PE combined_mean_q_raw Float Mean quality score for the combined raw reads PE combined_mean_readlength_clean Float Mean read length for the combined clean reads PE combined_mean_readlength_raw Float Mean read length for the combined raw reads PE contigs_fastg File Assembly graph if megahit used for genome assembly PE contigs_gfa File Assembly graph if spades used for genome assembly ONT, PE, SE contigs_lastgraph File Assembly graph if velvet used for genome assembly PE dragonflye_version String Version of dragonflye used for de novo assembly ONT ectyper_predicted_serotype String Serotype predicted by ECTyper FASTA, ONT, PE, SE ectyper_results File TSV file of evidence for ECTyper predicted serotype (see https://github.com/phac-nml/ecoli_serotyping#report-format) FASTA, ONT, PE, SE ectyper_version String Version of ECTyper used FASTA, ONT, PE, SE emmtypingtool_docker String Docker image for emm-typing-tool PE emmtypingtool_emm_type String emm-type predicted PE emmtypingtool_results_xml File XML file with emm-typing-tool resuls PE emmtypingtool_version String Version of emm-typing-tool used PE est_coverage_clean Float Estimated coverage calculated from clean reads and genome length ONT, PE, SE est_coverage_raw Float Estimated coverage calculated from raw reads and genome length ONT, PE, SE fastp_html_report File The HTML report made with fastp PE, SE fastp_version String Version of fastp software used PE, SE fastq_scan_clean1_json File JSON file output fromfastq-scan containing summary stats about clean forward read quality and length PE, SE fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length PE fastq_scan_num_reads_clean_pairs String Number of read pairs after cleaning as calculated by fastq_scan PE fastq_scan_num_reads_clean1 Int Number of forward reads after cleaning as calculated by fastq_scan PE, SE fastq_scan_num_reads_clean2 Int Number of reverse reads after cleaning as calculated by fastq_scan PE fastq_scan_num_reads_raw_pairs String Number of input read pairs calculated by fastq_scan PE fastq_scan_num_reads_raw1 Int Number of input forward reads calculated by fastq_scan PE, SE fastq_scan_num_reads_raw2 Int Number of input reverse reads calculated by fastq_scan PE fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length PE, SE fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length PE fastq_scan_version String Version of fastq-scan software used PE, SE fastqc_clean1_html File Graphical visualization of clean forward read quality from fastqc to open in an internet browser PE, SE fastqc_clean2_html File Graphical visualization of clean reverse read quality from fastqc to open in an internet browser PE fastqc_docker String Docker container used with fastqc PE, SE fastqc_num_reads_clean_pairs String Number of read pairs after cleaning by fastqc PE fastqc_num_reads_clean1 Int Number of forward reads after cleaning by fastqc PE, SE fastqc_num_reads_clean2 Int Number of reverse reads after cleaning by fastqc PE fastqc_num_reads_raw_pairs String Number of input read pairs by fastqc PE fastqc_num_reads_raw1 Int Number of input reverse reads by fastqc PE, SE fastqc_num_reads_raw2 Int Number of input reverse reads by fastqc PE fastqc_raw1_html File Graphical visualization of raw forward read quality from fastqc to open in an internet browser PE, SE fastqc_raw2_html File Graphical visualization of raw reverse read qualityfrom fastqc to open in an internet browser PE fastqc_version String Version of fastqc software used PE, SE gambit_closest_genomes File CSV file listing genomes in the GAMBIT database that are most similar to the query assembly FASTA, ONT, PE, SE gambit_db_version String Version of GAMBIT used FASTA, ONT, PE, SE gambit_docker String GAMBIT docker file used FASTA, ONT, PE, SE gambit_predicted_taxon String Taxon predicted by GAMBIT FASTA, ONT, PE, SE gambit_predicted_taxon_rank String Taxon rank of GAMBIT taxon prediction FASTA, ONT, PE, SE gambit_report File GAMBIT report in a machine-readable format FASTA, ONT, PE, SE gambit_version String Version of GAMBIT software used FASTA, ONT, PE, SE genotyphi_final_genotype String Final genotype call from GenoTyphi ONT, PE, SE genotyphi_genotype_confidence String Confidence in the final genotype call made by GenoTyphi ONT, PE, SE genotyphi_mykrobe_json File JSON file of GenoTyphi output, described https://github.com/katholt/genotyphi#explanation-of-columns-in-the-output ONT, PE, SE genotyphi_report_tsv File TSV file of GenoTyphi output, described https://github.com/katholt/genotyphi#explanation-of-columns-in-the-output ONT, PE, SE genotyphi_species String Species call from Mykrobe, used to run GenoTyphi ONT, PE, SE genotyphi_st_probes_percent_coverage Float Percentage coverage to the Typhi MLST probes ONT, PE, SE genotyphi_version String Version of GenoTyphi used ONT, PE, SE hicap_docker String Docker image used for hicap ONT, PE, SE hicap_genes String cap\u00a0genes identified. genes on different contigs delimited by;. truncation shown by trailing\u00a0* ONT, PE, SE hicap_results_tsv File TSV file of hicap output ONT, PE, SE hicap_serotype String hicap serotype ONT, PE, SE hicap_version String hicap version used ONT, PE, SE kaptive_k_locus String Best matching K locus identified by Kaptive FASTA, ONT, PE, SE kaptive_k_type String Best matching K type identified by Kaptive FASTA, ONT, PE, SE kaptive_kl_confidence String Kaptive\u2019s confidence in the KL match (see https://github.com/katholt/Kaptive/wiki/Interpreting-the-results) FASTA, ONT, PE, SE kaptive_oc_locus String Best matching K locus identified by Kaptive FASTA, ONT, PE, SE kaptive_ocl_confidence String Kaptive\u2019s confidence in the OCL match (see https://github.com/katholt/Kaptive/wiki/Interpreting-the-results) FASTA, ONT, PE, SE kaptive_output_file_k File TSV https://github.com/katholt/Kaptive/wiki/How-to-run#output-filesfrom the K locus from Kaptive FASTA, ONT, PE, SE kaptive_output_file_oc File TSV https://github.com/katholt/Kaptive/wiki/How-to-run#output-filesfrom the OC locus from Kaptive FASTA, ONT, PE, SE kaptive_version String Version of Kaptive used FASTA, ONT, PE, SE kleborate_docker String Kleborate docker image used FASTA, ONT, PE, SE kleborate_genomic_resistance_mutations String Genomic resistance mutations identifies by Kleborate FASTA, ONT, PE, SE kleborate_key_resistance_genes String Key resistance genes identified by Kleborate FASTA, ONT, PE, SE kleborate_klocus String Best matching K locus identified by Kleborate via Kaptive FASTA, ONT, PE, SE kleborate_klocus_confidence String Kaptive\u2019s confidence in the KL match (see https://github.com/katholt/Kaptive/wiki/Interpreting-the-results) FASTA, ONT, PE, SE kleborate_ktype String Best matching K type identified by Kleborate via Kaptive FASTA, ONT, PE, SE kleborate_mlst_sequence_type String https://github.com/katholt/Kleborate/wiki/MLST#multi-locus-sequence-typing-mlst call by Kleborate FASTA, ONT, PE, SE kleborate_olocus String Best matching OC locus identified by Kleborate via Kaptive FASTA, ONT, PE, SE kleborate_olocus_confidence String Kaptive\u2019s confidence in the KL match (see https://github.com/katholt/Kaptive/wiki/Interpreting-the-results) FASTA, ONT, PE, SE kleborate_otype String Best matching OC type identified by Kleborate via Kaptive FASTA, ONT, PE, SE kleborate_output_file File https://github.com/katholt/Kleborate/wiki/Scores-and-counts FASTA, ONT, PE, SE kleborate_resistance_score String Resistance score as given by kleborate FASTA, ONT, PE, SE kleborate_version String Version of Kleborate used FASTA, ONT, PE, SE kleborate_virulence_score String Virulence score as given by kleborate FASTA, ONT, PE, SE kmerfinder_database String Database used to run KmerFinder FASTA, ONT, PE, SE kmerfinder_docker String Docker image used to run KmerFinder FASTA, ONT, PE, SE kmerfinder_query_coverage String KmerFinder\u2019s query coverage of the top hit result FASTA, ONT, PE, SE kmerfinder_results_tsv File Output TSV file created by KmerFinder FASTA, ONT, PE, SE kmerfinder_template_coverage String FASTA, ONT, PE, SE kmerfinder_top_hit String Top hit species of KmerFinder FASTA, ONT, PE, SE kraken2_database String Kraken2 database used for the taxonomic assignment ONT, PE, SE kraken2_docker String Docker container for Kraken2 ONT, PE, SE kraken2_report File Report, in text format, of Kraken2 results ONT, PE, SE kraken2_version String Kraken2 version ONT, PE, SE legsta_predicted_sbt String Sequence based type predicted by Legsta FASTA, ONT, PE, SE legsta_results File TSV file of legsta results (see https://github.com/tseemann/legsta#output) FASTA, ONT, PE, SE legsta_version String Version of legsta used FASTA, ONT, PE, SE lissero_results File TSV results file from LisSero (see https://github.com/MDU-PHL/LisSero#example-output) FASTA, ONT, PE, SE lissero_serotype String Serotype predicted by LisSero FASTA, ONT, PE, SE lissero_version String Version of LisSero used FASTA, ONT, PE, SE meningotype_BAST String BAST type FASTA, ONT, PE, SE meningotype_FetA String FetA type FASTA, ONT, PE, SE meningotype_fHbp String fHbp type FASTA, ONT, PE, SE meningotype_NadA String NBA type FASTA, ONT, PE, SE meningotype_NHBA String NHBA type FASTA, ONT, PE, SE meningotype_PorA String PorA type FASTA, ONT, PE, SE meningotype_PorB String PorB type FASTA, ONT, PE, SE meningotype_serogroup String Serogroup FASTA, ONT, PE, SE meningotype_tsv File Full result file FASTA, ONT, PE, SE meningotype_version String Version of meningotype used FASTA, ONT, PE, SE midas_docker String MIDAS docker image used PE, SE midas_primary_genus String Genus of most abundant species in reads PE, SE midas_report File TSV report of full MIDAS results PE, SE midas_secondary_genus String Genus of the next most abundant species after removing all species of the most abundant genus PE, SE midas_secondary_genus_abundance String Relative abundance of secondary genus PE, SE midas_secondary_genus_coverage String Absolute coverage of secondary genus PE, SE n50_value Int N50 of assembly calculated by QUAST FASTA, ONT, PE, SE nanoplot_docker String Docker image for nanoplot ONT nanoplot_html_clean File Clean read file ONT nanoplot_html_raw File Raw read file ONT nanoplot_num_reads_clean1 Int Number of clean reads ONT nanoplot_num_reads_raw1 Int Number of raw reads ONT nanoplot_r1_est_coverage_clean Float Estimated coverage on the clean reads by nanoplot ONT nanoplot_r1_est_coverage_raw Float Estimated coverage on the raw reads by nanoplot ONT nanoplot_r1_mean_q_clean Float Mean quality score of clean forward reads ONT nanoplot_r1_mean_q_raw Float Mean quality score of raw forward reads ONT nanoplot_r1_mean_readlength_clean Float Mean read length of clean forward reads ONT nanoplot_r1_mean_readlength_raw Float Mean read length of raw forward reads ONT nanoplot_r1_median_q_clean Float Median quality score of clean forward reads ONT nanoplot_r1_median_q_raw Float Median quality score of raw forward reads ONT nanoplot_r1_median_readlength_clean Float Median read length of clean forward reads ONT nanoplot_r1_median_readlength_raw Float Median read length of raw forward reads ONT nanoplot_r1_n50_clean Float N50 of clean forward reads ONT nanoplot_r1_n50_raw Float N50 of raw forward reads ONT nanoplot_r1_stdev_readlength_clean Float Standard deviation read length of clean forward reads ONT nanoplot_r1_stdev_readlength_raw Float Standard deviation read length of raw forward reads ONT nanoplot_tsv_clean File Output TSV file created by nanoplot ONT nanoplot_tsv_raw File Output TSV file created by nanoplot ONT nanoplot_version String Version of nanoplot used for analysis ONT nanoq_version String Version of nanoq used in analysis ONT ngmaster_ngmast_porB_allele String porB allele number FASTA, ONT, PE, SE ngmaster_ngmast_sequence_type String NG-MAST sequence type FASTA, ONT, PE, SE ngmaster_ngmast_tbpB_allele String tbpB allele number FASTA, ONT, PE, SE ngmaster_ngstar_23S_allele String 23S rRNA allele number FASTA, ONT, PE, SE ngmaster_ngstar_gyrA_allele String gyrA allele number FASTA, ONT, PE, SE ngmaster_ngstar_mtrR_allele String mtrR allele number FASTA, ONT, PE, SE ngmaster_ngstar_parC_allele String parC allele number FASTA, ONT, PE, SE ngmaster_ngstar_penA_allele String penA allele number FASTA, ONT, PE, SE ngmaster_ngstar_ponA_allele String ponA allele number FASTA, ONT, PE, SE ngmaster_ngstar_porB_allele String porB allele number FASTA, ONT, PE, SE ngmaster_ngstar_sequence_type String NG-STAR sequence type FASTA, ONT, PE, SE ngmaster_tsv File TSV file with NG-MAST/NG-STAR typing FASTA, ONT, PE, SE ngmaster_version String ngmaster version FASTA, ONT, PE, SE number_contigs Int Total number of contigs in assembly FASTA, ONT, PE, SE pasty_all_serogroups File TSV file with details of each serogroup from pasty (see https://github.com/rpetit3/pasty#example-prefixdetailstsv) FASTA, ONT, PE, SE pasty_blast_hits File TSV file of BLAST hits from pasty (see https://github.com/rpetit3/pasty#example-prefixblastntsv) FASTA, ONT, PE, SE pasty_comment String FASTA, ONT, PE, SE pasty_docker String pasty docker image used FASTA, ONT, PE, SE pasty_serogroup String Serogroup predicted by pasty FASTA, ONT, PE, SE pasty_serogroup_coverage Float The breadth of coverage of the O-antigen by pasty FASTA, ONT, PE, SE pasty_serogroup_fragments Int Number of BLAST hits included in the prediction (fewer is better) FASTA, ONT, PE, SE pasty_summary_tsv File TSV summary file of pasty outputs (see https://github.com/rpetit3/pasty#example-prefixtsv) FASTA, ONT, PE, SE pasty_version String Version of pasty used FASTA, ONT, PE, SE pbptyper_docker String pbptyper docker image used FASTA, ONT, PE, SE pbptyper_pbptype_predicted_tsv File TSV file of pbptyper results (see https://github.com/rpetit3/pbptyper#example-prefixtsv) FASTA, ONT, PE, SE pbptyper_predicted_1A_2B_2X String PBP type predicted by pbptyper FASTA, ONT, PE, SE pbptyper_version String Version of pbptyper used FASTA, ONT, PE, SE plasmidfinder_db_version String Version of PlasmidFnder used FASTA, ONT, PE, SE plasmidfinder_docker String PlasmidFinder docker image used FASTA, ONT, PE, SE plasmidfinder_plasmids String Names of plasmids identified by PlasmidFinder FASTA, ONT, PE, SE plasmidfinder_results File Output file from PlasmidFinder in TSV format FASTA, ONT, PE, SE plasmidfinder_seqs File Hit_in_genome_seq.fsa file produced by PlasmidFinder FASTA, ONT, PE, SE poppunk_docker String PopPUNK docker image with GPSC database used FASTA, ONT, PE, SE poppunk_gps_cluster String GPS cluster predicted by PopPUNK FASTA, ONT, PE, SE poppunk_GPS_db_version String Version of GPSC database used FASTA, ONT, PE, SE poppunk_gps_external_cluster_csv File GPSC v6 scheme designations FASTA, ONT, PE, SE poppunk_version String Version of PopPUNK used FASTA, ONT, PE, SE prokka_gbk File GenBank file produced from Prokka annotation of input FASTA FASTA, ONT, PE, SE prokka_gff File Prokka output GFF3 file containing sequence and annotation (you can view this in IGV) FASTA, ONT, PE, SE prokka_sqn File A Sequin file for GenBank submission FASTA, ONT, PE, SE qc_check String A string that indicates whether or not the sample passes a set of pre-determined and user-provided QC thresholds FASTA, ONT, PE, SE qc_standard File The user-provided file that contains the QC thresholds used for the QC check FASTA, ONT, PE, SE quast_gc_percent Float The GC percent of your sample FASTA, ONT, PE, SE quast_report File TSV report from QUAST FASTA, ONT, PE, SE quast_version String Software version of QUAST used FASTA, ONT, PE, SE r1_mean_q_clean Float Mean quality score of clean forward reads PE, SE r1_mean_q_raw Float Mean quality score of raw forward reads PE, SE r1_mean_readlength_clean Float Mean read length of clean forward reads PE, SE r1_mean_readlength_raw Float Mean read length of raw forward reads PE, SE r2_mean_q_clean Float Mean quality score of clean reverse reads PE r2_mean_q_raw Float Mean quality score of raw reverse reads PE r2_mean_readlength_clean Float Mean read length of clean reverse reads PE r2_mean_readlength_raw Float Mean read length of raw reverse reads PE rasusa_version String Version of RASUSA used for analysis ONT read_screen_clean String PASS or FAIL result from clean read screening; FAIL accompanied by the reason for failure ONT, PE, SE read_screen_raw String PASS or FAIL result from raw read screening; FAIL accompanied by thereason for failure ONT, PE, SE read1_clean File Clean forward reads file ONT, PE, SE read2_clean File Clean reverse reads file PE resfinder_db_version String Version of ResFinder database FASTA, ONT, PE, SE resfinder_docker String ResFinder docker image used FASTA, ONT, PE, SE resfinder_pheno_table File Table containing al AMR phenotypes FASTA, ONT, PE, SE resfinder_pheno_table_species File Table with species-specific AMR phenotypes FASTA, ONT, PE, SE resfinder_pointfinder_pheno_table File TSV showing presence(1)/absence(0) of predicted resistance against an antibiotic class FASTA, ONT, PE, SE resfinder_pointfinder_results File Predicted point mutations, grouped by the gene they occur in FASTA, ONT, PE, SE resfinder_predicted_pheno_resistance String Semicolon delimited list of antimicrobial drugs and associated genes and/or point mutations.\u00a0: , , ; : , ; FASTA, ONT, PE, SE resfinder_predicted_resistance_Amp String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Ampicillin based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Axo String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Ceftriaxone based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Azm String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Azithromycin based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Cip String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Ciprofloxacin based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Smx String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Sulfamethoxazole based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Tmp String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Trimothoprim based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_xdr_shigella String Final prediction of XDR Shigella status based on CDC definition. Explanation can be found in the description above this table. FASTA, ONT, PE, SE resfinder_results File Predicted resistance genes grouped by antibiotic class FASTA, ONT, PE, SE resfinder_seqs File FASTA of resistance gene sequences from user\u2019s input sequence FASTA, ONT, PE, SE seq_platform String Sequencing platform input by the user FASTA, ONT, PE, SE seqsero2_predicted_antigenic_profile String Antigenic profile predicted for Salmonella spp. by SeqSero2 ONT, PE, SE seqsero2_predicted_contamination String Indicates whether contamination between Salmonella with different serotypes was predicted by SeqSero2 ONT, PE, SE seqsero2_predicted_serotype String Serotype predicted by SeqSero2 ONT, PE, SE seqsero2_report File TSV report produced by SeqSero2 ONT, PE, SE seqsero2_version String Version of SeqSero2 used ONT, PE, SE seroba_ariba_identity String Percentage identity between the query sequence and ARIBA-predicted serotype PE seroba_ariba_serotype String Serotype predicted by ARIBA, via SeroBA PE seroba_details File Detailed TSV file from SeroBA PE seroba_docker String SeroBA docker image used PE seroba_serotype String Serotype predicted by SeroBA PE seroba_version String SeroBA version used PE serotypefinder_docker String SerotypeFinder docker image used FASTA, ONT, PE, SE serotypefinder_report File TSV report produced by SerotypeFinder FASTA, ONT, PE, SE serotypefinder_serotype String Serotype predicted by SerotypeFinder FASTA, ONT, PE, SE shigatyper_docker String ShigaTyper docker image used ONT, PE, SE shigatyper_hits_tsv File Detailed TSV report from ShigaTyper (seehttps://github.com/CFSAN-Biostatistics/shigatyper#example-prefix-hitstsv) ONT, PE, SE shigatyper_ipaB_presence_absence String Presence (+) or absence (-) of ipaB identified by ShigaTyper ONT, PE, SE shigatyper_notes String Any notes output from ShigaTyper ONT, PE, SE shigatyper_predicted_serotype String Serotype predicted by ShigaTyper ONT, PE, SE shigatyper_summary_tsv File TSV summary report from ShigaTyper (see https://github.com/CFSAN-Biostatistics/shigatyper#example-prefixtsv) ONT, PE, SE shigatyper_version String Version of ShigaTyper used ONT, PE, SE shigeifinder_cluster String Shigella/EIEC cluster identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_cluster_reads String Shigella/EIEC cluster identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_docker String ShigEiFinder docker image used FASTA, ONT, PE, SE shigeifinder_docker_reads String ShigEiFinder docker image used using read files as inputs PE, SE shigeifinder_H_antigen String H-antigen gene identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_H_antigen_reads String H-antigen gene identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_ipaH_presence_absence String Presence (+) or absence (-) of ipaH identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_ipaH_presence_absence_reads String Presence (+) or absence (-) of ipaH identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_notes String Any notes output from ShigEiFinder FASTA, ONT, PE, SE shigeifinder_notes_reads String Any notes output from ShigEiFinder using read files as inputs PE, SE shigeifinder_num_virulence_plasmid_genes String Number of virulence plasmid genes identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_num_virulence_plasmid_genes_reads String Number of virulence plasmid genes identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_O_antigen String O-antigen gene identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_O_antigen_reads String O-antigen gene identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_report File TSV report from ShigEiFinder (see https://github.com/LanLab/ShigEiFinder#shigeifinder) FASTA, ONT, PE, SE shigeifinder_report_reads File TSV report from ShigEiFinder (see https://github.com/LanLab/ShigEiFinder#shigeifinder) using read files as inputs PE, SE shigeifinder_serotype String Serotype predicted by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_serotype_reads String Serotype predicted by ShigEiFinder using read files as inputs PE, SE shigeifinder_version String ShigEiFinder version used FASTA, ONT, PE, SE shigeifinder_version_reads String ShigEiFinder version used using read files as inputs PE, SE shovill_pe_version String Shovill version used PE shovill_se_version String Shovill version used SE sistr_allele_fasta File FASTA file of novel cgMLST alleles from SISTR FASTA, ONT, PE, SE sistr_allele_json File JSON file of cgMLST allele sequences and information (see https://github.com/phac-nml/sistr_cmd#cgmlst-allele-search-results) FASTA, ONT, PE, SE sistr_cgmlst File CSV file of the cgMLST allelic profile from SISTR (see https://github.com/phac-nml/sistr_cmd#cgmlst-allelic-profiles-output---cgmlst-profiles-cgmlst-profilescsv) FASTA, ONT, PE, SE sistr_predicted_serotype String Serotype predicted by SISTR FASTA, ONT, PE, SE sistr_results File TSV results file produced by SISTR (see https://github.com/phac-nml/sistr_cmd#primary-results-output--o-sistr-results) FASTA, ONT, PE, SE sistr_version String Version of SISTR used FASTA, ONT, PE, SE sonneityping_final_genotype String Final genotype call from Mykrobe, via sonneityper ONT, PE, SE sonneityping_final_report_tsv File Detailed TSV report from mykrobe, via sonneityper (see https://github.com/katholt/sonneityping#example-output) ONT, PE, SE sonneityping_genotype_confidence String Confidence in the final genotype call from sonneityper ONT, PE, SE sonneityping_genotype_name String Human readable alias for genotype, where available provided by sonneityper ONT, PE, SE sonneityping_mykrobe_docker String sonneityping docker image used ONT, PE, SE sonneityping_mykrobe_report_csv File CSV report from mykrobe via sonneityper (see https://github.com/Mykrobe-tools/mykrobe/wiki/AMR-prediction-output#csv-file) ONT, PE, SE sonneityping_mykrobe_report_json File JSON report from mykrobe via sonneityper (see https://github.com/Mykrobe-tools/mykrobe/wiki/AMR-prediction-output#json-file) ONT, PE, SE sonneityping_mykrobe_version String Version of sonneityping used ONT, PE, SE sonneityping_species String Species call from Mykrobe via sonneityping ONT, PE, SE spatyper_docker String spatyper docker image used FASTA, ONT, PE, SE spatyper_repeats String order of identified repeats FASTA, ONT, PE, SE spatyper_tsv File TSV report with spatyper results FASTA, ONT, PE, SE spatyper_type String spa type FASTA, ONT, PE, SE spatyper_version String spatyper version used FASTA, ONT, PE, SE srst2_vibrio_biotype String Biotype classification according to tcpA gene sequence (Classical or ElTor) PE, SE srst2_vibrio_ctxA String Presence or absence of the ctxA gene PE, SE srst2_vibrio_detailed_tsv File Detailed https://github.com/katholt/srst2 output file PE, SE srst2_vibrio_ompW String Presence or absence of the ompW gene PE, SE srst2_vibrio_serogroup String Serotype classification as O1 (wbeN gene), O139 (wbfR gene) or not detected. PE, SE srst2_vibrio_toxR String Presence or absence of the toxR gene PE, SE srst2_vibrio_version String The SRST2 version run PE, SE staphopiasccmec_docker String staphopia-sccmec docker image used FASTA, ONT, PE, SE staphopiasccmec_hamming_distance_tsv File staphopia-sccmec version FASTA, ONT, PE, SE staphopiasccmec_results_tsv File sccmec types and mecA presence FASTA, ONT, PE, SE staphopiasccmec_types_and_mecA_presence String staphopia-sccmec Hamming distance file FASTA, ONT, PE, SE staphopiasccmec_version String staphopia-sccmec presence and absence TSV file FASTA, ONT, PE, SE stxtyper_all_hits String Comma-separated list of matches of all types. Includes complete, partial, frameshift, internal stop, and novel hits. List is de-duplicated so multiple identical hits are only listed once. For example if 5 partial stx2 hits are detected in the genome, only 1 \"stx2\" will be listed in this field. To view the potential subtype for each partial hit, the user will need to view the stxtyper_report TSV file. FASTA, ONT, PE, SE stxtyper_complete_operons String Comma-separated list of all COMPLETE operons detected by StxTyper. Show multiple hits if present in results. FASTA, ONT, PE, SE stxtyper_docker String Name of docker image used by the stxtyper task. FASTA, ONT, PE, SE stxtyper_novel_hits String Comma-separated list of matches that have the OPERON output of \"COMPLETE_NOVEL\". Possible outputs \"stx1\", \"stx2\", or \"stx1,stx2\" FASTA, ONT, PE, SE stxtyper_num_hits Int Number of \"hits\" or rows present in the stxtyper_report TSV file FASTA, ONT, PE, SE stxtyper_partial_hits String Possible outputs \"stx1\", \"stx2\", or \"stx1,stx2\". Tells the user that there was a partial hit to either the A or B subunit, but does not describe which subunit, only the possible types from the PARTIAL matches. FASTA, ONT, PE, SE stxtyper_report File Raw results TSV file produced by StxTyper FASTA, ONT, PE, SE stxtyper_stx_frameshifts_or_internal_stop_hits String Comma-separated list of matches that have the OPERON output of \"FRAMESHIFT\" or \"INTERNAL_STOP\". Possible outputs \"stx1\", \"stx2\", or \"stx1,stx2\" FASTA, ONT, PE, SE stxtyper_version String Version of StxTyper used FASTA, ONT, PE, SE taxon_table_status String Status of the taxon table upload FASTA, ONT, PE, SE tbp_parser_average_genome_depth Float Optional output. Average genome depth across the reference genome ONT, PE, SE tbp_parser_coverage_report File Optional output. TSV file with breadth of coverage of each gene associated with antimicrobial resistance in mycobacterium tuberculosis. ONT, PE, SE tbp_parser_docker String Optional output. The docker image for tbp-parser ONT, PE tbp_parser_genome_percent_coverage Float Optional output. The percent of the genome covered at a depth greater than the specified minimum (default 10) ONT, PE, SE tbp_parser_laboratorian_report_csv File Optional output. Human-readable laboratorian report file containing the list of mutations found to be conferring resistance, both by WHO classification and expert rule implementation. The file contains the following columns: sample_id, tbprofiler_gene_name, tbprofiler_variant_locus_tag, tbprofiler_variant_substitution_type, tbprofiler_variant_substitution_nt, tbprofiler_variant_substitution_aa, confidence according to WHO, antimicrobial, depth, frequency, read_support, rationale ( WHO or expert rule), and warning if the coverage is below specified minimum (default 10) ONT, PE, SE tbp_parser_lims_report_csv File Optional output. LIMS digestable CSV report containing information on resistance for a set of antimicrobials ( No resistance to X detected, The detected genetic determinant(s) have uncertain significance, resistance to X cannot be ruled out and Genetic determinant(s) associated with resistance to X detected). For each antimicrobial, the mutations found are reported in the mutation_nucleotide; (mutation_protein) format, otherwise No mutations detected is reported. ONT, PE, SE tbp_parser_looker_report_csv File Optional output. Looker digestible CSV report containing information on resistance for a set of antimicrobials (R for resistant, S for susceptible) ONT, PE, SE tbp_parser_version String Optional output. The version of tbp-parser ONT, PE tbprofiler_dr_type String Drug resistance type predicted by TB-Profiler (sensitive, Pre-MDR, MDR, Pre-XDR, XDR) ONT, PE, SE tbprofiler_main_lineage String Lineage(s) predicted by TBProfiler ONT, PE, SE tbprofiler_median_coverage Int The median coverage of the H37Rv TB reference genome ONT, PE tbprofiler_output_bai File Index BAM file generated by mapping sequencing reads to reference genome by TBProfiler ONT, PE, SE tbprofiler_output_bam File BAM alignment file produced by TBProfiler ONT, PE, SE tbprofiler_output_file File CSV report from TBProfiler ONT, PE, SE tbprofiler_output_vcf File VCF file output from TBProfiler; the concatenation of all of the different VCF files produced during TBProfiler analysis ONT, PE, SE tbprofiler_pct_reads_mapped Float The percentage of reads mapped to the H37Rv TB reference genome ONT, PE tbprofiler_resistance_genes String List of resistance mutations detected by TBProfiler ONT, PE, SE tbprofiler_sub_lineage String Sub-lineage(s) predicted by TBProfiler ONT, PE, SE tbprofiler_version String Version of TBProfiler used ONT, PE, SE theiaprok_fasta_analysis_date String Date of TheiaProk FASTA workflow execution FASTA theiaprok_fasta_version String Version of TheiaProk FASTA workflow execution FASTA theiaprok_illumina_pe_analysis_date String Date of TheiaProk PE workflow execution PE theiaprok_illumina_pe_version String Version of TheiaProk PE workflow execution PE theiaprok_illumina_se_analysis_date String Date of TheiaProk SE workflow execution SE theiaprok_illumina_se_version String Version of TheiaProk SE workflow execution SE theiaprok_ont_analysis_date String Date of TheiaProk ONT workflow execution ONT theiaprok_ont_version String Version of TheiaProk ONT workflow execution ONT tiptoft_plasmid_replicon_fastq File File produced by tiptoft that contains reads containing plasmid rep/inc genes ONT tiptoft_plasmid_replicon_genes String Rep/inc genes found in sample ONT tiptoft_version String Version of tiptoft used for analysis ONT trimmomatic_docker String Docker image used for trimmomatic PE, SE trimmomatic_version String Version of trimmomatic used PE, SE ts_mlst_allelic_profile String Profile of MLST loci and allele numbers predicted by MLST FASTA, ONT, PE, SE ts_mlst_docker String Docker image used for MLST FASTA, ONT, PE, SE ts_mlst_novel_alleles File FASTA file containing nucleotide sequence of any alleles that are not in the MLST database used by TheiaProk FASTA, ONT, PE, SE ts_mlst_predicted_st String ST predicted by MLST FASTA, ONT, PE, SE ts_mlst_pubmlst_scheme String PubMLST scheme used byMLST FASTA, ONT, PE, SE ts_mlst_results File TSV report with detailed MLST profile, including https://github.com/tseemann/mlst#missing-data FASTA, ONT, PE, SE ts_mlst_version String Version of Torsten Seeman\u2019s MLST tool used FASTA, ONT, PE, SE virulencefinder_docker String VirulenceFinder docker image used FASTA, ONT, PE, SE virulencefinder_hits String Virulence genes detected by VirulenceFinder FASTA, ONT, PE, SE virulencefinder_report_tsv File Output TSV file created by VirulenceFinder FASTA, ONT, PE, SE"},{"location":"workflows/genomic_characterization/vadr_update/","title":"VADR_Update","text":""},{"location":"workflows/genomic_characterization/vadr_update/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Viral PHB v1.2.1 Yes Sample-level"},{"location":"workflows/genomic_characterization/vadr_update/#vadr_update_phb","title":"Vadr_Update_PHB","text":"The VADR_Update workflow updates prior VADR assessments for each sample in line with the assessment criteria in an alternative docker image. This may be useful when samples have previously been subject to VADR alerts as updates to VADR assessment criteria may mean that the sample no longer raises concern about quality. The latest docker image SARS-CoV-2 for VADR can be found\u00a0here.
Various models are available for many organisms. The following table provides an overview of the recommended container to be used and what options should be passed on to VADR.
Organism docker vadr_opts max_length sars-cov-2 \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"--noseqnamemax --glsearch -s -r --nomisc --mkey sarscov2 --lowsim5seq 6 --lowsim3seq 6 --alt_fail lowscore,insertnn,deletinn --out_allfasta\" 30000 MPXV \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"--glsearch -s -r --nomisc --mkey mpxv --r_lowsimok --r_lowsimxd 100 --r_lowsimxl 2000 --alt_pass discontn,dupregin --out_allfasta --minimap2 --s_overhang 150\" 210000 WNV \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"--mkey flavi --mdir /opt/vadr/vadr-models-flavi/ --nomisc --noprotid --out_allfasta\" 11000 flu \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"--atgonly --xnocomp --nomisc --alt_fail extrant5,extrant3 --mkey flu\" 13500 rsv_a \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"-r --mkey rsv --xnocomp\" 15500 rsv_b \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"-r --mkey rsv --xnocomp\" 15500 HAV \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3-hav\" \"-r -xnocomp -mkey hav.vadr\" 10500"},{"location":"workflows/genomic_characterization/vadr_update/#inputs","title":"Inputs","text":"Please note the default values are for SARS-CoV-2.
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status vadr_update assembly_length_unambiguous Int Number of unambiguous basecalls within the consensus assembly Required vadr_update docker String The Docker container to use for the task Required vadr_update genome_fasta File Consensus genome assembly Required vadr cpu Int Number of CPUs to allocate to the task 2 Optional vadr disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional vadr max_length Int Maximum length for the fasta-trim-terminal-ambigs.pl VADR script 30000 Optional vadr memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional vadr min_length Int Minimum length subsequence to possibly replace Ns for the fasta-trim-terminal-ambigs.pl VADR script 50 Optional vadr skip_length Int Minimum assembly length (unambiguous) to run vadr 10000 Optional vadr vadr_opts String Options for the v-annotate.pl VADR script ''--glsearch -s -r --nomisc --mkey sarscov2 --alt_fail lowscore,fstukcnf,insertnn,deletinn --mdir /opt/vadr/vadr-models/'' Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/vadr_update/#outputs","title":"Outputs","text":"Variable Type Description vadr_alerts_list File File containing all of the fatal alerts as determined by VADR vadr_docker String Docker image used to run VADR vadr_fastas_zip_archive File Archive file (in zip format) of all VADR outputs vadr_num_alerts String Number of fatal alerts as determined by VADR vadr_update_analysis_date String Date of analysis vadr_update_version String Version of the Public Health Bioinformatics (PHB) repository used"},{"location":"workflows/phylogenetic_construction/augur/","title":"Augur","text":""},{"location":"workflows/phylogenetic_construction/augur/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Viral PHB v2.1.0 Yes Sample-level, Set-level"},{"location":"workflows/phylogenetic_construction/augur/#augur-workflows","title":"Augur Workflows","text":"Genomic Epidemiology is an important approach in the effort to understand and mitigate against disease transmission. An often-critical step in viral genomic epidemiology is the generation of phylogenetic trees to explore the genetic relationship between viruses on a local, regional, national or global scale. The Augur workflows, currently only targeted for viral pathogens, facilitate this process by generating files for the visualization of phylogenetic trees with accompanying metadata.
Two workflows are offered: Augur_Prep_PHB and Augur_PHB. These must be run sequentially, respectively, to first prepare each individual sample for running Augur, and secondly to run Augur itself on the set of samples, generating the phylogenetic tree files with accompanying metadata. The outputs from these workflows can be visualized in\u00a0Auspice\u00a0and\u00a0UShER.
Helpful resources for epidemiological interpretation
The Augur_Prep_PHB workflow was written to prepare individual sample assemblies and their metadata for running the Augur_PHB analysis.
"},{"location":"workflows/phylogenetic_construction/augur/#augur_prep-inputs","title":"Augur_Prep Inputs","text":"The Augur_Prep_PHB workflow takes assembly FASTA files and associated metadata formatted in a data table. FASTA files may be generated with one of the TheiaCoV Characterization workflows and should adhere to quality control guidelines, (e.g.\u00a0QC guidelines produced by PHA4GE). The metadata can be uploaded to Terra as TSV file, formatted as in this\u00a0example.
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status augur_prep assembly File Assembly/consensus file (single FASTA file per sample) Required augur_prep collection_date String Collection date of the sample Optional augur_prep continent String Continent where sample was collected Optional augur_prep country String Country where sample was collected Optional augur_prep county String County (or smaller locality) where sample was collected Optional augur_prep nextclade_clade String The Nextclade clade of the sample Optional augur_prep pango_lineage String The Pangolin lineage of the sample Optional augur_prep state String State (or province) where sample was collected Optional prep_augur_metadata cpu Int Number of CPUs to allocate to the task 1 Optional prep_augur_metadata disk_size Int Amount of storage (in GB) to allocate to the task 10 Optional prep_augur_metadata docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional prep_augur_metadata memory Int Amount of memory/RAM (in GB) to allocate to the task 3 Optional prep_augur_metadata organism String The organism to be analyzed in Augur; options: \"sars-cov-2\", \"flu\", \"MPXV\", \"rsv-a\", \"rsv-b\" sars-cov-2 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/augur/#augur_prep-outputs","title":"Augur_Prep Outputs","text":"Variable Type Description augur_metadata File TSV file of the metadata provided as input to the workflow in the proper format for Augur analysis augur_prep_phb_analysis_date String Date of analysis augur_prep_phb_version String Version of the Public Health Bioinformatics (PHB) repository used"},{"location":"workflows/phylogenetic_construction/augur/#augur_phb","title":"Augur_PHB","text":"Helpful Hint
You may have to generate phylogenies multiple times, running the Augur_PHB workflow, assessing results, and amending inputs to generate a final tree with sufficient diversity and high-quality data of interest.
The Augur_PHB workflow takes a set of assembly/consensus files (FASTA format) and sample metadata files (TSV format) that have been reformatted using\u00a0Augur_Prep_PHB\u00a0and runs Augur to generate the phylogenetic tree files with accompanying metadata. Additionally, the workflow infers pairwise SNP distances.
"},{"location":"workflows/phylogenetic_construction/augur/#augur-inputs","title":"Augur Inputs","text":"The Augur_PHB workflow takes in a\u00a0set\u00a0of SARS-CoV-2 (or any other viral pathogen) FASTA and metadata files. If running the workflow via Terra, individual samples will need to be added to a set before running the workflow. Input FASTAs should meet QA metrics. Sets of FASTAs with highly discordant quality metrics may result in the inaccurate inference of genetic relatedness. There must be some sequence diversity among the set of input assemblies. If insufficient diversity is present, it may be necessary to add a more divergent sequence to the set.
Optional Inputs
There are many optional user inputs. For SARS-CoV-2, Flu, rsv-a, rsv-b, and mpxv, default values that mimic the NextStrain builds have been preselected. To use these defaults, you must write either \"sars-cov-2\",\"flu\", \"rsv-a\", \"rsv-b\", or \"mpxv\" for the organism variable.
For Flu - it is required to set flu_segment to either \"HA\" or \"NA\" & flu_subtype to either \"H1N1\" or \"H3N2\" or \"Victoria\" or \"Yamagata\" or \"H5N1\" (\"H5N1\" will only work with \"HA\") depending on your set of samples.
\"gs://theiagen-public-files-rp/terra/flu-references/lat_longs.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_h1n1pdm.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h1n1pdm_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/clades_h1n1pdm_ha.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h1n1pdm_na.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_h3n2.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h3n2_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/clades_h3n2_ha.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h3n2_na.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_vic.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_vic_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/clades_vic_ha.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_vic_na.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_yam.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_yam_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/clades_yam_ha.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_yam_na.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_h5n1.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h5n1_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/h5nx-clades.tsv\"\"gs://theiagen-public-files-rp/terra/augur-mpox-references/mpox_clades.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/lat_longs.tsv\"\"gs://theiagen-public-files-rp/terra/augur-mpox-references/NC_063383.1.reference.fasta\"\"gs://theiagen-public-files-rp/terra/augur-mpox-references/NC_063383.1_reference.gb\"\"gs://theiagen-public-files-rp/terra/augur-mpox-references/mpox_auspice_config_mpxv.json\"\"gs://theiagen-public-files-rp/terra/rsv_references/rsv_a_clades.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/lat_longs.tsv\"\"gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_a.fasta\"\"\"gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_a.gb\"\"\"gs://theiagen-public-files-rp/terra/rsv_references/rsv_auspice_config.json\"\"gs://theiagen-public-files-rp/terra/rsv_references/rsv_b_clades.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/lat_longs.tsv\"\"gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_b.fasta\"\"\"gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_b.gb\"\"\"gs://theiagen-public-files-rp/terra/rsv_references/rsv_auspice_config.json\"For more information regarding these optional inputs, please view Nextrain's detailed documentation on Augur
What's required or not?
For organisms other than SARS-CoV-2 or Flu, the required variables have both the \"required\" and \"optional\" tags.
This workflow runs on the set level. Please note that for every task, runtime parameters are modifiable (cpu, disk_size, docker, and memory); most of these values have been excluded from the table below for convenience.
Terra Task Name Variable Type Description Default Value Terra Status augur assembly_fastas Array[File] An array of the assembly files to use; use either the HA or NA segment for flu samples Required augur build_name String Name to give to the Augur build Required augur auspice_config File Auspice config file for customizing visualizations; takes priority over the other customization values available for augur_export Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, a minimal auspice config file is provided to prevent workflow failure, \"gs://theiagen-public-files-rp/terra/augur-defaults/minimal-auspice-config.json\", but will not be as useful as an organism specific config file. Optional augur clades_tsv File TSV file containing clade mutation positions in four columns Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, an empty clades file is provided to prevent workflow failure, \"gs://theiagen-public-files-rp/terra/augur-defaults/minimal-clades.tsv\", but will not be as useful as an organism specific clades file. Optional, Required augur distance_tree_only Boolean Create only a distance tree (skips all Augur steps after augur_tree) TRUE Optional augur flu_segment String Required if organism = \"flu\". The name of the segment to be analyzed; options: \"HA\" or \"NA\" \"HA\" (only used if organism = \"flu\") Optional, Required augur flu_subtype String Required if organism = \"flu\". The subtype of the flu samples being analyzed; options: \"H1N1\", \"H3N2\", \"Victoria\", \"Yamagata\", \"H5N1\" Optional, Required augur lat_longs_tsv File Tab-delimited file of geographic location names with corresponding latitude and longitude values Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, a minimal lat-long file is provided to prevent workflow failure, \"gs://theiagen-public-files-rp/terra/augur-defaults/minimal-lat-longs.tsv\", but will not be as useful as a detailed lat-longs file covering all the locations for the samples to be visualized. Optional augur min_date Float Minimum date to begin filtering or frequencies calculations Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default value is 0.0 Optional augur min_num_unambig Int Minimum number of called bases in genome to pass prefilter Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default value is 0 Optional augur organism String Organism used to preselect default values; options: \"sars-cov-2\", \"flu\", \"mpxv\", \"rsv-a\", \"rsv-b\" sars-cov-2 Optional augur reference_fasta File The reference FASTA file used to align the genomes and build the trees Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, a reference fasta file must be provided otherwise the workflow fails. Optional, Required augur reference_genbank File The GenBank .gb file for the same reference genome used for the reference_fasta Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, a reference genbank file must be provided otherwise the workflow fails. Optional, Required augur sample_metadata_tsvs Array[File] An array of the metadata files produced in Augur_Prep_PHB Optional augur build_name_updated String Internal component, do not modify. Used for replacing spaces with underscores _ Do Not Modify augur_align fill_gaps Boolean If true, gaps represent missing data rather than true indels and so are replaced by N after aligning. FALSE Optional augur_ancestral infer_ambiguous Boolean If true, infer nucleotides and ambiguous sites and replace with most likely FALSE Optional augur_ancestral inference String Calculate joint or marginal maximum likelihood ancestral sequence states; options: \"joint\", \"marginal\" joint Optional augur_ancestral keep_ambiguous Boolean If true, do not infer nucleotides at ambiguous (N) sides FALSE Optional augur_ancestral keep_overhangs Boolean If true, do not infer nucleotides for gaps on either side of the alignment FALSE Optional augur_export colors_tsv File Custom color definitions, one per line in TSV format with the following fields: TRAIT_TYPE TRAIT_VALUE HEX_CODE Optional augur_export description_md File Markdown file with description of build and/or acknowledgements Optional augur_export include_root_sequence Boolean Export an additional JSON containing the root sequence used to identify mutations FALSE Optional augur_export title String Title to be displayed by Auspice Optional augur_refine branch_length_inference String Branch length mode of timetree to use; options: \"auto\", \"joint\", \"marginal\", \"input\" auto Optional augur_refine clock_filter_iqd Int Remove tips that deviate more than n_iqd interquartile ranges from the root-to-tip vs time regression 4 Optional augur_refine clock_rate Float Fixed clock rate to use for time tree calculations Optional augur_refine clock_std_dev Float Standard deviation of the fixed clock_rate estimate Optional augur_refine coalescent String Coalescent time scale in units of inverse clock rate (float), optimize as scalar (\"opt\") or skyline (\"skyline\") Optional augur_refine covariance Boolean If true, account for covariation when estimating rates and/or rerooting TRUE Optional augur_refine date_confidence Boolean If true, calculate confidence intervals for node dates TRUE Optional augur_refine date_inference String Assign internal nodes to their marginally most likely dates; options: \"joint\", \"marginal\" marginal Optional augur_refine divergence_units String Units in which sequence divergences is exported; options: \"mutations\" or \"mutations-per-site\" mutations Optional augur_refine gen_per_year Int Number of generations per year 50 Optional augur_refine keep_polytomies Boolean If true, don't attempt to resolve polytomies FALSE Optional augur_refine keep_root Boolean If true, do not reroot the tree; use it as-is (overrides anything specified by root) TRUE Optional augur_refine precision String Precision used to determine the number of grid points; options: 0 (rough) to 3 (ultra fine) auto Optional augur_refine root String Rooting mechanism; options: \"best\", \"least-squares\", \"min_dev\", \"oldest\", etc. Optional augur_translate genes File A file containing a list of genes to translate (from nucleotides to amino acids) Optional augur_tree exclude_sites File File of one-based sites to exclude for raw tree building (BED format in .bed files, DRM format in tab-delimited files, or one position per line) Optional augur_tree method String Which method to use to build the tree; options: \"fasttree\", \"raxml\", \"iqtree\" iqtree Optional augur_tree override_default_args Boolean If true, override default tree builder arguments instead of augmenting them FALSE Optional augur_tree substitution_model String The substitution model to use; only available for iqtree. Specify \"auto\" to run ModelTest; model options can be found here GTR Optional augur_tree tree_builder_args String Additional tree builder arguments either augmenting or overriding the default arguments. FastTree defaults: \"-nt -nosupport\". RAxML defaults: \"-f d -m GTRCAT -c 25 -p 235813\". IQ-TREE defaults: \"-ninit 2 -n 2 -me 0.05 -nt AUTO -redo\" Optional sc2_defaults nextstrain_ncov_repo_commit String The version of the from which to draw default values for SARS-CoV-2.23d1243127e8838a61b7e5c1a72bc419bf8c5a0d Optional organism_parameters gene_locations_bed_file File Use to provide locations of interest where average coverage will be calculated Defaults are organism-specific. Please find default values for some organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, an empty file is provided, \"gs://theiagen-public-files/terra/theiacov-files/empty.bed\", but will not be as useful as an organism specific gene locations bed file. Optional organism_parameters genome_length_input Int Use to specify the expected genome length; provided by default for all supported organisms Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the genome length input must be provided otherwise the workflow fails. Optional, Required organism_parameters hiv_primer_version String The version of HIV primers used. Options are https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl#L156 and https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl#L164. This input is ignored if provided for TheiaCoV_Illumina_SE and TheiaCoV_ClearLabs v1 Optional organism_parameters kraken_target_organism_input String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is \"\". Optional organism_parameters nextclade_dataset_name_input String NextClade organism dataset name Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is \"NA\". Optional organism_parameters nextclade_dataset_tag_input String NextClade organism dataset tag Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is \"NA\". Optional organism_parameters pangolin_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.26 Optional organism_parameters primer_bed_file File The bed file containing the primers used when sequencing was performed Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, an empty primer bed file is provided, \"gs://theiagen-public-files/terra/theiacov-files/empty.bed\", but will not be as useful as an organism specific primer bed file. Optional organism_parameters reference_gff_file File Reference GFF file for the organism being analyzed Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, an empty gff file is provided, \"gs://theiagen-public-files/terra/theiacov-files/empty.gff3\", but will not be as useful as an organism specific gff file. Optional organism_parameters vadr_max_length Int Maximum length for the fasta-trim-terminal-ambigs.pl VADR script Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is 0. Optional organism_parameters vadr_mem Int Memory, in GB, allocated to this task 32 (RSV-A and RSV-B) and 8 (all other TheiaCoV organisms) organism_parameters vadr_options String Options for the v-annotate.pl VADR script Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is \"NA\". Optional organism_parameters vadr_skip_length Int Minimum assembly length (unambiguous) to run VADR Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is 0. Optional mutation_context cpu Int CPUs requested for the mutation_context task that is specific to Mpox. 1 Optional mutation_context disk_size Int Disk size in GB requested for the mutation_context task that is specific to Mpox. 50 Optional mutation_context docker String Docker image used for the mutation_context task that is specific to Mpox. Do not modify. us-docker.pkg.dev/general-theiagen/theiagen/nextstrain-mpox-mutation-context:2024-06-27 Do Not Modify, Optional mutation_context memory Int Memory size in GB requested for the mutation_context task that is specific to Mpox. 4 Optional Workflow Tasks"},{"location":"workflows/phylogenetic_construction/augur/#augur-tasks","title":"Augur Workflow Tasks","text":"The Augur_PHB workflow uses the inputs to generate a phylogenetic tree in JSON format that is compatible with phylogenetic tree visualization software.
In Augur_PHB, the tasks below are called. For the Augur subcommands, please view the Nextstrain Augur documentation for more details and explanations.
cat_files - concatenate all of the input fasta files togethersc2_defaults - if organism is SARS-CoV-2, establish default parametersflu_defaults - if organism is Flu, establish default parametersfilter_sequences_by_length - remove any sequences that do not meet the quality threshold set by min_num_unambigtsv_join - merge the metadata filesfasta_to_ids - extract a list of remaining sequences so we know which ones were droppedaugur_align - perform MAFFT alignment on the sequencesaugur_tree - create a distance treeaugur_refine - create a timetreeaugur_ancestral - infer ancestral sequencesaugur_translate - translate gene regions from nucleotides to amino acidsmutation_context - if organism is MPXV, calculates the mutation fraction of G->A or C->T changesaugur_clades - if clade information is provided, assign clades to nodes based on amino-acid or nucleotide signaturesaugur_export - export all the results in a JSON file suitable for Auspice visualizationsnp_dists - create a SNP matrix from the alignmentreorder_matrix - reorder the SNP matrix to match the distance treeDiversity dependent
Note that the node & branch coloring by clade or lineage assignment might be dependent on the diversity of your input dataset. This is because the clade assignment is done using the ancestrally reconstructed amino acid or nucleotide changes at the tree nodes rather than a direct sequence-to-reference mutation comparison. You may notice this happening when you get clade/lineage assignments from NextClade when running TheiaCoV workflows, but no clade/lineage assignment on the Augur Auspice tree.
To get around this issue, you can upload the Augur output file merged-metadata.tsv to Auspice that includes the correct clade/lineage assignments to allow for coloring by Clade.
Flu clade assignments
Note that for flu, the clade assignment is usually mostly done for the more recent seasonal influenza viruses. Older strains may get an \"unassigned\" designation for clades. Therefore, it is important to counter check with the NextClade results from TheiaCoV if the lack of clade assignment is due to analyzing older sequences or sequence related.
The auspice_input_json is intended to be uploaded to\u00a0Auspice\u00a0to view the phylogenetic tree. This provides a visualization of the genetic relationships between your set of samples. The metadata_merged output can also be uploaded to add context to the phylogenetic visualization. The combined_assemblies output can be uploaded to\u00a0UShER\u00a0to view the samples on a global tree of representative sequences from the public repositories.
The Nextstrain team hosts documentation surrounding the Augur workflow \u2192 Auspice visualization here, which details the various components of the Auspice interface: How data is exported by Augur for visualisation in Auspice.
Variable Type Description aligned_fastas File A FASTA file of the aligned genomes augur_fasttree_version String The fasttree version used, blank if other tree method used augur_iqtree_model_used String The iqtree model used during augur tree, blank if iqtree not used augur_iqtree_version String The iqtree version used during augur tree (defualt), blank if other tree method used augur_mafft_version String The mafft version used in augur align augur_phb_analysis_date String The date the analysis was run augur_phb_version String The version of the Public Health Bioinformatics (PHB) repository used augur_raxml_version String The version of raxml used during augur tree, blank if other tree method used augur_version String Version of Augur used auspice_input_json File JSON file used as input to Auspice combined_assemblies File Concatenated FASTA file containing all samples distance_tree File The distance tree created in Newick (.nwk) format keep_list File A list of samples included in the phylogenetic tree metadata_merged File Tab-delimited text file of the merged augur_metadata input files from all samples snp_matrix File The SNP distance matrix for all samples used in the phylogenetic tree time_tree File The time tree created in Newick (.nwk) format traits_json File A JSON file containing sample traits"},{"location":"workflows/phylogenetic_construction/augur/#mpox-specific-auspice-output-json","title":"Mpox-specific Auspice Output JSON","text":"If you are building a tree for Mpox samples and set the optional input parameter organism to \"mpox\" , an additional step will be carried out in the Augur_PHB workflow. This additional step will calculate the mutation fraction of G\u2192A or C\u2192T changes. These mutations have been shown to be a characteristic of APOBEC3-type editing, which indicate adaptation of the virus to circulation among humans as was observed with the 2022 clade IIb outbreak, and more recently (2024) with the clade Ib outbreak in South Kivu, Democratic Republic of the Congo.
When visualizing the output auspice_input_json file, there will be 2 new choices in the drop-down menu for \"Color By\":
An example Mpox tree with these \"Color By\" options can be viewed here: https://nextstrain.org/mpox/clade-IIb?c=GA_CT_fraction
"},{"location":"workflows/phylogenetic_construction/augur/#references","title":"References","text":"When publishing work using the Augur_PHB workflow, please reference the following:
Nextstrain:\u00a0Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, Sagulenko P, Bedford T, Neher RA. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics. 2018 Dec 1;34(23):4121-3.
When publishing work using inferences from UShER, please reference:
UShER:\u00a0Turakhia Y, Thornlow B, Hinrichs AS, De Maio N, Gozashti L, Lanfear R, Haussler D, Corbett-Detig R. Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic. Nature Genetics. 2021 Jun;53(6):809-16.
"},{"location":"workflows/phylogenetic_construction/core_gene_snp/","title":"Core_Gene_SNP","text":""},{"location":"workflows/phylogenetic_construction/core_gene_snp/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.1.0 Yes, some optional features incompatible Set-level"},{"location":"workflows/phylogenetic_construction/core_gene_snp/#core_gene_snp_phb","title":"Core_Gene_SNP_PHB","text":"Core Gene SNP Workflow Diagram
The Core_Gene_SNP workflow is intended for pangenome analysis, core gene alignment, and phylogenetic analysis. The workflow takes in gene sequence data in GFF3 format from a set of samples. It first produces a pangenome summary using Pirate, which clusters genes within the sample set into orthologous gene families. By default, the workflow also instructs Pirate to produce both core gene and pangenome alignments. The workflow subsequently triggers the generation of a phylogenetic tree and SNP distance matrix from the core gene alignment using iqtree and snp-dists, respectively. Optionally, the workflow will also run this analysis using the pangenome alignment. This workflow also features an optional module, summarize_data, that creates a presence/absence matrix for the analyzed samples from a list of indicated columns (such as AMR genes, etc.) that can be used in Phandango.
Default Parameters
Please note that while default parameters for pangenome construction and phylogenetic tree generation are provided, these default parameters may not suit every dataset and have not been validated against known phylogenies. Users should take care to select the parameters that are most appropriate for their dataset. Please reach out to support@theiagen.com or one of the other resources listed at the bottom of this page if you would like assistance with this task.
"},{"location":"workflows/phylogenetic_construction/core_gene_snp/#inputs","title":"Inputs","text":"For further detail regarding Pirate options, please see [PIRATE's documentation)[https://github.com/SionBayliss/PIRATE). For further detail regarding IQ-TREE options, please see http://www.iqtree.org/doc/Command-Reference.
This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status core_gene_snp_workflow cluster_name String Name of sample set Required core_gene_snp_workflow gff3 Array[File] Array of gff3 files to include in analysis, output gff files from both prokka and bakta using TheiaProk workflows are compatible Required core_gene_snp_workflow midpoint_root_tree Boolean Boolean variable that will instruct the workflow to reroot the tree at the midpoint FALSE Optional core_gene_snp_workflow phandango_coloring Boolean Boolean variable that tells the data summary task and the reorder matrix task to include a suffix that enables consistent coloring on Phandango; by default, this suffix is not added. To add this suffix set this variable to true. FALSE Optional core_gene_snp_workflow data_summary_terra_table String The name of the Terra data table that you want data pulled from Optional core_gene_snp_workflow data_summary_column_names String A comma-delimited list of columns in the origin data table that contains contain that you would like a presence/absence .csv matrix generated for Optional core_gene_snp_workflow core_tree Boolean Boolean variable that instructs the workflow to create a phylogenetic tree and SNP distance matrix from the core gene alignment. Align must also be set to true. TRUE Optional core_gene_snp_workflow pan_tree Boolean Boolean variable that instructs the workflow to create a phylogenetic tree and SNP distance matrix from the pangenome alignment. Align must also be set to true. FALSE Optional core_gene_snp_workflow data_summary_terra_workspace String The name of the current Terra workspace you are in; this can be found at the top of the webpage, or in the URL after the billing project. Optional core_gene_snp_workflow align Boolean Boolean variable that instructs the workflow to generate core and pangenome alignments if \"true\". If \"false\", the workflow will produce only a pangenome summary. TRUE Optional core_gene_snp_workflow data_summary_terra_project String The billing project for the current workspace; can be found after the \"#workspaces/\" section in the workflow's URL Optional core_gene_snp_workflow sample_names Array[String] Array of sample_ids from the data table used Optional core_iqtree memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional core_iqtree disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional core_iqtree cpu Int Number of CPUs to allocate to the task 4 Optional core_iqtree docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/iqtree:1.6.7 Optional core_iqtree iqtree_model String Substitution model, frequency type (optional) and rate heterogeneity type (optional) used by IQ-TREE. This string follows the IQ-TREE \"-m\" option. For comparison to other tools use HKY for Bactopia, GTR+F+I for Grandeur, GTR+G4 for Nullarbor, GTR+G for Dryad GTR+I+G Optional core_iqtree iqtree_opts String Additional options for IQ-TREE, see http://www.iqtree.org/doc/Command-Reference Optional core_iqtree iqtree_bootstraps String Number of ultrafast bootstrap replicates. Follows IQ-TREE \"-bb\" option. 1000 Optional core_iqtree alrt String Number of replicates to perform SH-like approximate likelihood ratio test (SH-aLRT). Follows IQ-TREE \"-alrt\" option 1000 Optional core_reorder_matrix cpu Int Number of CPUs to allocate to the task 2 Optional core_reorder_matrix disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional core_reorder_matrix docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional core_reorder_matrix memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional core_snp_dists memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional core_snp_dists docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional core_snp_dists disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional core_snp_dists cpu Int Number of CPUs to allocate to the task 1 Optional pan_iqtree memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional pan_iqtree disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pan_iqtree cpu Int Number of CPUs to allocate to the task 4 Optional pan_iqtree alrt String Number of replicates to perform SH-like approximate likelihood ratio test (SH-aLRT). Follows IQ-TREE \"-alrt\" option 1000 Optional pan_iqtree iqtree_model String Substitution model, frequency type (optional) and rate heterogeneity type (optional) used by IQ-TREE. This string follows the IQ-TREE \"-m\" option. For comparison to other tools use HKY for Bactopia, GTR+F+I for Grandeur, GTR+G4 for Nullarbor, GTR+G for Dryad GTR+I+G Optional pan_iqtree iqtree_bootstraps String Number of ultrafast bootstrap replicates. Follows IQ-TREE \"-bb\" option. 1000 Optional pan_iqtree iqtree_opts String Additional options for IQ-TREE, see http://www.iqtree.org/doc/Command-Reference Optional pan_iqtree docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/iqtree:1.6.7 Optional pan_reorder_matrix cpu Int Number of CPUs to allocate to the task 2 Optional pan_reorder_matrix disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pan_reorder_matrix docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional pan_reorder_matrix memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional pan_snp_dists cpu Int Number of CPUs to allocate to the task 1 Optional pan_snp_dists disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional pan_snp_dists docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional pan_snp_dists memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional pirate disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pirate cpu Int Number of CPUs to allocate to the task 4 Optional pirate nucl Boolean Boolean variable that instructs pirate to create a pangenome on CDS features using nucleotide identity, rather than amino acid identity, if true. FALSE Optional pirate memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional pirate panopt String Additional arguments for Pirate Optional pirate docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/pirate:1.0.5--hdfd78af_0 Optional pirate features String Features to use for pangenome construction [default: CDS] CDS Optional pirate steps String Identity thresholds to use for pangenome construction 50,60,70,80,90,95,98 Optional summarize_data disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional summarize_data docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16 Optional summarize_data memory Int Amount of memory/RAM (in GB) to allocate to the task 1 Optional summarize_data id_column_name String Use in the case your sample IDs are not in the table ID column 1 Optional summarize_data cpu Int Number of CPUs to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/core_gene_snp/#workflow-tasks","title":"Workflow Tasks","text":"By default, the Core_Gene_SNP workflow will begin by analyzing the input sample set using PIRATE. Pirate takes in GFF3 files and classifies the genes into gene families by sequence identity, outputting a pangenome summary file. The workflow will instruct Pirate to create core gene and pangenome alignments using this gene family data. Setting the \"align\" input variable to false will turn off this behavior, and the workflow will output only the pangenome summary. The workflow will then use the core gene alignment from Pirate to infer a phylogenetic tree using IQ-TREE. It will also produce an SNP distance matrix from this alignment using snp-dists. This behavior can be turned off by setting the core_tree input variable to false. The workflow will not create a pangenome tree or SNP-matrix by default, but this behavior can be turned on by setting the pan_tree input variable to true.
The optional summarize_data task performs the following only if all of the data_summary_* and sample_names optional variables are filled out:
\"amrfinderplus_virulence_genes,amrfinderplus_stress_genes\", etc. that can be found within the origin Terra data table.amrfinder_amr_genes column for a sample contains these values: \"aph(3')-IIIa,tet(O),blaOXA-193\", the summarize_data task will check each sample in the set to see if they also have those AMR genes detected.By default, this task appends a Phandango coloring tag to color all items from the same column the same; this can be turned off by setting the optional phandango_coloring variable to false.
Sion C Bayliss, Harry A Thorpe, Nicola M Coyle, Samuel K Sheppard, Edward J Feil, PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria,\u00a0GigaScience, Volume 8, Issue 10, October 2019, giz119,\u00a0https://doi.org/10.1093/gigascience/giz119
Lam-Tung Nguyen, Heiko A. Schmidt, Arndt von Haeseler, Bui Quang Minh, IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies,\u00a0Molecular Biology and Evolution, Volume 32, Issue 1, January 2015, Pages 268\u2013274,\u00a0https://doi.org/10.1093/molbev/msu300
https://github.com/tseemann/snp-dists
"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/","title":"CZGenEpi_Prep","text":""},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Viral PHB v1.3.0 No Set-level"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#czgenepi_prep_phb","title":"CZGenEpi_Prep_PHB","text":"The CZGenEpi_Prep workflow prepares data for upload to the Chan Zuckerberg GEN EPI platform, where phylogenetic trees and additional data processing can occur. This workflow extracts the necessary metadata fields from your Terra table.
"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#inputs","title":"Inputs","text":"In order to enable customization for where certain fields should be pulled from the Terra table, the user can specify different column names in the appropriate location. For example, if the user wants to use the \"clearlabs_fasta\" column for the assembly file instead of the default \"assembly_fasta\" column, they can write \"clearlabs_fasta\" for the assembly_fasta_column_name optional variable.
Variables with both the \"Optional\" and \"Required\" tag require the column (regardless of name) to be present in the data table.
This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status czgenepi_prep sample_names Array[String] The array of sample ids you want to prepare for CZ GEN EPI Required czgenepi_prep terra_table_name String The name of the Terra table where the data is hosted Required czgenepi_prep terra_project_name String The name of the Terra project where the data is hosted Required czgenepi_prep terra_workspace_name String The name of the Terra workspace where the data is hosted Required download_terra_table memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional download_terra_table docker String The Docker container to use for the task quay.io/theiagen/terra-tools:2023-06-21 Optional download_terra_table disk_size String The size of the disk used when running this task 1 Optional download_terra_table cpu Int Number of CPUs to allocate to the task 1 Optional czgenepi_prep assembly_fasta_column_name String The column name where the sample's assembly file can be found assembly_fasta Optional, Required czgenepi_prep county_column_name String The column name where the samples' originating county can be found county Optional, Required czgenepi_prep organism String The organism for data preparation. Options: \"mpox\" or \"sars-cov-2\" sars-cov-2 Optional czgenepi_prep is_private Boolean Sets whether sample status is provate, or not true Optional czgenepi_prep genbank_accession_column_name String The column name where the genbank accession for the sample can be found genbank_accession Optional czgenepi_prep country_column_name String The column name where the sample's originating country can be found country Optional, Required czgenepi_prep collection_date_column_name String The column name where the sample's collection date can be found collection_date Optional, Required czgenepi_prep state_column_name String The column name where the sample's originating state can be found state Optional, Required czgenepi_prep continent_column_name String The column name where the sample's originating continent can be found continent Optional, Required czgenepi_prep sequencing_date_column_name String The column name where the sample's sequencing data can be found sequencing_date Optional czgenepi_prep private_id_column_name String The column name where the Private ID for the sample can be found terra_table_name_id Optional, Required czgenepi_wrangling memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional czgenepi_wrangling docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-08-08-2 Optional czgenepi_wrangling disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional czgenepi_wrangling cpu Int Number of CPUs to allocate to the task 1 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#outputs","title":"Outputs","text":"The concatenated_czgenepi_fasta and concatenated_czgenepi_metadata files can be uploaded directly to CZ GEN EPI without any adjustments.
Variable Type Description concatenate_czgenepi_fasta File The concatenated fasta file with the renamed headers (the headers are renamed to account for clearlabs data which has unique headers) concatenate_czgenepi_metadata File The concatenated metadata that was extracted from the terra table using the specified columns czgenepi_prep_version String The version of PHB the workflow is in czgenepi_prep_analysis_date String The date the workflow was run"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#references","title":"References","text":"CZ GEN EPI Help Center \"Uploading Data\" https://help.czgenepi.org/hc/en-us/articles/6160372401172-Uploading-data
"},{"location":"workflows/phylogenetic_construction/find_shared_variants/","title":"Find_Shared_Variants","text":""},{"location":"workflows/phylogenetic_construction/find_shared_variants/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria, Mycotics PHB v2.0.0 Yes Set-level"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#find_shared_variants_phb","title":"Find_Shared_Variants_PHB","text":"Find_Shared_Variants_PHB is a workflow for concatenating the variant results produced by the Snippy_Variants_PHB workflow across multiple samples and reshaping the data to illustrate variants that are shared among multiple samples.
Find_Shared_Variants Workflow Diagram
"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#inputs","title":"Inputs","text":"The primary intended input of the workflow is the snippy_variants_results output from Snippy_Variants_PHB or the theiaeuk_snippy_variants_results output of the TheiaEuk workflow. Variant results files from other tools may not be compatible at this time.
All variant data included in the sample set should be generated from aligning sequencing reads to the same reference genome. If variant data was generated using different reference genomes, shared variants cannot be identified and results will be less useful.
Terra Task Name Variable Type Description Default Value Terra Status shared_variants_wf concatenated_file_name String String of your choice to prefix output files Required shared_variants_wf samplenames Array[String] The samples to be included in the analysis Required shared_variants_wf variants_to_cat Array[File] The result file from the Snippy_Variants workflow Required cat_variants docker_image String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional shared_variants cpu Int Number of CPUs to allocate to the task 1 Optional shared_variants disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional shared_variants docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16\" Optional shared_variants memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#tasks","title":"Tasks","text":"Concatenate Variants Shared Variants Task"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#concatenate_variants_task","title":"Concatenate Variants Task","text":"The cat_variants task concatenates variant data from multiple samples into a single file concatenated_variants. It is very similar to the cat_files task, but also adds a column to the output file that indicates the sample associated with each row of data.
The concatenated_variants file will be in the following format:
Technical Details
Links Task /tasks/utilities/file_handling/task_cat_files.wdl Software Source Code task_cat_files.wdl"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#shared_variants_task","title":"Shared Variants Task","text":"The shared_variants task takes in the concatenated_variants output from the cat_variants task and reshapes the data so that variants are rows and samples are columns. For each variant, samples where the variant was detected are populated with a \"1\" and samples were either the variant was not detected or there was insufficient coverage to call variants are populated with a \"0\". The resulting table is available as the shared_variants_table output.
The shared_variants_table file will be in the following format:
Technical Details
Links Task task_shared_variants.wdl Software Source Code task_shared_variants.wdl"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#outputs","title":"Outputs","text":"The outputs of this workflow are the concatenated_variants file and the shared_variants_table file.
The kSNP3 workflow is for phylogenetic analysis of bacterial genomes using single nucleotide polymorphisms (SNPs). The kSNP3 workflow identifies SNPs amongst a set of genome assemblies, then calculates a number of phylogenetic trees based on those SNPs:
_pan._core.This workflow also features an optional module, summarize_data that creates a presence/absence matrix for the analyzed samples from a list of indicated columns (such as AMR genes, plasmid types etc.). If the phandango_coloring variable is set to true, this will be formatted for visualization in Phandango, else it can be viewed in Excel.
You can learn more about the kSNP3 workflow, including how to visualize the outputs with MicrobeTrace in the following video: \ud83d\udcfa Using KSNP3 in Terra and Visualizing Bacterial Genomic Networks in MicrobeTrace
"},{"location":"workflows/phylogenetic_construction/ksnp3/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status ksnp3_workflow assembly_fasta Array[File] The assembly files to be analyzed Required ksnp3_workflow cluster_name String Free text string used to label output files Required ksnp3_workflow samplename Array[String] The set of sample names Required core_ksnp3_shared_snps_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional core_reorder_matrix cpu Int Number of CPUs to allocate to the task 2 Optional core_reorder_matrix disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional core_reorder_matrix docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional core_reorder_matrix memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional core_snp_dists cpu Int Number of CPUs to allocate to the task 1 Optional core_snp_dists disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional core_snp_dists docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional core_snp_dists memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ksnp3_task cpu Int Number of CPUs to allocate to the task 4 Optional ksnp3_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ksnp3_task docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ksnp3:3.1 Optional ksnp3_task kmer_size Int The length of kmer containing the SNP you want kSNP3 to use 19 Optional ksnp3_task ksnp3_args String Additional arguments you want kSNP3 to use; e.g., \"-ML\" or \"-NJ\" Optional ksnp3_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional ksnp3_task previous_ksnp3_snps File File with existing SNPs for the current run to be appended to. Optional ksnp3_workflow data_summary_column_names String A comma-separated list of the column names from the sample-level data table for generating a data summary (presence/absence .csv matrix); e.g., \"amrfinderplus_amr_genes,amrfinderplus_virulence_genes\" Optional ksnp3_workflow data_summary_terra_project String The billing project for your current workspace. This can be found after the \"#workspaces/\" section in the workspace's URL Optional ksnp3_workflow data_summary_terra_table String The name of the sample-level Terra data table that will be used for generating a data summary Optional ksnp3_workflow data_summary_terra_workspace String The name of the Terra workspace you are in. This can be found at the top of the webpage, or in the URL after the billing project. Optional ksnp3_workflow midpoint_root_tree Boolean If true, midpoint root the final tree FALSE Optional ksnp3_workflow phandango_coloring Boolean Boolean variable that tells the data summary task and the reorder matrix task to include a suffix that enables consistent coloring on Phandango; by default, this suffix is not added. To add this suffix set this variable to true. FALSE Optional pan_reorder_matrix cpu Int Number of CPUs to allocate to the task 100 Optional pan_reorder_matrix disk_size Int Amount of storage (in GB) to allocate to the task 2 Optional pan_reorder_matrix docker String The Docker container to use for the task 100 Optional pan_reorder_matrix memory Int Amount of memory/RAM (in GB) to allocate to the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional pan_snp_dists cpu Int Number of CPUs to allocate to the task 1 Optional pan_snp_dists disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional pan_snp_dists docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional pan_snp_dists memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional summarize_data cpu Int Number of CPUs to allocate to the task 8 Optional summarize_data disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional summarize_data docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16 Optional summarize_data id_column_name String If the sample IDs are in a different column to samplenames, it can be passed here and it will be used instead. Optional summarize_data memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/ksnp3/#workflow-actions","title":"Workflow Actions","text":"The ksnp3 workflow is run on the set of assembly files to produce both pan-genome and core-genome phylogenies. This also results in alignment files which - are used by snp-dists to produce a pairwise SNP distance matrix for both the pan-genome and core-genomes.
If you fill out the data_summary_* and sample_names optional variables, you can use the optional summarize_data task. The task takes a comma-separated list of column names from the Terra data table, which should each contain a list of comma-separated items. For example, \"amrfinderplus_virulence_genes,amrfinderplus_stress_genes\" (with quotes, comma separated, no spaces) for these output columns from running TheiaProk. The task checks whether those comma-separated items are present in each row of the data table (sample), then creates a CSV file of these results. The CSV file indicates presence (TRUE) or absence (empty) for each item. By default, the task adds a Phandango coloring tag to group items from the same column, but you can turn this off by setting phandango_coloring to false.
Sample_Name,aph(3')-IIa,blaCTX-M-65,blaOXA-193,tet(O)\nsample1,TRUE,,TRUE,TRUE\nsample2,,,FALSE,TRUE\nsample3,,,FALSE,\nData summary produced using the phandango_coloring option, visualized alongside Newick tree at http://jameshadfield.github.io/phandango/#/main
Example phandango_coloring output
"},{"location":"workflows/phylogenetic_construction/ksnp3/#outputs","title":"Outputs","text":"Variable Type Description ksnp3_core_snp_matrix File The SNP matrix made with the core genome; formatted for Phandango ifphandango_coloring input is true ksnp3_core_snp_matrix_status String Will print either The core SNP matrix was produced OR The core SNP matrix could not be produced ksnp3_core_snp_table File Formatted version of ksnp3_vcf_ref_genome file with only core SNPs, sorted by number of occurrences in the sample set ksnp3_core_tree File The phylogenetic tree made with the core genome ksnp3_docker String The docker image used ksnp3_filtered_metadata File Optional output file with filtered metadata that is only produced if the optional summarize_data task is used. ksnp3_ml_tree File Maximum likelihood tree that is only produced if ksnp3_args includes \"-ML\" ksnp3_nj_tree File Neighbor joining tree that is only produced if ksnp3_args includes \"-NJ\" ksnp3_number_core_snps String Number of core SNPs in the sample set ksnp3_number_snps String Number of SNPs in the sample set ksnp3_pan_snp_matrix File The SNP matrix made with the pangenome; formatted for Phandango if phandango_coloring input is true ksnp3_pan_tree File The phylogenetic tree made with the pangenome ksnp3_snp_dists_version String The version of snp_dists used in the workflow ksnp3_snps File File containing the set of SNPs used in the analysis. Required if more trees are to be appended to the existing one. ksnp3_summarized_data File CSV presence/absence matrix generated by the summarize_data task from the list of columns provided; formatted for Phandango if phandango_coloring input is true ksnp3_vcf_ref_genome File A VCF file containing the variants detected in the core genome ksnp3_vcf_ref_samplename String The name of the (user-supplied) sample used as the reference for calling SNPs. ksnp3_vcf_snps_not_in_ref File A TSV file of the SNPs not present in the reference genome, but were identified by kSNP3. ksnp3_wf_analysis_date String The date the workflow was run ksnp3_wf_version String The version of the repository the workflow is hosted in"},{"location":"workflows/phylogenetic_construction/ksnp3/#references","title":"References","text":"Shea N Gardner, Tom Slezak, Barry G. Hall, kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome,\u00a0Bioinformatics, Volume 31, Issue 17, 1 September 2015, Pages 2877\u20132878,\u00a0https://doi.org/10.1093/bioinformatics/btv271
https://github.com/tseemann/snp-dists
"},{"location":"workflows/phylogenetic_construction/lyve_set/","title":"Lyve_SET","text":""},{"location":"workflows/phylogenetic_construction/lyve_set/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.1.0 Yes Set-level"},{"location":"workflows/phylogenetic_construction/lyve_set/#lyve_set_phb","title":"Lyve_SET_PHB","text":"The Lyve_SET WDL workflow runs the Lyve-SET pipeline developed by Lee Katz et al. for phylogenetic analysis of bacterial genomes using high quality single nucleotide polymorphisms (hqSNPs). The Lyve_SET workflow identifies SNPs amongst a set of samples by mapping sequencing reads to a reference genome, identifying high quality SNPs, and inferring phylogeny using RAxML.
"},{"location":"workflows/phylogenetic_construction/lyve_set/#lyve-set-pipeline-from-lyve-set-paper","title":"Lyve-SET Pipeline (from Lyve-SET paper)","text":"Lyve-SET Workflow Diagram
"},{"location":"workflows/phylogenetic_construction/lyve_set/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status lyveset_workflow dataset_name String Free text string used to label output files Required lyveset_workflow read1 Array[File] Array of read1 files for sample set. We recommend using cleaned rather than raw reads. Required lyveset_workflow read2 Array[File] Array of read2 files for sample set. We recommend using cleaned rather than raw reads. Required lyveset_workflow reference_genome File Path to reference genome in a Terra-accessible Google bucket. For considerations when choosing a reference genome, see: https://github.com/lskatz/lyve-SET/blob/master/docs/FAQ.md Required lyveset allowedFlanking Int Allowed flanking distance in base pairs. Nucleotides this close together cannot be considered as high-quality. 0 Optional lyveset cpu Int Number of CPUs to allocate to the task 4 Optional lyveset disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional lyveset docker_image String Docker image used for running Lyve-SET \"us-docker.pkg.dev/general-theiagen/staphb/lyveset:1.1.4f\" Optional lyveset downsample Boolean If true, downsample all reads to 50x. Approximated according to the ref genome assembly FALSE Optional lyveset fast Boolean Shorthand for--downsample --mapper snap --nomask-phages --nomask-cliffs --sample-sites FALSE Optional lyveset mapper String Which mapper? Choices: \"smalt\", \"snap\" \"smalt\" Optional lyveset mask_cliffs Boolean If true, search for and mask 'Cliffs' in pileups FALSE Optional lyveset mask_phages Boolean If true, search for and mask phages in the reference genome FALSE Optional lyveset memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional lyveset min_alt_frac Float The percent consensus that needs to be reached before a SNP is called. Otherwise, 'N' 0.75 Optional lyveset min_coverage Int Minimum coverage needed before a SNP is called. Otherwise, 'N' 10 Optional lyveset nomatrix Boolean If true, do not create an hqSNP matrix FALSE Optional lyveset nomsa Boolean If true, do not make a multiple sequence alignment FALSE Optional lyveset notrees Boolean If true, do not make phylogenies FALSE Optional lyveset presets String See presets.conf for more information Optional lyveset read_cleaner String Which read cleaner? Choices: \"none\", \"CGP\", \"BayesHammer\" \"CGP\" Optional lyveset sample_sites Boolean If true, randomly choose a genome and find SNPs in a quick and dirty way. Then on the SNP-calling stage, only interrogate those sites for SNPs for each genome (including the randomly-sampled genome). FALSE Optional lyveset snpcaller String Which SNP caller? Choices: \"varscan\", \"vcftools\" \"varscan\" Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/lyve_set/#workflow-actions","title":"Workflow Actions","text":"The Lyve_SET WDL workflow is run using read data from a set of samples. The workflow will produce a pairwise SNP matrix for the sample set and a maximum likelihood phylogenetic tree. Details regarding the default implementation of Lyve_SET and optional modifications are listed below.
read_cleaner input variable.mask_cliffs and mask_phages variables to \"true\".smalt and varscan). Additional options for each are available using the mapper and snpcaller input variables.min_alt_frac and min_coverage input variables.nomsa = true, nomatrix = true, or notrees = true, respectively.For full descriptions of Lyve-SET pipeline outputs, we recommend consulting the Lyve-SET documentation: https://github.com/lskatz/lyve-SET/blob/master/docs/OUTPUT.md
The following output files are populated to the Terra data table. However, please note that certain files may not appear in the data table following a run for two main reasons:
notrees = true, no tree file will appearIn addition to these outputs, all of the files produced by the Lyve-SET pipeline are available in the task-level outputs, including intermediate files and individual bam and vcf files for each sample. These files can be accessed viewing the execution directory for the run.
"},{"location":"workflows/phylogenetic_construction/lyve_set/#references","title":"References","text":"Lyve-SET Katz LS, Griswold T, Williams-Newkirk AJ, Wagner D, Petkau A, et al. (2017) A Comparative Analysis of the Lyve-SET Phylogenomics Pipeline for Genomic Epidemiology of Foodborne Pathogens. Frontiers in Microbiology 8.
"},{"location":"workflows/phylogenetic_construction/mashtree_fasta/","title":"MashTree_FASTA","text":""},{"location":"workflows/phylogenetic_construction/mashtree_fasta/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.1.0 Yes Set-level"},{"location":"workflows/phylogenetic_construction/mashtree_fasta/#mashtree_fasta_phb","title":"MashTree_FASTA_PHB","text":"MashTree_FASTA creates a phylogenetic tree using Mash distances.
Mash distances are representations of how many kmers two sequences have in common. These distances are generated by transforming all kmers from a sequence into an integer value with hashing and Bloom filters. The hashed kmers are sorted and a \"sketch\" is created by only using the kmers that appear at the top of the sorted list. These sketches can be compared by counting the number of hashed kmers they have in common. Mashtree uses a neighbor-joining algorithm to cluster these \"distances\" into phylogenetic trees.
This workflow also features an optional module, summarize_data, that creates a presence/absence matrix for the analyzed samples from a list of indicated columns (such as AMR genes, etc.) that can be used in Phandango.
MashTree_Fasta is run on a set of assembly fastas and creates a phylogenetic tree and matrix. These outputs are passed to a task that will rearrange the matrix to match the order of the terminal ends in the phylogenetic tree.
The optional summarize_data task performs the following only if all of the data_summary_* and sample_names optional variables are filled out:
\"amrfinderplus_virulence_genes,amrfinderplus_stress_genes\", etc. that can be found within the origin Terra data table.amrfinder_amr_genes column for a sample contains these values: \"aph(3')-IIIa,tet(O),blaOXA-193\", the summarize_data task will check each sample in the set to see if they also have those AMR genes detected.By default, this task appends a Phandango coloring tag to color all items from the same column the same; this can be turned off by setting the optional phandango_coloring variable to false.
summarize_data task is used mashtree_matrix File The SNP matrix made mashtree_summarized_data File CSV presence/absence matrix generated by the summarize_data task from the list of columns provided; formatted for Phandango if phandango_coloring input is true mashtree_tree File The phylogenetic tree made mashtree_version String The version of mashtree used in the workflow mashtree_wf_analysis_date String The date the workflow was run mashtree_wf_version String The version of PHB the workflow is hosted in"},{"location":"workflows/phylogenetic_construction/mashtree_fasta/#references","title":"References","text":"Katz, L. S., Griswold, T., Morrison, S., Caravas, J., Zhang, S., den Bakker, H.C., Deng, X., and Carleton, H. A., (2019). Mashtree: a rapid comparison of whole genome sequence files. Journal of Open Source Software, 4(44), 1762,\u00a0https://doi.org/10.21105/joss.01762
Ondov, B. D., Treangen, T. J., Melsted, P., Mallonee, A. B., Bergman, N. H., Koren, S., & Phillippy, A. M. (2016). Mash: Fast genome and metagenome distance estimation using minhash. Genome Biology, 17(1), 132. doi:10.1186/s13059-016-0997-x
"},{"location":"workflows/phylogenetic_construction/snippy_streamline/","title":"Snippy_Streamline","text":""},{"location":"workflows/phylogenetic_construction/snippy_streamline/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.2.0 Yes; some optional features incompatible Set-level"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#snippy_streamline_phb","title":"Snippy_Streamline_PHB","text":"Snippy_Streamline_PHB Workflow Diagram
The Snippy_Streamline workflow is an all-in-one approach to generating a reference-based phylogenetic tree and associated SNP-distance matrix. The workflow can be run in multiple ways with options for:
Centroid task and Assembly_Fetch sub-workflow to find a close reference genome to your datasetassembly_fasta field for automatic reference genome selection.snippy_core_bed)core_genome; default = true)use_gubbins; default=true)iqtree2_model), or allowing IQ-Tree's ModelFinder to identify the best model for your dataset (default)Sequencing Data Requirements
Sequencing data used in the Snippy_Streamline workflow must:
Gubbins, input data should represent complete genomes from the same strain/lineage (e.g. MLST) that share a recent common ancestor.Reference Genomes
If reference genomes have multiple contigs, they will not be compatible with using Gubbins to mask recombination in the phylogenetic tree. The automatic selection of a reference genome by the workflow may result in a reference with multiple contigs. In this case, an alternative reference genome should be sought.
"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#inputs","title":"Inputs","text":"To run Snippy_Streamline, either a reference genome must be provided (reference_genome_file), or you must provide assemblies of the samples in your tree so that the workflow can automatically find and download the closest reference genome to your dataset (via assembly_fasta)
Guidance for optional inputs
Several core and optional tasks can be used to generate the Snippy phylogenetic tree, making it highly flexible and suited to a wide range of datasets. You will need to decide which tasks to use depending on the genomes that you are analyzing. Some guidelines for the optional tasks to use for different genome types are provided below.
Default settings (suitable for most bacteria)The default settings are as follows and are suitable for generating phylogenies for most bacteria
core_genome = true (creates core genome phylogeny)use_gubbins = true (recombination masked)Phylogenies of MTBC are typically constructed
reference_genome_file = gs://theiagen-public-files-rp/terra/theiaprok-files/Mtb_NC_000962.3.fastasnippy_core_bed = gs://theiagen-public-files/terra/theiaprok-files/Mtb_NC_000962.3.beduse_gubbins = falsecore_genome = true (as default)For automatic reference selection by the workflow (optional):
Centroid (optional) Assembly_Fetch workflow (optional)For all cases:
Snippy_Variants workflow Snippy_Tree workflow"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#centroid","title":"Centroid","text":"Centroid selects the most central genome among a list of assemblies by computing pairwise mash distances. In Snippy_Streamline, this centroid assembly is then used to find a closely related reference genome that can be used to generate the tree. In order to use Centroid, should complete the samplenames input.
Centroid Technical Details
Links Task task_centroid.wdl Software Source Code https://github.com/theiagen/centroid Software Documentation https://github.com/theiagen/centroid"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#assembly_fetch","title":"Assembly_Fetch","text":"The Assembly_Fetch workflow compares the centroid assembly with the RefSeq database to identify the closest reference and then downloads this assembly in FASTA format, and optionally also in GFF3 and/or GBFF format. The Reference database is for bacteria by default but this can be changed by adjusting the referenceseeker_db input to the appropriate database. See the Assembly_Fetch workflow documentation for more information.
Call-Caching Disabled
If using Snippy_Streamline workflow (which runs the Assembly_Fetch workflow if no reference genome is provided by user) version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is downloaded fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#snippy_variants","title":"Snippy_Variants","text":"Snippy_Variants aligns reads for each sample against the reference genome. As part of Snippy_Streamline, the only output from this workflow is the snippy_variants_outdir_tarball which is provided in the set-level data table. Please see the full documentation for Snippy_Variants for more information.
This task also extracts QC metrics from the Snippy output for each sample and saves them in per-sample TSV files (snippy_variants_qc_metrics). These per-sample QC metrics include the following columns:
min_coverage threshold (default is 10).These per-sample QC metrics are then combined into a single file (snippy_combined_qc_metrics). The combined QC metrics file includes the same columns as above for all samples. Note that the last set of columns (#rname to meanmapq) may repeat for each chromosome or contig in the reference genome.
QC Metrics for Phylogenetic Analysis
These QC metrics provide valuable insights into the quality and coverage of your sequencing data relative to the reference genome. Monitoring these metrics can help identify samples with low coverage, poor alignment, or potential issues that may affect downstream analyses
Snippy Variants Technical Details
Links Task task_snippy_variants.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#snippy_tree","title":"Snippy_Tree","text":"A simplified version of Snippy_Tree is used to build the phylogeny in the Snippy_Streamline workflow. The tasks undertaken are exactly the same between both workflows, but the user inputs and outputs have been reduced for clarity and ease. Please see the full documentation for Snippy_Tree for more information.
In Snippy Streamline, the nucleotide substitution model used by gubbins will always be GTR+GAMMA.
"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#outputs","title":"Outputs","text":"Variable Type Description snippy_centroid_docker String Docker file used for Centroid snippy_centroid_fasta File FASTA file for the centroid sample snippy_centroid_mash_tsv File TSV file containing mash distances computed by centroid snippy_centroid_samplename String Name of the centroid sample snippy_centroid_version String Centroid version used snippy_cg_snp_matrix File CSV file of core genome pairwise SNP distances between samples, calculated from the final alignment snippy_concatenated_variants File The concatenated variants file snippy_combined_qc_metrics File Combined QC metrics file containing concatenated QC metrics from all samples. snippy_filtered_metadata File TSV recording the columns of the Terra data table that were used in the summarize_data task snippy_final_alignment File Final alignment (FASTA file) used to generate the tree (either after snippy alignment, gubbins recombination removal, and/or core site selection with SNP-sites) snippy_final_tree File Final phylogenetic tree produced by Snippy_Streamline snippy_gubbins_branch_stats File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_gubbins_docker String Docker file used for Gubbins snippy_gubbins_recombination_gff File Recombination statistics in GFF format; these can be viewed in Phandango against the phylogenetic tree snippy_gubbins_version String Gubbins version used snippy_iqtree2_docker String Docker file used for IQTree2 snippy_iqtree2_model_used String Nucleotide substitution model used by IQTree2 snippy_iqtree2_version String IQTree2 version used snippy_msa_snps_summary File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_ncbi_datasets_docker String Docker file used for NCBI datasets snippy_ncbi_datasets_version String NCBI datasets version used snippy_ref File Reference genome used by Snippy snippy_ref_metadata_json File Metadata associated with the refence genome used by Snippy, in JSON format snippy_referenceseeker_database String ReferenceSeeker database used snippy_referenceseeker_docker String Docker file used for ReferenceSeeker snippy_referenceseeker_top_hit_ncbi_accession String NCBI Accession for the top it identified by Assembly_Fetch snippy_referenceseeker_tsv File TSV file of the top hits between the query genome and the Reference Seeker database snippy_referenceseeker_version String ReferenceSeeker version used snippy_snp_dists_docker String Docker file used for SNP-dists snippy_snp_dists_version String SNP-dists version used snippy_snp_sites_docker String Docker file used for SNP-sites snippy_snp_sites_version String SNP-sites version used snippy_streamline_analysis_date String Date of workflow run snippy_streamline_version String Version of Snippy_Streamline used snippy_summarized_data File CSV presence/absence matrix generated by the summarize_data task (within Snippy_Tree workflow) from the list of columns provided snippy_tree_snippy_docker String Docker file used for Snippy in the Snippy_Tree subworkfow snippy_tree_snippy_version String Version of Snippy_Tree subworkflow used snippy_variants_outdir_tarball Array[File] A compressed file containing the whole directory of snippy output files. This is used when running Snippy_Tree snippy_variants_percent_reads_aligned Float Percentage of reads aligned to the reference genome snippy_variants_percent_ref_coverage Float Proportion of the reference genome covered by reads with a depth greater than or equal to themin_coverage threshold (default is 10). snippy_variants_snippy_docker Array[String] Docker file used for Snippy in the Snippy_Variants subworkfow snippy_variants_snippy_version Array[String] Version of Snippy_Tree subworkflow used snippy_wg_snp_matrix File CSV file of whole genome pairwise SNP distances between samples, calculated from the final alignment"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/","title":"Snippy_Streamline_FASTA","text":""},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.2.0 Yes; some optional features incompatible Set-level"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#snippy_streamline_fasta_phb","title":"Snippy_Streamline_FASTA_PHB","text":"This workflow is a FASTA-compatible version of Snippy_Streamline. Please see the Snippy_Streamline documentation for more information regarding the workflow tasks.
Snippy_Streamline_FASTA_PHB Workflow Diagram
The Snippy_Streamline_FASTA workflow is an all-in-one approach to generating a reference-based phylogenetic tree and associated SNP-distance matrix. The workflow can be run in multiple ways with options for:
Centroid task and Assembly_Fetch sub-workflow to find a close reference genome to your datasetsnippy_core_bed)core_genome; default = true)use_gubbins; default=true)iqtree2_model), or allowing IQ-Tree's ModelFinder to identify the best model for your dataset (default)Assembly Data Requirements
Input data used in the Snippy_Streamline_FASTA workflow must:
Gubbins, input data should represent complete genomes from the same strain/lineage (e.g. MLST) that share a recent common ancestor.Reference Genomes
If reference genomes have multiple contigs, they will not be compatible with using Gubbins to mask recombination in the phylogenetic tree. The automatic selection of a reference genome by the workflow may result in a reference with multiple contigs. In this case, an alternative reference genome should be sought.
"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#workflow-tasks","title":"Workflow Tasks","text":"Snippy_Variants QC Metrics Concatenation (optional)"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#snippy_variants","title":"Snippy_Variants QC Metric Concatenation (optional)","text":"Optionally, the user can provide the snippy_variants_qc_metrics file produced by the Snippy_Variants workflow as input to the workflow to concatenate the reports for each sample in the tree. These per-sample QC metrics include the following columns:
min_coverage threshold (default is 10).The combined QC metrics file includes the same columns as above for all samples. Note that the last set of columns (#rname to meanmapq) may repeat for each chromosome or contig in the reference genome.
QC Metrics for Phylogenetic Analysis
These QC metrics provide valuable insights into the quality and coverage of your sequencing data relative to the reference genome. Monitoring these metrics can help identify samples with low coverage, poor alignment, or potential issues that may affect downstream analyses, and we recommend examining them before proceeding with phylogenetic analysis if performing Snippy_Variants and Snippy_Tree separately.
Snippy Variants Technical Details
Links Task task_snippy_variants.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status snippy_streamline_fasta assembly_fasta Array[File] The assembly files for your samples Required snippy_streamline_fasta samplenames Array[String] The names of your samples Required snippy_streamline_fasta tree_name String String of your choice to prefix output files Required snippy_streamline_fasta reference_genome_file File Reference genome in FASTA or GENBANK format (must be the same reference used in Snippy_Variants workflow); provide this if you want to skip the detection of a suitable reference Optional centroid cpu Int Number of CPUs to allocate to the task 1 Optional centroid disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional centroid docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/centroid:0.1.0 Optional centroid memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional ncbi_datasets_download_genome_accession cpu Int Number of CPUs to allocate to the task 1 Optional ncbi_datasets_download_genome_accession disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ncbi_datasets_download_genome_accession docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ncbi-datasets:14.13.2 Optional ncbi_datasets_download_genome_accession include_gbff3 Boolean When set to true, outputs a gbff3 file (Genbank file) FALSE Optional ncbi_datasets_download_genome_accession include_gff Boolean When set to true, outputs a gff file (Annotation file) FALSE Optional ncbi_datasets_download_genome_accession memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional referenceseeker cpu Int Number of CPUs to allocate to the task 4 Optional referenceseeker disk_size Int Amount of storage (in GB) to allocate to the task 200 Optional referenceseeker docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/referenceseeker:1.8.0--pyhdfd78af_0 Optional referenceseeker memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional referenceseeker referenceseeker_ani_threshold Float Bidirectional average nucleotide identity to use as a cut off for identifying reference assemblies with ReferenceSeeker; default value set according to https://github.com/oschwengers/referenceseeker#description 0.95 Optional referenceseeker referenceseeker_conserved_dna_threshold Float Conserved DNA % to use as a cut off for identifying reference assemblies with ReferenceSeeker; default value set according to https://github.com/oschwengers/referenceseeker#description 0.69 Optional referenceseeker referenceseeker_db File Database to use with ReferenceSeeker gs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-bacteria-refseq-205.v20210406.tar.gz Optional snippy_tree_wf call_shared_variants Boolean Activates the shared variants analysis task TRUE Optional snippy_tree_wf core_genome Boolean When \"true\", workflow generates core genome phylogeny; when \"false\", whole genome is used TRUE Optional snippy_tree_wf data_summary_column_names String A comma-separated list of the column names from the sample-level data table for generating a data summary (presence/absence .csv matrix) Optional snippy_tree_wf data_summary_terra_project String The billing project for your current workspace. This can be found after the \"#workspaces/\" section in the workspace's URL Optional snippy_tree_wf data_summary_terra_table String The name of the sample-level Terra data table that will be used for generating a data summary Optional snippy_tree_wf data_summary_terra_workspace String The name of the Terra workspace you are in. This can be found at the top of the webpage, or in the URL after the billing project. Optional snippy_tree_wf gubbins_cpu Int Number of CPUs to allocate to the task 4 Optional snippy_tree_wf gubbins_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_tree_wf gubbins_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/gubbins:3.3--py310pl5321h8472f5a_0 Optional snippy_tree_wf gubbins_memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional snippy_tree_wf iqtree2_bootstraps String Number of replicates for http://www.iqtree.org/doc/Tutorial#assessing-branch-supports-with-ultrafast-bootstrap-approximation (Minimum recommended= 1000) 1000 Optional snippy_tree_wf iqtree2_cpu Int Number of CPUs to allocate to the task 4 Optional snippy_tree_wf iqtree2_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_tree_wf iqtree2_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/iqtree2:2.1.2 Optional snippy_tree_wf iqtree2_memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional snippy_tree_wf iqtree2_model String Nucelotide substitution model to use when generating the final tree with IQTree2. By default, IQtree runs its ModelFinder algorithm to identify the model it thinks best fits your dataset Optional snippy_tree_wf iqtree2_opts String Additional options to pass to IQTree2 Optional snippy_tree_wf midpoint_root_tree Boolean A True/False option that determines whether the tree used in the SNP matrix re-ordering task should be re-rooted or not. Options: true of false TRUE Optional snippy_tree_wf phandango_coloring Boolean Boolean variable that tells the data summary task and the reorder matrix task to include a suffix that enables consistent coloring on Phandango; by default, this suffix is not added. To add this suffix set this variable to true. FALSE Optional snippy_tree_wf snippy_core_bed File User-provided bed file to mask out regions of the genome when creating multiple sequence alignments Optional snippy_tree_wf snippy_core_cpu Int Number of CPUs to allocate to the task 8 Optional snippy_tree_wf snippy_core_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_tree_wf snippy_core_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snippy:4.6.0 Optional snippy_tree_wf snippy_core_memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional snippy_tree_wf snp_dists_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional snippy_tree_wf snp_sites_cpu Int Number of CPUs to allocate to the task 1 Optional snippy_tree_wf snp_sites_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_tree_wf snp_sites_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-sites:2.5.1 Optional snippy_tree_wf snp_sites_memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional snippy_tree_wf use_gubbins Boolean When \"true\", workflow removes recombination with gubbins tasks; when \"false\", gubbins is not used TRUE Optional snippy_variants_wf base_quality Int Minimum quality for a nucleotide to be used in variant calling 13 Optional snippy_variants_wf cpu Int Number of CPUs to allocate to the task 4 Optional snippy_variants_wf docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snippy:4.6.0 Optional snippy_variants_wf map_qual Int Minimum mapping quality to accept in variant calling Optional snippy_variants_wf maxsoft Int Number of bases of alignment to soft-clip before discarding the alignment Optional snippy_variants_wf memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional snippy_variants_wf min_coverage Int Minimum read coverage of a position to identify a mutation 10 Optional snippy_variants_wf min_frac Float Minimum fraction of bases at a given position to identify a mutation 0.9 Optional snippy_variants_wf min_quality Int Minimum VCF variant call \"quality\" 100 Optional snippy_variants_wf query_gene String Indicate a particular gene of interest Optional snippy_variants_wf read1 File Internal component, do not modify. Do Not Modify, Optional snippy_variants_wf read2 File Internal component, do not modify. Do Not Modify, Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#outputs","title":"Outputs","text":"Variable Type Description snippy_centroid_docker String Docker file used for Centroid snippy_centroid_fasta File FASTA file for the centroid sample snippy_centroid_mash_tsv File TSV file containing mash distances computed by centroid snippy_centroid_samplename String Name of the centroid sample snippy_centroid_version String Centroid version used snippy_cg_snp_matrix File CSV file of core genome pairwise SNP distances between samples, calculated from the final alignment snippy_combined_qc_metrics File Combined QC metrics file containing concatenated QC metrics from all samples. snippy_concatenated_variants File The concatenated variants file snippy_filtered_metadata File TSV recording the columns of the Terra data table that were used in the summarize_data task snippy_final_alignment File Final alignment (FASTA file) used to generate the tree (either after snippy alignment, gubbins recombination removal, and/or core site selection with SNP-sites) snippy_final_tree File Final phylogenetic tree produced by Snippy_Streamline snippy_gubbins_branch_stats File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_gubbins_docker String Docker file used for Gubbins snippy_gubbins_recombination_gff File Recombination statistics in GFF format; these can be viewed in Phandango against the phylogenetic tree snippy_gubbins_version String Gubbins version used snippy_iqtree2_docker String Docker file used for IQTree2 snippy_iqtree2_model_used String Nucleotide substitution model used by IQTree2 snippy_iqtree2_version String IQTree2 version used snippy_msa_snps_summary File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_ncbi_datasets_docker String Docker file used for NCBI datasets snippy_ncbi_datasets_version String NCBI datasets version used snippy_ref File Reference genome used by Snippy snippy_ref_metadata_json File Metadata associated with the refence genome used by Snippy, in JSON format snippy_referenceseeker_database String ReferenceSeeker database used snippy_referenceseeker_docker String Docker file used for ReferenceSeeker snippy_referenceseeker_top_hit_ncbi_accession String NCBI Accession for the top it identified by Assembly_Fetch snippy_referenceseeker_tsv File TSV file of the top hits between the query genome and the Reference Seeker database snippy_referenceseeker_version String ReferenceSeeker version used snippy_snp_dists_docker String Docker file used for SNP-dists snippy_snp_dists_version String SNP-dists version used snippy_snp_sites_docker String Docker file used for SNP-sites snippy_snp_sites_version String SNP-sites version used snippy_streamline_analysis_date String Date of workflow run snippy_streamline_version String Version of Snippy_Streamline used snippy_summarized_data File CSV presence/absence matrix generated by the summarize_data task (within Snippy_Tree workflow) from the list of columns provided snippy_tree_snippy_docker String Docker file used for Snippy in the Snippy_Tree subworkfow snippy_tree_snippy_version String Version of Snippy_Tree subworkflow used snippy_variants_outdir_tarball Array[File] A compressed file containing the whole directory of snippy output files. This is used when running Snippy_Tree snippy_variants_snippy_docker Array[String] Docker file used for Snippy in the Snippy_Variants subworkfow snippy_variants_snippy_version Array[String] Version of Snippy_Tree subworkflow used snippy_wg_snp_matrix File CSV file of whole genome pairwise SNP distances between samples, calculated from the final alignment"},{"location":"workflows/phylogenetic_construction/snippy_tree/","title":"Snippy_Tree","text":""},{"location":"workflows/phylogenetic_construction/snippy_tree/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.3.0 Yes; some optional features incompatible Set-level"},{"location":"workflows/phylogenetic_construction/snippy_tree/#snippy_tree_phb","title":"Snippy_Tree_PHB","text":"Snippy_Tree is a workflow for generating high-quality bacterial phylogenies. It produces a phylogenetic tree and pairwise SNP-distance matrix, with the option to summarize additional metadata to visualize with the tree.
The tree produced by Snippy_Tree will always be a maximum-likelihood phylogeny using a reference-based alignment. There are key options for whether to:
core_genome)bed_file)use_gubbins)Snippy_Tree is intended to be run after the Snippy_Variants workflow. It is a set-level workflow that takes in an array of directories generated by the Snippy_Variants workflow, which must be run for each sample that you wish to include in the phylogenetic tree. You should ensure that for all samples included in the phylogeny, Snippy_Variants has been run with identical inputs including the same reference genome. When running the Snippy_Tree workflow, you will need to provide the same reference genome that you used when running Snippy_Variants. Snippy_Variants and Snippy_Tree can both automatically be run by using the Snippy_Streamline workflow.
Sequencing data used in the Snippy_Tree workflow must:
Gubbins, input data should represent whole genomes from the same strain/lineage (e.g. MLST) that share a recent common ancestor.Guidance for optional inputs
Several core and optional tasks can be used to generate the Snippy phylogenetic tree, making it highly flexible and suited to a wide range of datasets. You will need to decide which tasks to use depending on the genomes that you are analyzing. Some guidelines for the optional tasks to use for different genome types are provided below.
Default settings (suitable for most bacteria)The default settings are as follows and are suitable for generating phylogenies for most bacteria
core_genome = true (creates core genome phylogeny)use_gubbins = true (recombination masked)Phylogenies of MTBC are typically constructed
reference_genome_file = gs://theiagen-public-files-rp/terra/theiaprok-files/Mtb_NC_000962.3.fastasnippy_core_bed = gs://theiagen-public-files/terra/theiaprok-files/Mtb_NC_000962.3.beduse_gubbins = falsecore_genome = true (as default)Snippy is a pipeline for calling SNPs and INDELs in haploid genomes. Before running Snippy_Tree, you must run Snippy_Variants, another workflow that uses the Snippy tool to align reads against a reference genome for individual samples. In Snippy_Tree, the snippy tool is used again to generate a whole-genome multiple sequence alignment (fasta file) of reads from all the samples we'd like in our tree.
When generating the multiple sequence alignment, a bed file can be provided by users to mask certain areas of the genome in the alignment. This is particularly relevant for masking known repetitive regions in Mycobacterium tuberculosis genomes, or masking known regions containing phage sequences.
Why do I see snippy_core in Terra?
In Terra, this task is named \"snippy_core\" after the name of the command in the original Snippy tool. Despite the name, this command is NOT being used to make a core genome, but instead a multiple sequence alignment of the whole genome (without any sections masked using a bed file).
Snippy Technical Details
Links Task task_snippy_core.wdl Default software version v4.6.0 (us-docker.pkg.dev/general-theiagen/staphb/snippy:4.6.0) Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_tree/#gubbins_task","title":"Gubbins (optional)","text":"Optional
Gubbins is used when use_gubbins is set to true (default=true).
Genealogies Unbiased By recomBinations In Nucleotide Sequences (Gubbins) identifies and masks genomic regions that are predicted to have arisen via recombination. It works by iteratively identifying loci containing elevated densities of SNPs and constructing phylogenies based on the putative single nucleotide variants outside these regions (for more details, see here). By default, these phylogenies are constructed using RaxML and a GTR-GAMMA nucleotide substitution model, which will be the most suitable model for most bacterial phylogenetics, though this can be modified with the tree_builder and nuc_subst_model inputs.
Gubbins is the industry standard for masking recombination from bacterial genomes when building phylogenies, but limitations to recombination removal exist. Gubbins cannot distinguish recombination from high densities of SNPs that may result from assembly or alignment errors, mutational hotspots, or regions of the genome with relaxed selection. The tool is also intended only to find recombinant regions that are short relative to the length of the genome, so large regions of recombination may not be masked. These factors should be considered when interpreting resulting phylogenetic trees, but overwhelmingly Gubbins improves our ability to understand ancestral relationships between bacterial genomes.
There are few optional inputs for Gubbins that can be modified by the user:
iterations: Gubbins works by iteratively identifying loci containing elevated densities of SNPs, while constructing phylogenies based on the putative single nucleotide variants outside these regions. It may take many iterations for Gubbins to converge on an alignment that it considers free of recombination, especially for phylogenies that contain large numbers of genomes. By default, Gubbins is limited to 5 iterations though this may be increased by the user with the iterationsoptional input (incurring increased computing time and cost, and possibly requiring increased memory allocation).nuc_subst_model, tree_builder and tree_args: When Gubbins constructs phylogenies, it can use a number of phylogenetic inference tools, each with different nucleotide substitution models and tree-building models. By default, the Snippy_Tree workflow uses a GTRGAMMA substitution model and RaxML for tree building (typically suitable for bacterial genomes), but these can be modified by the user depending on the genome sequences being used with the nuc_subst_model and tree_builder optional inputs, respectively. The nucleotide substitution models that are available depend on the tree building algorithm being used (see here). Additional options for generating the phylogenetic trees in Gubbins can be specified with the tree_args optional input, providing an input string that is consistent with the option formats of the Gubbins command.filter_percent: By default, Gubbins removes genomes from the multiple sequence alignment if more than 25 % of the genome is represented by gaps. The percentage of gaps can be modified by the user using the filter_percent optional input.Gubbins Technical Details
Links Task task_gubbins.wdl Software Source Code Gubbins on GitHub Software Documentation Gubbins v3.3 manual Original Publication(s) Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins Default software version us-docker.pkg.dev/general-theiagen/biocontainers/gubbins:3.3--py310pl5321h8472f5a_0"},{"location":"workflows/phylogenetic_construction/snippy_tree/#snp_sites_task","title":"SNP-sites (optional)","text":"Turn on SNP-Sites with core_genome
SNP-sites runs when the core_genome option is set to true.
SNP-sites is used to filter out invariant sites in the whole-genome alignment, thereby creating a core genome alignment for phylogenetic inference. The output is a fasta file containing the core genome of each sample only. If Gubbins has been used, this output fasta will not contain any sites that are predicted to have arisen via recombination.
SNP-sites technical details
Links Task task_snp_sites.wdl Default software version 2.5.1 (us-docker.pkg.dev/general-theiagen/biocontainers/snp-sites:2.5.1--hed695b0_0) Software Source Code SNP-sites on GitHub Software Documentation SNP-sites on GitHub Original Publication(s) SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments"},{"location":"workflows/phylogenetic_construction/snippy_tree/#iqtree2_task","title":"IQTree2","text":"IQTree2 is used to build the final phylogeny. It uses the alignment generated in the previous steps of the workflow. The contents of this alignment will depend on whether any sites were masked with recombination.
The phylogeny is generated using the maximum-likelihood method and a specified nucleotide substitution model. By default, the Snippy_Tree workflow will run Model Finder to determine the most appropriate nucleotide substitution model for your data, but you may specify the nucleotide substitution model yourself using the iqtree2_model optional input (see here for available models).
IQTree will perform assessments of the tree using the Shimodaira\u2013Hasegawa approximate likelihood-ratio test (SH-aLRT test), and ultrafast bootstrapping with UFBoot2, a quicker but less biased alternative to standard bootstrapping. A clade should not typically be trusted if it has less than 80% support from the SH-aLRT test and less than 95% support with ultrafast bootstrapping.
Nucleotide substitution model
When core_genome= true, the default nucleotide substitution model is set to the General Time Reverside model with Gamma distribution (GTR+G).
When the user sets core_genome= false, the default nucleotide substitution model is set to the General Time Reversible model with invariant sites and Gamma distribution (GTR+I+G).
IQTree2 technical details
Links Task task_iqtree2.wdl Software Source Code IQ-TREE on GitHub Software Documentation IQTree documentation for the latest version (not necessarily the version used in this workflow) Original Publication(s) IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era Publication for the SH-alRT test New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0 Publication for ultrafast bootstrapping integration to IQTree Ultrafast Approximation for Phylogenetic Bootstrap; UFBoot2: Improving the Ultrafast Bootstrap Approximation Publication for ModelFinder ModelFinder: fast model selection for accurate phylogenetic estimates"},{"location":"workflows/phylogenetic_construction/snippy_tree/#snp_dists_task","title":"SNP-dists","text":"SNP-dists computes pairwise SNP distances between genomes. It takes the same alignment of genomes used to generate your phylogenetic tree and produces a matrix of pairwise SNP distances between sequences. This means that if you generated pairwise core-genome phylogeny, the output will consist of pairwise core-genome SNP (cgSNP) distances. Otherwise, these will be whole-genome SNP distances. Regardless of whether core-genome or whole-genome SNPs, this SNP distance matrix will exclude all SNPs in masked regions (i.e. masked with a bed file or gubbins).
The SNP-distance output can be visualized using software such as Phandango to explore the relationships between the genomic sequences. The task adds a Phandango coloring tag (:c1) to the column names in the output matrix to ensure that all columns are colored with the same color scheme throughout.
SNP-dists Technical Details
Links Task task_snp_dists.wdl Default software version 0.8.2 (us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2) Software Source Code SNP-dists on GitHub Software Documentation SNP-dists on GitHub Original Publication(s) Not known to be published"},{"location":"workflows/phylogenetic_construction/snippy_tree/#data_summary_task","title":"Data Summary (optional)","text":"If you fill out the data_summary_* and sample_names optional variables, you can use the optional summarize_data task. The task takes a comma-separated list of column names from the Terra data table, which should each contain a list of comma-separated items. For example, \"amrfinderplus_virulence_genes,amrfinderplus_stress_genes\" (with quotes, comma separated, no spaces) for these output columns from running TheiaProk. The task checks whether those comma-separated items are present in each row of the data table (sample), then creates a CSV file of these results. The CSV file indicates presence (TRUE) or absence (empty) for each item. By default, the task adds a Phandango coloring tag to group items from the same column, but you can turn this off by setting phandango_coloring to false.
Sample_Name,aph(3')-IIa,blaCTX-M-65,blaOXA-193,tet(O)\nsample1,TRUE,,TRUE,TRUE\nsample2,,,FALSE,TRUE\nsample3,,,FALSE,\nData summary produced using the phandango_coloring option, visualized alongside Newick tree at http://jameshadfield.github.io/phandango/#/main
Example phandango_coloring output
Data summary technical details
Links Task task_summarize_data.wdl"},{"location":"workflows/phylogenetic_construction/snippy_tree/#concatenate_variants_task","title":"Concatenate Variants (optional)","text":"The cat_variants task concatenates variant data from multiple samples into a single file concatenated_variants. It is very similar to the cat_files task, but also adds a column to the output file that indicates the sample associated with each row of data.
The concatenated_variants file will be in the following format:
Technical Details
Links Task /tasks/utilities/file_handling/task_cat_files.wdl Software Source Code task_cat_files.wdl"},{"location":"workflows/phylogenetic_construction/snippy_tree/#shared_variants_task","title":"Shared Variants (optional)","text":"The shared_variants task takes in the concatenated_variants output from the cat_variants task and reshapes the data so that variants are rows and samples are columns. For each variant, samples where the variant was detected are populated with a \"1\" and samples were either the variant was not detected or there was insufficient coverage to call variants are populated with a \"0\". The resulting table is available as the shared_variants_table output.
The shared_variants_table file will be in the following format:
Technical Details
Links Task task_shared_variants.wdl Software Source Code task_shared_variants.wdl"},{"location":"workflows/phylogenetic_construction/snippy_tree/#snippy_variants","title":"Snippy_Variants QC Metric Concatenation (optional)","text":"Optionally, the user can provide the snippy_variants_qc_metrics file produced by the Snippy_Variants workflow as input to the workflow to concatenate the reports for each sample in the tree. These per-sample QC metrics include the following columns:
min_coverage threshold (default is 10).The combined QC metrics file includes the same columns as above for all samples. Note that the last set of columns (#rname to meanmapq) may repeat for each chromosome or contig in the reference genome.
QC Metrics for Phylogenetic Analysis
These QC metrics provide valuable insights into the quality and coverage of your sequencing data relative to the reference genome. Monitoring these metrics can help identify samples with low coverage, poor alignment, or potential issues that may affect downstream analyses, and we recommend examining them before proceeding with phylogenetic analysis if performing Snippy_Variants and Snippy_Tree separately.
Snippy Variants Technical Details
Links Task task_snippy_variants.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_tree/#outputs","title":"Outputs","text":"Variable Type Description snippy_cg_snp_matrix File CSV file of core genome pairwise SNP distances between samples, calculated from the final alignment snippy_concatenated_variants File Concatenated snippy_results file across all samples in the set snippy_combined_qc_metrics File Combined QC metrics file containing concatenated QC metrics from all samples. snippy_filtered_metadata File TSV recording the columns of the Terra data table that were used in the summarize_data task snippy_final_alignment File Final alignment (FASTA file) used to generate the tree (either after snippy alignment, gubbins recombination removal, and/or core site selection with SNP-sites) snippy_final_tree File Newick tree produced from the final alignment. Depending on user input for core_genome, the tree could be a core genome tree (default when core_genome is true) or whole genome tree (if core_genome is false) snippy_gubbins_branch_stats File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_gubbins_docker String Docker file used for running Gubbins snippy_gubbins_recombination_gff File Recombination statistics in GFF format; these can be viewed in Phandango against the phylogenetic tree snippy_gubbins_version String Gubbins version used snippy_iqtree2_docker String Docker file used for running IQTree2 snippy_iqtree2_model_used String Nucleotide substitution model used by IQTree2 snippy_iqtree2_version String IQTree2 version used snippy_msa_snps_summary File TXT file containing summary statistics for each alignment of each input genome against the reference. This indicates how good the alignment is. Pay particular attention to # unaligned sites, and heterogeneous positions. snippy_ref File Reference genome (FASTA or GenBank file) used for generating phylogeny snippy_shared_snp_table File Table illustrating variants shared among samples snippy_snp_dists_docker String Docker file used for running SNP-dists snippy_snp_dists_version String SNP-dists version used snippy_snp_sites_docker String Docker file used for running SNP-sites snippy_snp_sites_version String SNP-sites version used snippy_summarized_data File CSV presence/absence matrix generated by the summarize_data task from the list of columns provided; formatted for Phandango if phandango_coloring input is true snippy_tree_analysis_date String Date of workflow run snippy_tree_snippy_docker String Docker file used for running Snippy snippy_tree_snippy_version String Snippy version used snippy_tree_version String Version of Snippy_Tree workflow snippy_wg_snp_matrix File CSV file of whole genome pairwise SNP distances between samples, calculated from the final alignment"},{"location":"workflows/phylogenetic_construction/snippy_tree/#references","title":"References","text":"Gubbins: Croucher, Nicholas J., Andrew J. Page, Thomas R. Connor, Aidan J. Delaney, Jacqueline A. Keane, Stephen D. Bentley, Julian Parkhill, and Simon R. Harris. 2015. \"Rapid Phylogenetic Analysis of Large Samples of Recombinant Bacterial Whole Genome Sequences Using Gubbins.\" Nucleic Acids Research 43 (3): e15.
SNP-sites: Page, Andrew J., Ben Taylor, Aidan J. Delaney, Jorge Soares, Torsten Seemann, Jacqueline A. Keane, and Simon R. Harris. 2016. \"SNP-Sites: Rapid Efficient Extraction of SNPs from Multi-FASTA Alignments.\" Microbial Genomics 2 (4): e000056.
IQTree: Nguyen, Lam-Tung, Heiko A. Schmidt, Arndt von Haeseler, and Bui Quang Minh. 2015. \"IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies.\" Molecular Biology and Evolution 32 (1): 268\u201374.
"},{"location":"workflows/phylogenetic_construction/snippy_variants/","title":"Snippy_Variants","text":""},{"location":"workflows/phylogenetic_construction/snippy_variants/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria, Mycotics, Viral PHB v2.3.0 Yes Sample-level"},{"location":"workflows/phylogenetic_construction/snippy_variants/#snippy_variants_phb","title":"Snippy_Variants_PHB","text":"The Snippy_Variants workflow aligns single-end or paired-end reads (in FASTQ format), or assembled sequences (in FASTA format), against a reference genome, then identifies single-nucleotide polymorphisms (SNPs), multi-nucleotide polymorphisms (MNPs), and insertions/deletions (INDELs) across the alignment. If a GenBank file is used as the reference, mutations associated with user-specified query strings (e.g. genes of interest) can additionally be reported to the Terra data table.
Snippy_Variants Workflow Diagram
Example Use Cases
Snippy_Variants may be used to identify these heterogeneous positions by aligning reads to the assembly of the same reads, or to a closely related reference genome and lowering the thresholds to call SNPs.Snippy_Variants produces a BAM file of the reads aligned to the reference genome. This BAM file can be visualized in IGV (see Theiagen Office Hours recordings) to assess the position of a mutation in supporting reads, or if the assembly of the reads was used as a reference, the position in the contig.read2assembly_fasta input and omit read1 and read2.fa, .fasta) or full GenBank (.gbk) format. The mutations identified by Snippy_Variants are highly dependent on the choice of reference genome. Mutations cannot be identified in genomic regions that are present in your query sequence and not the reference.Query String
The query string can be a gene or any other annotation that matches the GenBank file/output VCF EXACTLY
Terra Task Name Variable Type Description Default Value Terra Status snippy_variants_wf reference_genome_file File Reference genome (GenBank file or fasta) Required snippy_variants_wf samplename String Names of samples Required snippy_gene_query cpu Int Number of CPUs to allocate to the task 8 Optional snippy_gene_query disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_gene_query docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-06-21 Optional snippy_gene_query memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional snippy_variants disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_variants_wf assembly_fasta File Assembly file Optional snippy_variants_wf base_quality Int Minimum quality for a nucleotide to be used in variant calling 13 Optional snippy_variants_wf cpus Int Number of CPUs to use 4 Optional snippy_variants_wf docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snippy:4.6.0 Optional snippy_variants_wf map_qual Int Minimum mapping quality to accept in variant calling, default from snippy tool is 60 Optional snippy_variants_wf maxsoft Int Number of bases of alignment to soft-clip before discarding the alignment, default from snippy tool is 10 Optional snippy_variants_wf memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional snippy_variants_wf min_coverage Int Minimum read coverage of a position to identify a mutation 10 Optional snippy_variants_wf min_frac Float Minimum fraction of bases at a given position to identify a mutation, default from snippy tool is 0 0.9 Optional snippy_variants_wf min_quality Int Minimum VCF variant call \"quality\" 100 Optional snippy_variants_wf query_gene String Comma-separated strings (e.g. gene names) in which to search for mutations to output to data table Optional snippy_variants_wf read1 File Forward read file Optional snippy_variants_wf read2 File Reverse read file Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/snippy_variants/#workflow-tasks","title":"Workflow Tasks","text":"Snippy_Variants uses Snippy to align reads to the reference and call SNPs, MNPs and INDELs according to optional input parameters. The output includes a file of variants that is then queried using the grep bash command to identify any mutations in specified genes or annotations of interest. The query string MUST match the gene name or annotation as specified in the GenBank file and provided in the output variant file in the snippy_results column.
Quality Control Metrics
Additionally, Snippy_Variants extracts quality control (QC) metrics from the Snippy output for each sample. These per-sample QC metrics are saved in TSV files (snippy_variants_qc_metrics). The QC metrics include:
snippy_variants_percent_reads_aligned output column.min_coverage threshold (default is 10); also available in the snippy_variants_percent_ref_coverage output column.Note that the last set of columns (#rname to meanmapq) may repeat for each chromosome or contig in the reference genome.
QC Metrics for Phylogenetic Analysis
These QC metrics provide valuable insights into the quality and coverage of your sequencing data relative to the reference genome. Monitoring these metrics can help identify samples with low coverage, poor alignment, or potential issues that may affect downstream analyses, and we recommend examining them before proceeding with phylogenetic analysis if performing Snippy_Variants and Snippy_Tree separately.
These per-sample QC metrics can also be combined into a single file (snippy_combined_qc_metrics) in downstream workflows, such as snippy_tree, providing an overview of QC metrics across all samples.
Snippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_variants/#outputs","title":"Outputs","text":"Visualize your outputs in IGV
Output bam/bai files may be visualized using IGV to manually assess read placement and SNP support.
Note on coverage calculations
The outputs from samtools coverage (found in the snippy_variants_coverage_tsv file) may differ from the snippy_variants_percent_ref_coverage due to different calculation methods. samtools coverage computes genome-wide coverage metrics (e.g., the proportion of bases covered at depth \u2265 1), while snippy_variants_percent_ref_coverage uses a user-defined minimum coverage threshold (default is 10), calculating the proportion of the reference genome with a depth greater than or equal to this threshold.
samtools coverage command, providing genome-wide metrics such as the proportion of bases covered (depth \u2265 1), mean depth, and other related statistics. snippy_variants_docker String Docker image for snippy variants task snippy_variants_gene_query_results File CSV file detailing results for mutations associated with the query strings specified by the user snippy_variants_hits String A summary of mutations associated with the query strings specified by the user snippy_variants_num_reads_aligned Int Number of reads that aligned to the reference genome as calculated by samtools view -c command snippy_variants_num_variants Int Number of variants detected between sample and reference genome snippy_variants_outdir_tarball File A compressed file containing the whole directory of snippy output files. This is used when running Snippy_Tree snippy_variants_percent_reads_aligned Float Percentage of reads aligned to the reference genome snippy_variants_percent_ref_coverage Float Proportion of the reference genome covered by reads with a depth greater than or equal to the min_coverage threshold (default is 10). snippy_variants_qc_metrics File TSV file containing quality control metrics for the sample snippy_variants_query String Query strings specified by the user when running the workflow snippy_variants_query_check String Verification that query strings are found in the reference genome snippy_variants_results File CSV file detailing results for all mutations identified in the query sequence relative to the reference snippy_variants_summary File A summary TXT fie showing the number of mutations identified for each mutation type snippy_variants_version String Version of Snippy used snippy_variants_wf_version String Version of Snippy_Variants used"},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/","title":"Samples_to_Ref_Tree","text":""},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Placement Viral PHB v2.1.0 Yes Sample-level, Set-level"},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/#samples_to_ref_tree_phb","title":"Samples_to_Ref_Tree_PHB","text":"Nextclade rapidly places new samples onto an existing reference phylogenetic tree. Phylogenetic placement is done by comparing the mutations of the query sequence (relative to the reference) with the mutations of every node and tip in the reference tree, and finding the node which has the most similar set of mutations. This operation is repeated for each query sequence, until all of them are placed onto the tree. This workflow uses the Nextstrain-maintained nextclade datasets for SARS-CoV-2, mpox, influenza A and B, and RSV-A and RSV-B. The organism must be specified as input in the field organism, and these align with the nextclade dataset names, i.e. \" sars-cov-2\", \"flu_h1n1pdm_ha\", \"flu_h1n1pdm_na\", \"flu_h3n2_ha\", \"flu_h3n2_na\", \"flu_vic_ha\", \"flu_vic_na\", \"flu_yam_ha\", \"hMPXV\", \"hMPXV_B1\", \"MPXV\", \"rsv_a\" and \"rsv_b\".
However, nextclade can be used on any organism as long as an an existing, high-quality input reference tree with representative samples on it is provided, in addition to other optional inputs. Contact us if you need help generating your own mutation-annotated tree, or follow the instructions available on the Augur wiki here.
Placement not construction
This workflow is not for building a tree from scratch, but rather for the placement of new sequences onto an existing high-quality input reference tree with representative samples on it. In effect, query samples are only compared to reference samples and never to the other query samples.
"},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status nextclade_addToRefTree assembly_fasta File A fasta file with query sequence(s) to be placed onto the global tree Required nextclade_addToRefTree nextclade_dataset_name String What nextclade dataset name to run nextclade on; the options are: \"sars-cov-2\", \"flu_h1n1pdm_ha\", \"flu_h1n1pdm_na\", \"flu_h3n2_ha\", \"flu_h3n2_na\", \"flu_vic_ha\", \"flu_vic_na\", \"flu_yam_ha\", \"hMPXV\", \"hMPXV_B1\", \"MPXV\", \"rsv_a\" and \"rsv_b\" Required nextclade_add_ref cpu Int Number of CPUs to allocate to the task 2 Optional nextclade_add_ref disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional nextclade_add_ref docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/nextstrain/nextclade:3.3.1 Optional nextclade_add_ref memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional nextclade_add_ref verbosity String Set the nextclade output verbosity level. Options: off, error, warn, info, debug, trace \"warn\" Optional nextclade_addToRefTree dataset_tag String nextclade dataset tag Uses the dataset tag associated with the nextclade docker image version Optional nextclade_addToRefTree gene_annotations_gff File A genome annotations file for codon-aware alignment, gene translation and calling of aminoacid mutations Uses the genome annotation associated with the nextclade dataset name Optional nextclade_addToRefTree input_ref File An optional FASTA file containing reference sequence. This file should contain exactly 1 sequence. Uses the reference fasta associated with the specified nextclade dataset name Optional nextclade_addToRefTree nextclade_pathogen_json File An optional pathogen JSON file containing configuration and data specific to a pathogen. Uses the reference pathogen JSON file associated with the specified nextclade dataset name Optional nextclade_addToRefTree reference_tree_json File An optional phylogenetic reference tree file which serves as a target for phylogenetic placement Uses the reference tree associated with the specified nextclade dataset name Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/#outputs","title":"Outputs","text":"Variable Type Description treeUpdate_auspice_json File Phylogenetic tree with user placed samples treeUpdate_nextclade_docker String Nextclade docker image used treeUpdate_nextclade_json File JSON file with the results of the Nextclade analysis treeUpdate_nextclade_tsv File Tab-delimited file with Nextclade results treeUpdate_nextclade_version String Nextclade version used samples_to_ref_tree_analysis_date String Date of analysis samples_to_ref_tree_version String Version of the Public Health Bioinformatics (PHB) repository used"},{"location":"workflows/phylogenetic_placement/usher/","title":"Usher","text":""},{"location":"workflows/phylogenetic_placement/usher/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Placement Viral PHB v2.1.0 Yes Sample-level, set-level"},{"location":"workflows/phylogenetic_placement/usher/#usher_phb","title":"Usher_PHB","text":"UShER (Ultrafast Sample Placement on Existing Trees) rapidly places new samples onto an existing phylogeny using maximum parsimony. This workflow uses the UCSC-maintained global trees for SARS-CoV-2, mpox, RSV-A, and RSV-B if those organisms are specified in the organism input field. However, UShER can be used on any organism as long as a mutation-annotated tree (MAT) is provided in protobuf format. Contact us if you need help generating your own mutation-annotated tree, or follow the instructions available on the UShER wiki here.
While this workflow is technically a set-level workflow, it works on the sample-level too. When run on the set-level, the samples are placed with respect to each other.
Terra Task Name Variable Type Description Default Value Terra Status usher_workflow assembly_fasta Array[File] The assembly files for the samples you want to place on the pre-existing; can either be a set of samples, an individual sample, or multiple individual samples Required usher_workflow organism String What organism to run UShER on; the following organism have default global phylogenies and reference files provided: sars-cov-2, mpox, RSV-A, RSV-B. Required usher_workflow tree_name String The output prefix for the uncondensed tree output and the clades output. Required usher cpu Int Number of CPUs to allocate to the task 8 Optional usher disk_size Int Amount of storage (in GB) to allocate to the task 200 Optional usher docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/pathogengenomics/usher:0.6.2 Optional usher memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional usher mutation_annotated_tree_pb File Required for organisms other than sars-cov-2, mpox, RSV-A or RSV-B. This is the mutation-annotated global phylogeny upon which your samples will be placed Optional, Required usher reference_genome File Required for organisms other than sars-cov-2, mpox, RSV-A or RSV-B. This is the reference genome used to determine your sequence's mutations to accurately place the sample on the phylogeny. Optional, Required usher subtree_size Int Indicates how many of the closest-related samples you want to show in a subtree; more subtrees are made if there is more sequence diversity in the set of input samples (multiple subtrees are only generated if this workflow is run on the set level). 20 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_placement/usher/#outputs","title":"Outputs","text":"Variable Type Description usher_clades File The clades predicted for the samples usher_phb_analysis_date String The date the analysis was run usher_phb_version String The version of PHB the workflow is from usher_protobuf_version String The version of the mutation-annotated protobuf tree (what day and what samples are included, if a default organism was used; otherwise, says it was user-provided) usher_subtree_mutations Array[File] An array of files showing the mutations at each internal node for the subtree usher_subtrees Array[File] An array of subtrees where your samples have been placed usher_uncondensed_tree File The entire global tree with your samples included (warning: may be a very large file if the organism is \"sars-cov-2\") usher_version String The version of UShER used"},{"location":"workflows/public_data_sharing/fetch_srr_accession/","title":"Fetch SRR Accession Workflow","text":""},{"location":"workflows/public_data_sharing/fetch_srr_accession/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Public Data Sharing Any Taxa PHB v2.3.0 Yes Sample-level"},{"location":"workflows/public_data_sharing/fetch_srr_accession/#fetch-srr-accession","title":"Fetch SRR Accession","text":"This workflow retrieves the Sequence Read Archive (SRA) accession (SRR) associated with a given sample accession. The primary inputs are BioSample IDs (e.g., SAMN00000000) or SRA Experiment IDs (e.g., SRX000000), which link to sequencing data in the SRA repository.
The workflow uses the fastq-dl tool to fetch metadata from SRA and specifically parses this metadata to extract the associated SRR accession and outputs the SRR accession.
"},{"location":"workflows/public_data_sharing/fetch_srr_accession/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status fetch_srr_metadata sample_accession String SRA-compatible accession, such as a BioSample ID (e.g., \"SAMN00000000\") or SRA Experiment ID (e.g., \"SRX000000\"), used to retrieve SRR metadata. Required fetch_srr_metadata cpu Int Number of CPUs allocated for the task. 2 Optional fetch_srr_metadata disk_size Int Disk space in GB allocated for the task. 10 Optional fetch_srr_metadata docker String Docker image for metadata retrieval.us-docker.pkg.dev/general-theiagen/biocontainers/fastq-dl:2.0.4--pyhdfd78af_0 Optional fetch_srr_metadata memory Int Memory in GB allocated for the task. 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/public_data_sharing/fetch_srr_accession/#workflow-tasks","title":"Workflow Tasks","text":"This workflow has a single task that performs metadata retrieval for the specified sample accession.
fastq-dl: Fetches SRR metadata for sample accession When provided a BioSample accession or SRA experiment ID, 'fastq-dl' collects metadata and returns the appropriate SRR accession.
fastq-dl Technical Details
Links Task Task on GitHub Software Source Code fastq-dl Source Software Documentation fastq-dl Documentation Original Publication fastq-dl: A fast and reliable tool for downloading SRA metadata"},{"location":"workflows/public_data_sharing/fetch_srr_accession/#outputs","title":"Outputs","text":"Variable Type Description srr_accession String The SRR accession's associated with the input sample accession. fetch_srr_accession_version String The version of the fetch_srr_accession workflow. fetch_srr_accession_analysis_date String The date the fetch_srr_accession analysis was run."},{"location":"workflows/public_data_sharing/fetch_srr_accession/#references","title":"References","text":"Valieris, R. et al., \"fastq-dl: A fast and reliable tool for downloading SRA metadata.\" Bioinformatics, 2021.
"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/","title":"Mercury_Prep_N_Batch","text":""},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Public Data Sharing Viral PHB v2.2.0 Yes Set-level"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#mercury_prep_n_batch_phb","title":"Mercury_Prep_N_Batch_PHB","text":"Mercury prepares and formats metadata and sequencing files\u00a0located in Google Cloud Platform (GCP) buckets\u00a0for submission to national & international databases, currently NCBI & GISAID. Mercury was initially developed to ingest read, assembly, and metadata files associated with SARS-CoV-2 amplicon reads from clinical samples and format that data for submission per the\u00a0Public Health Alliance for Genomic Epidemiology (PH4GE)'s SARS-CoV-2 Contextual Data Specifications.
Currently, Mercury supports submission preparation for SARS-CoV-2, mpox, and influenza. These organisms have different metadata requirements, and are submitted to different repositories; the following table lists the repositories for each organism & what is supported in Mercury:
BankIt (NCBI) BioSample (NCBI) GenBank (NCBI) GISAID SRA (NCBI)\"flu\" \u2713 \u2713 \"mpox\" \u2713 \u2713 \u2713 \u2713 \"sars-cov-2\" \u2713 \u2713 \u2713 \u2713 Mercury expects data tables made with TheiaCoV
Mercury was designed to work with metadata tables that were partially created after running the TheiaCoV workflows. If you are using a different pipeline, please ensure that the metadata table is formatted correctly. See this file for the hard-coded list of all of the different metadata fields expected for each organism.
"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#metadata-formatters","title":"Metadata Formatters","text":"To help users collect all required metadata, we have created the following Excel spreadsheets that can help you collect the necessary metadata and allow for easy upload of this metadata into your Terra data tables:
For fluFlu Metadata Formatter
Flu uses the same metadata formatter as the Terra_2_NCBI Pathogen BioSample package.
If neither strain nor isolate are found in the Terra data table, Mercury will automatically generate an isolate, using the following format ABRicate flu type / State / sample name / year (ABRicate flu subtype). Example: A/California/Sample-01/2024 (H1N1)
The ABRicate flu type and subtype (abricate_flu_type and abricate_flu_subtype columns) are extracted from your table, and are required to generate the isolate field if it is not provided.
Mpox Metadata Formatter
For sars-cov-2SARS-CoV-2 Metadata Formatter
Usage on Terra
"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#usage-on-terra","title":"Usage on Terra","text":"A note on the using_clearlabs_data\u00a0&\u00a0using_reads_dehosted optional input parameters
The\u00a0using_clearlabs_data\u00a0and\u00a0using_reads_dehosted\u00a0arguments change the default values for the\u00a0read1_column_name,\u00a0assembly_fasta_column_name, and\u00a0assembly_mean_coverage_column_name\u00a0metadata columns. The default values are shown in the table below in addition to what they are changed to depending on what arguments are used.
using_clearlabs_data with\u00a0using_reads_dehosted with both\u00a0 using_clearlabs_data and using_reads_dehosted read1_column_name \"read1_dehosted\" \"clearlabs_fastq_gz\" \"reads_dehosted\" \"reads_dehosted\" assembly_fasta_column_name \"assembly_fasta\" \"clearlabs_fasta\" \"assembly_fasta\" \"clearlabs_fasta\" assembly_mean_coverage_column_name \"assembly_mean_coverage\" \"clearlabs_sequencing_depth\" \"assembly_mean_coverage\" \"clearlabs_sequencing_depth\""},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#inputs","title":"Inputs","text":"Use the sample table for the terra_table_name input
Make sure your entry for terra_table_name is for the sample table! While the root entity needs to be the set table, the input value for terra_table_name should be the sample table.
This workflow runs on the set-level.
Terra Task Name Variable Type Description Default Value Terra Status mercury_prep_n_batch gcp_bucket_uri String Google bucket where your SRA reads will be temporarily stored before transferring to SRA. Example: \"gs://theiagen_sra_transfer\" Required mercury_prep_n_batch sample_names Array[String] The samples you want to submit Required mercury_prep_n_batch terra_project_name String The name of your Terra project. You can find this information in the URL of the webpage of your Terra dashboard. For example, if your URL contains#workspaces/example/my_workspace/ then your project name is example Required mercury_prep_n_batch terra_table_name String The name of the Terra table where your samples can be found. Do not include the entity: prefix, the _id suffix, or the _set_id suffix, just the name of the sample-level data table as listed in the sidebar on lefthand side of the Terra Data tab. Required mercury_prep_n_batch terra_workspace_name String The name of your Terra workspace where your samples can be found. For example, if your URL contains #workspaces/example/my_workspace/ then your project name is my_workspace Required download_terra_table cpu Int Number of CPUs to allocate to the task 1 Optional download_terra_table disk_size Int Amount of storage (in GB) to allocate to the task 10 Optional download_terra_table docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-06-21 Optional download_terra_table memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional mercury cpu Int Number of CPUs to allocate to the task 2 Optional mercury disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional mercury docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/mercury:1.0.9 Optional mercury memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional mercury number_N_threshold Int Only for \"sars-cov-2\" submissions; used to filter out any samples that contain more than the indicated number of Ns in the assembly file 5000 Optional mercury single_end Boolean Set to true if your data is single-end; this ensures that a read2 column is not included in the metadata FALSE Optional mercury skip_county Boolean Use if your Terra table contains a county column that you do not want to include in your submission. FALSE Optional mercury usa_territory Boolean If true, the \"state\" column will be used in place of the \"country\" column. For example, if \"state\" is Puerto Rico, then the GISAID virus name will be\u00a0hCoV-19/Puerto Rico/<name>/<year>. The NCBI\u00a0geo_loc_name\u00a0will be\u00a0\"USA: Puerto Rico\". This optional Boolean variable should only be used with clear understanding of what it does. FALSE Optional mercury using_clearlabs_data Boolean When set to true will change read1_dehosted \u2192 clearlabs_fastq_gz; assembly_fasta \u2192 clearlabs_fasta; assembly_mean_coverage \u2192 clearlabs_sequencing_depth FALSE Optional mercury using_reads_dehosted Boolean When set to true will only change read1_dehosted \u2192 reads_dehosted. Takes priority over the replacement for read1_dehosted made with the using_clearlabs_data Boolean input FALSE Optional mercury vadr_alert_limit Int Only for \"sars-cov-2\" submissions; used to filter out any samples that contain more than the indicated number of vadr alerts 0 Optional mercury_prep_n_batch authors_sbt File Only for \"mpox\" submissions; a file that contains author information. This file can be created here: Optional mercury_prep_n_batch organism String The organism that you want submission prepare for \u2014 each organism requires different metadata fields so please ensure this field is accurate. Options: \"flu\", \"mpox\"\" or \"sars-cov-2\" sars-cov-2 Optional mercury_prep_n_batch output_name String Free text prefix for all output files mercury Optional mercury_prep_n_batch skip_ncbi Boolean Set to true if you only want to prepare GISAID submission files FALSE Optional table2asn cpu Int Number of CPUs to allocate to the task 1 Optional table2asn disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional table2asn docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ncbi-table2asn:1.26.678 Optional table2asn memory Int Amount of memory/RAM (in GB) to allocate to the task 1 Optional trim_genbank_fastas cpu Int Number of CPUs to allocate to the task 1 Optional trim_genbank_fastas disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional trim_genbank_fastas docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/vadr:1.3 Optional trim_genbank_fastas max_length Int Only for \"sars-cov-2\" submissions; the maximum genome length for trimming terminal ambiguous nucleotides. If your sample's genome is higher than this value, the workflow will error/fail. 30000 Optional trim_genbank_fastas memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional trim_genbank_fastas min_length Int Only for \"sars-cov-2\" submissions; the minimum genome length for trimming terminal ambiguous nucleotides. If your sample's genome is lower than this value, the workflow will error/fail. 50 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#outputs","title":"Outputs","text":"Variable Type Description bankit_sqn_to_email File Only for mpox submission: the sqn file that you will use to submit mpox assembly files to NCBI via email biosample_metadata File BioSample metadata TSV file for upload to NCBI excluded_samples File A file that contains the names and reasons why a sample was excluded from submission. For SARS-CoV-2, there are two sections: First, a section for any samples that failed to meet pre-determined quality thresholds (number_N and vadr_num_alert). Second, a section that includes a table that describes any missing required metadata for each sample. This table has the sample name for rows and any columns that have missing metadata as headers. If a sample is missing a piece of required metadata, the corresponding cell will be blank. However, if a different sample does have metadata for that column, the associated value will appear in the corresponding cell. For flu and mpox, only the second section described above exists. Please see the example below for more details. genbank_fasta File Only for SARS-CoV-2 submission: GenBank fasta file for upload genbank_metadata File Only for SARS-CoV-2 submission: GenBank metadata for upload gisaid_fasta File Only for mpox and SARS-CoV-2 submission: GISAID fasta file for upload gisaid_metadata File Only for mpox and SARS-CoV-2 submission: GISAID metadata for upload mercury_prep_n_batch_analysis_date String Date analysis was run mercury_prep_n_batch_version String Version of the PHB repository that hosts this workflow mercury_script_version String Version of the Mercury tool that was used in this workflow sra_metadata File SRA metadata TSV file for upload An example excluded_samples.tsv file"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#example-excluded-samples","title":"An example excluded_samples.tsv file","text":"Due to the nature of tsv files, it may be easier to download and open this file in Excel.
example_excluded_samples.tsv
Samples excluded for quality thresholds:\nsample_name message \nsample2 VADR skipped due to poor assembly\nsample3 VADR number alerts too high: 3 greater than limit of 0\nsample4 Number of Ns was too high: 10000 greater than limit of 5000\n\nSamples excluded for missing required metadata (will have empty values in indicated columns):\ntablename_id organism country library_layout\nsample5 paired\nsample6 SARS-CoV-2 USA\nThis example informs the user that samples 2-4 were excluded for quality reasons (the exact reason is listed in the message column), and that samples 5 and 6 were excluded because they were missing required metadata fields (sample5 was missing the organism and country fields, and sample6 was missing the library_layout field).
This tool can also be used on the command-line. Please see the Mercury GitHub for more information on how to run Mercury with a Docker image or in your local command-line environment.
"},{"location":"workflows/public_data_sharing/terra_2_gisaid/","title":"Terra_2_GISAID","text":""},{"location":"workflows/public_data_sharing/terra_2_gisaid/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Public Data Sharing Viral PHB v1.2.1 Yes Set-level"},{"location":"workflows/public_data_sharing/terra_2_gisaid/#terra_2_gisaid_phb","title":"Terra_2_GISAID_PHB","text":"Terra_2_GISAID programmatically submits SARS-CoV-2 assembly files to GISAID.
This workflow expects data that has been prepared for submission using either Mercury_Batch or Mercury_Prep_N_Batch (recommended).
client-ID
To obtain a client-ID, contact clisupport@gisaid.org and include your username in your request.
The optional variable frameshift_notification has three options that correspond to the associated web-browser options:
GISAID Credentials
Please note that the user must provide either an authentication_file or a gisaid_credentials file to run this workflow; explanations for both can be found in the table below.
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status Terra_2_GISAID client_id String This value should be filled with the client-ID provided by GISAID Required Terra_2_GISAID concatenated_fastas File The GISAID FASTA file generated by Mercury_Prep_N_Batch (or Mercury_Prep) Required Terra_2_GISAID concatenated_metadata File The GISAID metadata file generated by Mercury_Prep_N_Batch (or Mercury_Prep) Required gisaid_upload authentication_file File [EITHER] The GISAID authentication file generated by running cli3 authenticate for the submitter. Optional, Required gisaid_upload cpu Int Number of CPUs to allocate to the task 1 Optional gisaid_upload disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional gisaid_upload docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/broadinstitute/gisaid-cli:3.0 Optional gisaid_upload frameshift_notification String See top of inputs section for explanation; the notification preference regarding frameshifts in your submission catch_novel Optional gisaid_upload gisaid_credentials File [EITHER] A tab-delimited file containing the submitter's GISAID username followed by their password, used to generate the GISAID authentication file. Optional, Required gisaid_upload memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/public_data_sharing/terra_2_gisaid/#outputs","title":"Outputs","text":"Variable Type Description failed_uploads Boolean The metadata for any failed uploads gisaid_cli_version String The verison of the GISAID CLI tool gisaid_logs File The log files regarding the submission terra_2_gisaid_analysis_date String The date of the analysis terra_2_gisaid_version String The version of the PHB repository that this workflow is hosted in"},{"location":"workflows/public_data_sharing/terra_2_ncbi/","title":"Terra_2_NCBI","text":""},{"location":"workflows/public_data_sharing/terra_2_ncbi/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Public Data Sharing Bacteria, Mycotics Viral PHB v2.1.0 No Set-level"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#terra_2_ncbi_phb","title":"Terra_2_NCBI_PHB","text":"Do not resubmit!
If the Terra_2_NCBI workflow fails, DO NOT resubmit.
Resubmission risks duplicate submissions and future failures.
Contact Theiagen (support@theiagen.com) to determine the reason for failure, and only move forward with Theiagen's guidance.
Key Resources
The Terra_2_NCBI workflow is a programmatic data submission method to share metadata information with NCBI BioSample and paired-end Illumina reads with NCBI SRA directly from Terra without having to use the NCBI portal.
"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#prerequisites","title":"Prerequisites","text":"Before running the Terra_2_NCBI workflowThe user must have access to the NCBI FTP. To gain these credentials, we recommend emailing **sra@ncbi.nlm.nih.gov** a variation of the following example, including all the information:
Hello,
We would like to automate submissions to the Submission Portal using XML metadata to accompany our cloud-hosted data files.\u00a0\u00a0We would like to upload via FTP and need to create a submission group.
Here is the relevant information:
We will be using an existing submission pipeline that is known to work and would like to request that the production folder be activated.\u00a0Thank you for your assistance!
From NCBI, you will need to get in response:
Please confirm that the production folder has been activated, or else the submission pipeline will either fail or only run test submissions and not actually submit to NCBI.
Before you can run the workflow for the first time, we also recommend scheduling a meeting with Theiagen to get additional things set up, including
The configuration file tells the workflow your username and password so you can access the FTP. It also provides important information about who should be contacted regarding the submission. We recommend contacting a member of Theiagen for help in the creation of this configuration file to ensure that everything is formatted correctly.
In order to create BioSamples, you need to choose the correct BioSample package and have the appropriate metadata included in your data table.
Currently, Terra_2_NCBI only supports Pathogen, Virus, and Microbe BioSample packages. Most organisms should be submitted using the Pathogen package unless you have been specifically directed otherwise (either through CDC communications or another reliable source). Definitions of packages supported by Terra_2_NCBI are listed below with more requirements provided via the links:
For each package, we have created a metadata template spreadsheet to help you organize your metadata:
Please note that the pathogen metadata formatter is for the clinical pathogen package, not the environmental pathogen.
We are constantly working on improving these spreadsheets and they will be updated in due course.
"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#running-the-workflow","title":"Running the Workflow","text":"We recommend running a test submission before your first production submission to ensure that all data has been formatted correctly. Please contact Theiagen (support@theiagen.com) to get this set up.
In the test submission, any real BioProject accession numbers you provide will not be recognized. You will have to make a \"fake\" or \"test\" BioProject. This cannot be done through the NCBI portal. Theiagen can provide assistance in creating this as it requires manual command-line work on the NCBI FTP using the account they provided for you.
What's the difference between a test submission and a production submission?A production submission means that your submission using Terra_2_NCBI will be submitted to NCBI as if you were using the online portal. That means that anything you submit on production will be given to the *real* NCBI servers and appear and become searchable on the NCBI website.
A test submission gives your data to a completely detached replica of the production server. This means that any data you submit as a test will behave exactly like a real submission, but since it's detached, nothing will appear on the NCBI website, and anything returned from the workflow (such as BioSample accession numbers) will be fake. If you search for these test BioSample accession numbers on the NCBI website, either (a) nothing will appear, or (b) it will link to a random sample.
If you want your data to be on NCBI, you must run a production submission. Initially, NCBI locks the production folder so that the user doesn't accidentally submit test data to the main database. You must have requested activation of the production folder prior to your first production submission.
"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#inputs","title":"Inputs","text":"This workflow runs on set-level data tables.
Production Submissions
Please note that an optional Boolean variable, submit_to_production, is required for a production submission.
The workflow will perform the following tasks, each highlighted as code
prune_tableformats all incoming metadata for submission.If you are submitting BioSamples:
biosample_submit_tsv_ftp_upload will add_biosample_accessions will
If BioSample accessions fail to be generated, this task ends the workflow and users should contact Theiagen for further support. Otherwise, the workflow will continue and outputs are returned to the Terra data table.
If BioSample accessions were generated or if BioSample submission was skipped
sra_tsv_to_xml converts the SRA metadata (including any generated or pre-provided BioSample accessions) into XML format.ncbi_sftp_upload If the workflow ends successfully, it returns the outputs to the Terra data table and the XML communications from NCBI will say that submission is underway. The workflow does not declare successful sample submission since SRA sometimes takes a while to do this. If the submission was successful, the point of contact for the submission will receive the SRA accessions via email from NCBI.
If the workflow ends unsuccessfully, no outputs will be shown on Terra and the biosample_status output variable will indicate that the BioSample submission failed.
The output files contain information mostly for debugging purposes. Additionally, if your submission is successful, the point of contact for the submission should also receive an email from NCBI notifying them of their submission success.
Variable Description Type biosample_failures Text file listing samples that failed BioSample submission File biosample_metadata Metadata used for BioSample submission in proper BioSample formatting File biosample_report_xmls One or more XML files that contain the response from NCBI regarding your BioSample submission. These can be pretty cryptic, but often contain information to determine if anything went wrong Array[File] biosample_status String showing whether BioSample submission was successful String biosample_submission_xml XML file used to submit your BioSamples to NCBI File excluded_samples Text file listing samples that were excluded from BioSample submission for missing required metadata File generated_accessions Text file mapping the BioSample accession with its sample name. File sra_metadata Metadata used for SRA submission in proper SRA formatting File sra_report_xmls One or more XML files containing the response from NCBI regarding your SRA submission. These can be pretty cryptic, but often contain information to determine if anything went wrong Array[File] sra_submission_xml XML file that was used to submit your SRA reads to NCBI File terra_2_ncbi_analysis_date Date that the workflow was run String terra_2_ncbi_version Version of the PHB repository where the workflow is hosted String An example excluded_samples.tsv file"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#example-excluded-samples","title":"An example excluded_samples.tsv file","text":"Due to the nature of tsv files, it may be easier to download and open this file in Excel.
example_excluded_samples.tsv
Samples excluded for quality thresholds:\nsample_name message \nsample2 VADR skipped due to poor assembly\nsample3 VADR number alerts too high: 3 greater than limit of 0\nsample4 Number of Ns was too high: 10000 greater than limit of 5000\n\nSamples excluded for missing required metadata (will have empty values in indicated columns):\ntablename_id organism country library_layout\nsample5 paired\nsample6 SARS-CoV-2 USA\nThis example informs the user that samples 2-4 were excluded for quality reasons (the exact reason is listed in the message column), and that samples 5 and 6 were excluded because they were missing required metadata fields (sample5 was missing the organism and country fields, and sample6 was missing the library_layout field).
This workflow would not have been possible without the invaluable contributions of Dr. Danny Park.
"},{"location":"workflows/standalone/cauris_cladetyper/","title":"Cauris_CladeTyper","text":"NEEDS WORK!!!!
This page is under construction and will be updated soon.
"},{"location":"workflows/standalone/cauris_cladetyper/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Mycotics PHB v1.0.0 Yes Sample-level"},{"location":"workflows/standalone/cauris_cladetyper/#cauris_cladetyper_phb","title":"Cauris_CladeTyper_PHB","text":"The Cauris_CladeTyper_PHB Workflow is designed to assign clade to Candida auris Whole Genome Sequencing assemblies based on their genomic sequence similarity to the five clade-specific reference files. Clade typing is essential for understanding the epidemiology and evolutionary dynamics of this emerging multidrug-resistant fungal pathogen.
"},{"location":"workflows/standalone/cauris_cladetyper/#inputs","title":"Inputs","text":""},{"location":"workflows/standalone/cauris_cladetyper/#workflow-tasks","title":"Workflow Tasks","text":"The Cauris_Cladetyper Workflow for Candida auris employs GAMBIT for taxonomic identification, comparing whole genome sequencing data against reference databases to accurately classify Candida auris isolates. A custom database featuring five clade-specific Candida auris reference genomes facilitates clade typing. Sequences undergo genomic signature comparison against the custom database, enabling assignment to one of the five Candida auris clades (Clade I to Clade V) based on sequence similarity and phylogenetic relationships. This integrated approach ensures precise clade assignments, crucial for understanding the genetic diversity and epidemiology of Candida auris.
"},{"location":"workflows/standalone/cauris_cladetyper/#outputs","title":"Outputs","text":""},{"location":"workflows/standalone/gambit_query/","title":"GAMBIT_Query","text":""},{"location":"workflows/standalone/gambit_query/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Bacteria, Mycotics PHB v2.2.0 Yes Sample-level"},{"location":"workflows/standalone/gambit_query/#gambit_query_phb","title":"GAMBIT_Query_PHB","text":"The GAMBIT_Query_PHB workflow performs taxon assignment of a genome assembly using the GAMBIT task.
"},{"location":"workflows/standalone/gambit_query/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status gambit_query assembly_fasta File Assembly file in FASTA format Required gambit_query samplename String Sample name Required gambit cpu Int Number of CPUs to allocate to the task 8 Optional gambit disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional gambit memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional gambit docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/gambit:1.0.0\" Optional gambit gambit_db_genomes File Database of metadata for assembled query genomes; requires complementary signatures file. If not provided, uses default database \"/gambit-db\" \"gs://gambit-databases-rp/2.0.0/gambit-metadata-2.0.0-20240628.gdb\" Optional gambit gambit_db_signatures File Signatures file; requires complementary genomes file. If not specified, the file from the docker container will be used. \"gs://gambit-databases-rp/2.0.0/gambit-signatures-2.0.0-20240628.gs\" Optional"},{"location":"workflows/standalone/gambit_query/#workflow-tasks","title":"Workflow Tasks","text":"GAMBIT determines the taxon of the genome assembly using a k-mer based approach to match the assembly sequence to the closest complete genome in a database, thereby predicting its identity. Sometimes, GAMBIT can confidently designate the organism to the species level. Other times, it is more conservative and assigns it to a higher taxonomic rank.
For additional details regarding the GAMBIT tool and a list of available GAMBIT databases for analysis, please consult the GAMBIT tool documentation.
GAMBIT Technical Details
Links Task task_gambit.wdl Software Source Code GAMBIT on GitHub Software Documentation GAMBIT ReadTheDocs Original Publication(s) GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification"},{"location":"workflows/standalone/gambit_query/#outputs","title":"Outputs","text":"Variable Type Description gambit_closest_genomes File CSV file listing genomes in the GAMBIT database that are most similar to the query assembly gambit_db_version String Version of the GAMBIT database used gambit_docker String GAMBIT Docker used gambit_predicted_taxon String Taxon predicted by GAMBIT gambit_predicted_taxon_rank String Taxon rank of GAMBIT taxon prediction gambit_query_wf_analysis_date String Date of analysis gambit_query_wf_version String PHB repository version gambit_report File GAMBIT report in a machine-readable format gambit_version String Version of gambit software usedGAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification. Lumpe et al. PLOS ONE, 2022. DOI: 10.1371/journal.pone.0277575
"},{"location":"workflows/standalone/kraken2/","title":"Kraken2","text":""},{"location":"workflows/standalone/kraken2/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.0.0 Yes Sample-level"},{"location":"workflows/standalone/kraken2/#kraken2-workflows","title":"Kraken2 Workflows","text":"The Kraken2 workflows assess the taxonomic profile of raw sequencing data (FASTQ files).
Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
There are three Kraken2 workflows:
Kraken2_PE is compatible with Illumina paired-end dataKraken2_SE is compatible with Illumina single-end dataKraken2_ONT is compatible with Oxford Nanopore dataBesides the data input types, there are minimal differences between these two workflows.
Kraken2 Workflow Diagram
"},{"location":"workflows/standalone/kraken2/#databases","title":"Databases","text":"Database selection
The Kraken2 software is database-dependent and taxonomic assignments are highly sensitive to the database used. An appropriate database should contain the expected organism(s) (e.g. Escherichia coli) and other taxa that may be present in the reads (e.g. Citrobacter freundii, a common contaminant).
"},{"location":"workflows/standalone/kraken2/#suggested-databases","title":"Suggested databases","text":"Database name Database Description Suggested Applications GCP URI (for usage in Terra) Source Database Size (GB) Date of Last Update Kalamari v5.1 Kalamari is a database of complete public assemblies, that has been fine-tuned for enteric pathogens and is backed by trusted institutions. Full list available here ( in chromosomes.tsv and plasmids.tsv) Single-isolate enteric bacterial pathogen analysis (Salmonella, Escherichia, Shigella, Listeria, Campylobacter, Vibrio, Yersinia)gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2.kalamari_5.1.tar.gz \u2023 1.5 18/5/2022 standard 8GB Standard RefSeq database (archaea, bacteria, viral, plasmid, human, UniVec_Core) capped at 8GB Prokaryotic or viral organisms, but for enteric pathogens, we recommend Kalamari gs://theiagen-large-public-files-rp/terra/databases/kraken2/k2_standard_08gb_20240112.tar.gz https://benlangmead.github.io/aws-indexes/k2 7.5 12/1/2024 standard 16GB Standard RefSeq database (archaea, bacteria, viral, plasmid, human, UniVec_Core) capped at 16GB Prokaryotic or viral organisms, but for enteric pathogens, we recommend Kalamari gs://theiagen-large-public-files-rp/terra/databases/kraken2/k2_standard_16gb_20240112.tar.gz https://benlangmead.github.io/aws-indexes/k2 15 12/1/2024 standard Standard RefSeq database (archaea, bacteria, viral, plasmid, human, UniVec_Core) Prokaryotic or viral organisms, but for enteric pathogens, we recommend Kalamari gs://theiagen-large-public-files-rp/terra/databases/kraken2/k2_standard_20240112.tar.gz https://benlangmead.github.io/aws-indexes/k2 72 18/4/2023 viral RefSeq viral Viral metagenomics gs://theiagen-large-public-files-rp/terra/databases/kraken2/k2_viral_20240112.tar.gz https://benlangmead.github.io/aws-indexes/k2 0.6 12/1/2024 EuPathDB48 Eukaryotic pathogen genomes with contaminants removed. Full list available here Eukaryotic organisms (Candida spp., Aspergillus spp., etc) gs://theiagen-public-files-rp/terra/theiaprok-files/k2_eupathdb48_20201113.tar.gz https://benlangmead.github.io/aws-indexes/k2 30.3 13/11/2020 EuPathDB48 Eukaryotic pathogen genomes with contaminants removed. Full list available here Eukaryotic organisms (Candida spp., Aspergillus spp., etc) gs://theiagen-large-public-files-rp/terra/databases/kraken/k2_eupathdb48_20230407.tar.gz https://benlangmead.github.io/aws-indexes/k2 11 7/4/2023"},{"location":"workflows/standalone/kraken2/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status Workflow *workflow_name kraken2_db File A Kraken2 database in .tar.gz format Required ONT, PE, SE *workflow_name read1 File Required ONT, PE, SE *workflow_name read2 File Required for PE only PE *workflow_name samplename String Required ONT, PE, SE kraken2_pe or kraken2_se classified_out String Allows user to rename the classified FASTQ files output. Must include .fastq as the suffix classified#.fastq Optional ONT, PE, SE kraken2_pe or kraken2_se cpu Int Number of CPUs to allocate to the task 4 Optional ONT, PE, SE kraken2_pe or kraken2_se disk_size Int GB of storage to request for VM used to run the kraken2 task. Increase this when using large (>30GB kraken2 databases such as the \"k2_standard\" database) 100 Optional ONT, PE, SE kraken2_pe or kraken2_se docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/kraken2:2.1.2-no-db Optional ONT, PE, SE kraken2_pe or kraken2_se kraken2_args String Allows a user to supply additional kraken2 command-line arguments Optional ONT, PE, SE kraken2_pe or kraken2_se memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional ONT, PE, SE kraken2_pe or kraken2_se unclassified_out String Allows user to rename unclassified FASTQ files output. Must include .fastq as the suffix unclassified#.fastq Optional ONT, PE, SE krona cpu Int Number of CPUs to allocate to the task 4 Optional PE, SE krona disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE, SE krona docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/krona:2.7.1--pl526_5 Optional PE, SE krona memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE kraken2_recalculate_abundances cpu Int Number of CPUs to allocate to the task 4 Optional ONT kraken2_recalculate_abundances disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT kraken2_recalculate_abundances docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-08-28-v4 Optional ONT kraken2_recalculate_abundances memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional ONT kraken2_recalculate_abundances target_organism String Target organism for the kraken2 abundance to be exported to the data table Optional ONT version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional ONT, PE, SE version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional ONT, PE, SE"},{"location":"workflows/standalone/kraken2/#outputs","title":"Outputs","text":"Variable Type Description kraken2_classified_read1 File FASTQ file of classified forward/R1 reads kraken2_classified_read2 File FASTQ file of classified reverse/R2 reads (if PE) kraken2_classified_report File Standard Kraken2 output report. TXT filetype, but can be opened in Excel as a TSV file kraken2_docker String Docker image used to run kraken2 kraken2_*_wf_analysis_date String Date the workflow was run kraken2_*_wf_version String Workflow version kraken2_report File TXT document describing taxonomic prediction of every FASTQ record. This file is usually very large and cumbersome to open and view kraken2_unclassified_read1 File FASTQ file of unclassified forward/R1 reads kraken2_unclassified_read2 File FASTQ file of unclassified reverse/R2 reads (if PE) kraken2_version String kraken2 version krona_docker String Docker image used to run krona (if PE or SE) krona_html File HTML report of krona with visualisation of taxonomic classification of reads (if PE or SE) krona_version String krona version (if PE or SE)"},{"location":"workflows/standalone/kraken2/#interpretation-of-results","title":"Interpretation of results","text":"The most important outputs of the Kraken2 workflows are the kraken2_report files. These will include a breakdown of the number of sequences assigned to a particular taxon, and the percentage of reads assigned. A complete description of the report format can be found here.
When assessing the taxonomic identity of a single isolate's sequence, it is normal that a few reads are assigned to very closely rated taxa due to the shared sequence identity between them. \"Very closely related taxa\" may be genetically similar species in the same genus, or taxa with which the dominant species have undergone horizontal gene transfer. Unrelated taxa or a high abundance of these closely related taxa is indicative of contamination or sequencing of non-target taxa. Interpretation of the results is dependent on the biological context.
Example Kraken2 reportBelow is an example kraken2_report for a Klebsiella pneumoniae sample. Only the first 30 lines are included here since rows near the bottom are often spurious results with only a few reads assigned to a non-target organism.
From this report, we can see that 84.35 % of the reads were assigned at the species level (S in the 4th column) to \"Klebsiella pneumoniae\". Given almost 6 % of reads were \"unclassified\" and ~2 % of reads were assigned to very closely related taxa (in the Klebsiella genus), this suggests the reads are from Klebsiella pneumoniae with very little -if any- read contamination.
5.98 108155 108155 U 0 unclassified\n 94.02 1699669 0 C 1 \n 94.02 1699669 1862 C1 131567 cellular organisms\n 93.91 1697788 2590 D 2 Bacteria\n 93.75 1694805 6312 P 1224 Proteobacteria\n 93.39 1688284 37464 C 1236 Gammaproteobacteria\n 91.31 1650648 35278 O 91347 Enterobacterales\n 89.31 1614639 43698 F 543 Enterobacteriaceae\n 86.40 1561902 22513 G 570 Klebsiella\n **84.35 1524918 1524918 S 573 Klebsiella pneumoniae**\n 0.75 13596 13596 S 548 Klebsiella aerogenes\n 0.03 600 600 S 244366 Klebsiella variicola\n 0.01 253 253 S 571 Klebsiella oxytoca\n 0.00 17 17 S 1134687 Klebsiella michiganensis\n 0.00 3 0 G1 2608929 unclassified Klebsiella\n 0.00 3 3 S 1972757 Klebsiella sp. PO552\n 0.00 2 2 S 1463165 Klebsiella quasipneumoniae\n 0.17 3035 129 G 590 Salmonella\n 0.15 2728 909 S 28901 Salmonella enterica\n 0.03 582 582 S1 9000010 Salmonella enterica subsp. IIa\n 0.02 306 306 S1 59201 Salmonella enterica subsp. enterica\n 0.01 230 230 S1 9000014 Salmonella enterica subsp. IIIa\n 0.01 221 221 S1 9000015 Salmonella enterica subsp. IIIb\n 0.01 136 136 S1 9000016 Salmonella enterica subsp. IX\n 0.01 132 132 S1 9000011 Salmonella enterica subsp. IIb\n 0.01 122 122 S1 59208 Salmonella enterica subsp. VII\n 0.00 41 41 S1 59207 Salmonella enterica subsp. indica\n 0.00 25 25 S1 9000017 Salmonella enterica subsp. X\n 0.00 24 24 S1 9000009 Salmonella enterica subsp. VIII\n 0.01 178 178 S 54736 Salmonella bongori\nKrona produces an interactive report that allows hierarchical data, such as the one from Kraken2, to be explored with zooming, multi-layered pie charts. These pie charts are intuitive and highly responsive.
Krona will only output hierarchical results for bacterial organisms in its current implementation.
Example Krona reportBelow is an example of the krona_html for a metagenomic sample. Taxonomic rank is organised from the centre of the pie chart to the edge, with each slice representing the relative abundance of a given taxa in the sample.
Kraken2 Technical Details
Links Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown Original Publication(s) Improved metagenomic analysis with Kraken 2"},{"location":"workflows/standalone/ncbi_amrfinderplus/","title":"NCBI-AMRFinderPlus","text":""},{"location":"workflows/standalone/ncbi_amrfinderplus/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Bacteria, Mycotics PHB v2.2.0 Yes Sample-level"},{"location":"workflows/standalone/ncbi_amrfinderplus/#ncbiamrfinderplus_phb","title":"NCBIAMRFinderPlus_PHB","text":"AMRFinderPlus identifies acquired antimicrobial resistance (AMR) genes, virulence genes, and stress genes. Such AMR genes confer resistance to antibiotics, metals, biocides, heat, or acid. For some taxa (see here), AMRFinderPlus will provide taxa-specific results including filtering out genes that are almost ubiquitous in the taxa (intrinsic genes) and identifying resistance-associated point mutations. In TheiaProk, the taxon used by AMRFinderPlus is specified based on the gambit_predicted_taxon or a user-provided expected_taxon.
You can check if a gene or point mutation is in the AMRFinderPlus database here, find the sequences of reference genes here, and search the query Hidden Markov Models (HMMs) used by AMRFinderPlus to identify AMR genes and some stress and virulence proteins (here). The AMRFinderPlus database is updated frequently. You can ensure you are using the most up-to-date version by specifying the docker image as a workflow input. You might like to save this docker image as a workspace data element to make this easier.
"},{"location":"workflows/standalone/ncbi_amrfinderplus/#use-cases","title":"\ud83d\udccb Use Cases","text":"Feldgarden M, Brover V, Gonzalez-Escalona N, Frye JG, Haendiges J, Haft DH, Hoffmann M, Pettengill JB, Prasad AB, Tillman GE, Tyson GH, Klimke W. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep. 2021 Jun 16;11(1):12728. doi: 10.1038/s41598-021-91456-0. PMID: 34135355; PMCID: PMC8208984. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8208984/
https://github.com/ncbi/amr
"},{"location":"workflows/standalone/ncbi_scrub/","title":"NCBI_Scrub","text":""},{"location":"workflows/standalone/ncbi_scrub/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.2.1 Yes Sample-level"},{"location":"workflows/standalone/ncbi_scrub/#ncbi-scrub-workflows","title":"NCBI Scrub Workflows","text":"NCBI Scrub, also known as the human read removal tool (HRRT), is based on the SRA Taxonomy Analysis Tool that will take as input a FASTQ file, and produce as output a FASTQ file in which all reads identified as potentially of human origin are either removed (default) or masked with 'N'. There are three Kraken2 workflows:
NCBI_Scrub_PE is compatible with Illumina paired-end dataNCBI_Scrub_SE is compatible with Illumina single-end dataThis workflow is composed of two tasks, one to dehost the input reads and another to screen the clean reads with kraken2 and the viral+human database.
ncbi_scrub: human read removal tool Briefly, the HRRT employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records and subtracts any k-mers found in non-Eukaryota RefSeq records. The remaining set of k-mers compose the database used to identify human reads by the removal tool.
Tool Name Technical Details
Links Task task_ncbi_scrub.wdl Software Source Code HRRT on GitHub Software Documentation HRRT on NCBIkraken2: taxonomic profiling Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
Kraken2 is run on the set of raw reads, provided as input, as well as the set of clean reads that are resulted from the read_QC_trim workflow
Database-dependent
TheiaCoV automatically uses a viral-specific Kraken2 database.
Kraken2 Technical Details
Links Task task_kraken2.wdl Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/wiki Original Publication(s) Improved metagenomic analysis with Kraken 2"},{"location":"workflows/standalone/ncbi_scrub/#outputs","title":"Outputs","text":"Variable Type Description Workflow kraken_human_dehosted Float Percent of human read data detected using the Kraken2 software after host removal PE, SE kraken_report_dehosted File Full Kraken report after host removal PE, SE kraken_sc2_dehosted Float Percent of SARS-CoV-2 read data detected using the Kraken2 software after host removal PE, SE kraken_version_dehosted String Version of Kraken2 software used PE, SE ncbi_scrub_docker String Docker image used to run HRRT PE, SE ncbi_scrub_human_spots_removed Int Number of spots removed (or masked) PE, SE ncbi_scrub_pe_analysis_date String Date of analysis PE, SE ncbi_scrub_pe_version String Version of HRRT software used PE, SE read1_dehosted File Dehosted forward reads PE, SE read2_dehosted File Dehosted reverse reads PE"},{"location":"workflows/standalone/rasusa/","title":"RASUSA","text":""},{"location":"workflows/standalone/rasusa/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.0.0 Yes Sample-level"},{"location":"workflows/standalone/rasusa/#rasusa_phb","title":"RASUSA_PHB","text":"RASUSA functions to randomly downsample the number of raw reads to a user-defined threshold.
"},{"location":"workflows/standalone/rasusa/#use-cases","title":"\ud83d\udccb Use Cases","text":"Call-caching disabled
If using RASUSA_PHB workflow version v2.0.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is downloaded fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/standalone/rasusa/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Attribute Terra Status rasusa_workflow coverage Float Use to specify the desired coverage of reads after downsampling; actual coverage of subsampled reads will not be exact and may be slightly higher; always check the estimated clean coverage after performing downstream workflows to verify coverage values, when necessary Required rasusa_workflow genome_length String Input the approximate genome size expected in quotations; this is used to determine the number of bases required to achieve the desired coverage; acceptable metric suffixes include:b, k, m, g, and t for base, kilo, mega, giga, and tera, respectively Required rasusa_workflow read1 File FASTQ file containing read1 sequences Required rasusa_workflow read2 File FASTQ file containing read2 sequences Required rasusa_workflow samplename String Name of the sample to be analyzed Required rasusa_task bases String Explicitly define the number of bases required in the downsampled reads in quotations; when used, genome size and coverage are ignored; acceptable metric suffixes include: b, k, m, g, and t for base, kilo, mega, giga, and tera, respectively Optional rasusa_task cpu Int Number of CPUs to allocate to the task 4 Optional rasusa_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional rasusa_task docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/rasusa:0.7.0\" Optional rasusa_task frac Float Explicitly define the fraction of reads to keep in the subsample; when used, genome size and coverage are ignored; acceptable inputs include whole numbers and decimals, e.g. 50.0 will leave 50% of the reads in the subsample Optional rasusa_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional rasusa_task num Int Optional: explicitly define the number of reads in the subsample; when used, genome size and coverage are ignored; acceptable metric suffixes include: b, k, m, g, and t for base, kilo, mega, giga, and tera, respectively Optional rasusa_task seed Int Use to assign a name to the \"random seed\" that is used by the subsampler; i.e. this allows the exact same subsample to be produced from the same input file/s in subsequent runs when providing the seed identifier; do not input values for random downsampling Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/standalone/rasusa/#outputs","title":"Outputs","text":"Variable Type Description rasusa_version String Version of RASUSA used for the analysis rasusa_wf_analysis_date String Date of analysis rasusa_wf_version String Version of PHB used for the analysis read1_subsampled File New read1 FASTQ files downsampled to desired coverage read2_subsampled File New read2 FASTQ files downsampled to desired coverage Don't Forget!
Remember to use the subsampled reads in downstream analyses with this.read1_subsampled and this.read2_subsampled inputs.
Verify
Confirm reads were successfully subsampled before downstream analyses by comparing read file size/s to the original read file size/s
View file sizes by clicking on the read file listed in the Terra data table and looking at the file size
"},{"location":"workflows/standalone/rasusa/#references","title":"References","text":"Hall, M. B., (2022). Rasusa: Randomly subsample sequencing reads to a specified coverage. Journal of Open Source Software, 7(69), 3941,\u00a0https://doi.org/10.21105/joss.03941
"},{"location":"workflows/standalone/rename_fastq/","title":"Rename_FASTQ","text":""},{"location":"workflows/standalone/rename_fastq/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.1.0 Yes Sample-level"},{"location":"workflows/standalone/rename_fastq/#rename_fastq_phb","title":"Rename_FASTQ_PHB","text":"This sample-level workflow receives a read file or a pair of read files (FASTQ), compressed or uncompressed, and returns a new, renamed and compressed FASTQ file.
"},{"location":"workflows/standalone/rename_fastq/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status rename_fastq_files new_filename String New name for the FASTQ file(s) Required rename_fastq_files read1 File FASTQ file containing read1 sequences Required rename_fastq_files read2 File FASTQ file containing read2 sequences Optional rename_PE_files or rename_SE_files cpu Int Number of CPUs to allocate to the task 2 Optional rename_PE_files or rename_SE_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional rename_PE_files or rename_SE_files docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/ubuntu/ubuntu:jammy-20230816\" Optional rename_PE_files or rename_SE_files memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/standalone/rename_fastq/#outputs","title":"Outputs","text":"If a reverse read (read2) is provided, the files get renamed to the provided\u00a0new_filename\u00a0input with the notation\u00a0<new_filename>_R1.fastq.gz\u00a0and\u00a0<new_filename>_R2.fastq.gz. If only\u00a0read1\u00a0is provided, the file is renamed to\u00a0<new_filename>.fastq.gz.
This workflow is still in experimental research stages. Documentation is minimal as changes may occur in the code; it will be fleshed out when a stable state has been achieved.
"},{"location":"workflows/standalone/tbprofiler_tngs/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status tbprofiler_tngs read1 File Illumina forward read file in FASTQ file format (compression optional) Required tbprofiler_tngs read2 File Illumina reverse read file in FASTQ file format (compression optional) Required tbprofiler_tngs samplename String Name of sample to be analyzed Required tbp_parser coverage_regions_bed File A file that contains the regions to perform coverage analysis on Optional tbp_parser coverage_threshold Int The minimum percentage of a region to exceed the minimum depth for a region to pass QC in tbp_parser 100 Optional tbp_parser cpu Int Number of CPUs to allocate to the task 1 Optional tbp_parser disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional tbp_parser docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/tbp-parser:1.6.0 Optional tbp_parser etha237_frequency Float Minimum frequency for a mutation in ethA at protein position 237 to pass QC in tbp-parser 0.1 Optional tbp_parser expert_rule_regions_bed File A file that contains the regions where R mutations and expert rules are applied Optional tbp_parser memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional tbp_parser min_depth Int Minimum depth for a variant to pass QC in tbp_parser 10 Optional tbp_parser min_frequency Float Minimum allele frequency for a variant to pass QC in tbp-parser 0.1 Optional tbp_parser min_read_support Int Minimum read support for a variant to pass QC in tbp-parser 10 Optional tbp_parser operator String Fills the \"operator\" field in the tbp_parser output files Optional tbp_parser rpob449_frequency Float Minimum frequency for a mutation at protein position 449 to pass QC in tbp-parser 0.1 Optional tbp_parser rrl_frequency Float Minimum frequency for a mutation in rrl to pass QC in tbp-parser 0.1 Optional tbp_parser rrl_read_support Int Minimum read support for a mutation in rrl to pass QC in tbp-parser 10 Optional tbp_parser rrs_frequency Float Minimum frequency for a mutation in rrs to pass QC in tbp-parser 0.1 Optional tbp_parser rrs_read_support Int Minimum read support for a mutation in rrs to pass QC in tbp-parser 10 Optional tbp_parser sequencing_method String Fills out the \"seq_method\" field in the tbp_parser output files Optional tbp_parser tbp_parser_debug Boolean Activate the debug mode on tbp_parser; increases logging outputs FALSE Optional tbprofiler cov_frac_threshold Int A cutoff used to calculate the fraction of the region covered by \u2264 this value 1 Optional tbprofiler cpu Int Number of CPUs to allocate to the task 8 Optional tbprofiler disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional tbprofiler mapper String The mapping tool used in TBProfiler to align the reads to the reference genome; see TBProfiler's original documentation for available options. bwa Optional tbprofiler memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional tbprofiler min_af Float The minimum allele frequency to call a variant 0.1 Optional tbprofiler min_af_pred Float The minimum allele frequency to use a variant for resistance prediction 0.1 Optional tbprofiler min_depth Int The minimum depth for a variant to be called. 10 Optional tbprofiler ont_data Boolean Internal component; do not modify Do not modify, Optional tbprofiler tbprofiler_custom_db File TBProfiler uses by default the TBDB database; if you have a custom database you wish to use, you must provide a custom database in this field and set tbprofiler_run_custom_db to true Optional tbprofiler tbprofiler_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/tbprofiler:4.4.2 Optional tbprofiler tbprofiler_run_custom_db Boolean FALSE Optional tbprofiler variant_caller String Select a different variant caller for TBProfiler to use by writing it in this block; see TBProfiler's original documentation for available options. freebayes Optional tbprofiler variant_calling_params String Enter additional variant calling parameters in this free text input to customize how the variant caller works in TBProfiler Optional tbprofiler bases_to_crop Int Indicate the number of bases to remove from the start and end of the read 30 Optional trimmomatic_pe cpu Int Number of CPUs to allocate to the task 4 Optional trimmomatic_pe disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional trimmomatic_pe docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/trimmomatic:0.39 Optional trimmomatic_pe memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional trimmomatic_pe trimmomatic_args String Additional arguments to pass to trimmomatic. \"-phred33\" specifies the Phred Q score encoding which is almost always phred33 with modern sequence data. -phred33 Optional trimmomatic_pe trimmomatic_min_length Int Specifies minimum length of each read after trimming to be kept 75 Optional trimmomatic_pe trimmomatic_quality_trim_score Int The trimming quality score 30 Optional trimmomatic_pe trimmomatic_window_size Int The window size for trimming 4 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/standalone/tbprofiler_tngs/#terra-outputs","title":"Terra Outputs","text":"Variable Type Description tbp_parser_average_genome_depth Float The mean depth of coverage across all target regions included in the analysis tbp_parser_coverage_report File A file containing the breadth of coverage across each target loci tbp_parser_docker String The docker image and version tag for the tbp_parser tool tbp_parser_genome_percent_coverage Float The percent breadth of coverage across the entire genome tbp_parser_laboratorian_report_csv File An output file containing information regarding each mutation and its associated drug resistance profile in a CSV file. This file also contains two interpretation fields -- \"Looker\" and \"MDL\" which are generated using the CDC's expert rules for interpreting the severity of potential drug resistance mutations. tbp_parser_lims_report_csv File An output file formatted specifically for STAR LIMS. This CSV report summarizes the highest severity mutations for each antimicrobial and lists the relevant mutations for each gene. tbp_parser_looker_report_csv File An output file that contains condensed information suitable for generating a dashboard in Google's Looker studio. tbp_parser_version String The version number of tbp_parser tbprofiler_dr_type String The drug resistance category as determined by TBProfiler tbprofiler_main_lineage String The Mycobacterium tuberculosis lineage assignment as made by TBProfiler tbprofiler_median_coverage Int The median depth of coverage across the target loci tbprofiler_num_dr_variants String The total number of drug resistance conferring variants detected by TBProfiler tbprofiler_num_other_variants String The total number of non-drug resistance conferring variants detected by TBProfiler tbprofiler_output_alignment_bai File The index file associated with the binary alignment map of the input reads against the H37Rv genome tbprofiler_output_alignment_bam File The binary alignment map of the input reads against the H37Rv genome tbprofiler_pct_reads_mapped Float The percentage of reads that successfully mapped to the H37Rv genome tbprofiler_report_csv File The raw output file from TBProfiler tbprofiler_report_json File The json output file from TBProfiler tbprofiler_report_tsv File The TSV output file from TBProfiler tbprofiler_resistance_genes String The genes in which a mutation was detected that may be resistance conferring tbprofiler_sub_lineage String The Mycobacterium tuberculosis sub-lineage assignment as made by TBProfiler tbprofiler_tngs_wf_analysis_date String The date on which the workflow was run tbprofiler_tngs_wf_version String The version of the tbprofiler_tngs workflow used for this analysis tbprofiler_version String The version of TBProfiler used for this analysis trimmomatic_docker String The docker image used for the trimmomatic module in this workflow trimmomatic_read1_trimmed File The read1 file post trimming trimmomatic_read2_trimmed File The read2 file post trimming trimmomatic_stats File The read trimming statistics trimmomatic_version String The version of trimmomatic used in this analysis"},{"location":"workflows/standalone/theiavalidate/","title":"TheiaValidate","text":""},{"location":"workflows/standalone/theiavalidate/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.0.0 No"},{"location":"workflows/standalone/theiavalidate/#theiavalidate_phb","title":"TheiaValidate_PHB","text":"TheiaValidate Workflow Diagram
TheiaValidate performs basic comparisons between user-designated columns in two separate tables. We anticipate this workflow being run to determine if any differences exist between version releases or two workflows, such as TheiaProk_ONT vs TheiaProk_Illumina_PE. A summary PDF report is produced in addition to a Excel spreadsheet that lists the values for any columns that do not have matching content for a sample.
Warning
The two tables being compared must have both identical sample names and an equal number of samples. If not, validation will not work or (in the case of unequal number of samples) not be attempted.
In order to enable this workflow to function for different workflow series, we require users to provide a list of columns they want to compare between the two tables. Feel free to use the information below that Theiagen uses to compare versions of the three main workflow series as a starting point for your own validations:
Validation Starting Points
Workflow Series Validation Criteria TSV Columns to Compare TheiaCoV Workflows TheiaCov Validation Criteria abricate_flu_subtype,abricate_flu_type,assembly_length_unambiguous,assembly_mean_coverage,irma_subtype,irma_type,kraken_human,kraken_human_dehosted,kraken_sc2,kraken_sc2_dehosted,kraken_target_org,kraken_target_org_dehosted,nextclade_aa_dels,nextclade_aa_subs,nextclade_clade,nextclade_lineage,nextclade_tamiflu_resistance_aa_subs,num_reads_clean1,num_reads_clean2,number_N,pango_lineage,percent_reference_coverage,vadr_num_alerts TheiaEuk Workflows TheiaEuk Validation Criteria assembly_length,busco_results,clade_type,est_coverage_clean,est_coverage_raw,gambit_predicted_taxon,n50_value,num_reads_clean1,num_reads_clean2,number_contigs,quast_gc_percent,theiaeuk_snippy_variants_hits TheiaProk Workflows TheiaProk Validation Criteria abricate_abaum_plasmid_type_genes,agrvate_agr_group,amrfinderplus_amr_core_genes,amrfinderplus_amr_plus_genes,amrfinderplus_stress_genes,amrfinderplus_virulence_genes,ani_highest_percent,ani_top_species_match,assembly_length,busco_results,ectyper_predicted_serotype,emmtypingtool_emm_type,est_coverage_clean,est_coverage_raw,gambit_predicted_taxon,genotyphi_final_genotype,hicap_genes,hicap_serotype,kaptive_k_type,kleborate_genomic_resistance_mutations,kleborate_key_resistance_genes,kleborate_mlst_sequence_type,legsta_predicted_sbt,lissero_serotype,meningotype_serogroup,midas_primary_genus,midas_secondary_genus,midas_secondary_genus_abundance,n50_value,ngmaster_ngmast_sequence_type,ngmaster_ngstar_sequence_type,num_reads_clean1,num_reads_clean2,number_contigs,pasty_serogroup,pbptyper_predicted_1A_2B_2X,plasmidfinder_plasmids,poppunk_gps_cluster,seqsero2_predicted_serotype,seroba_ariba_serotype,seroba_serotype,serotypefinder_serotype,shigatyper_ipaB_presence_absence,shigatyper_predicted_serotype,shigeifinder_cluster,shigeifinder_serotype,sistr_predicted_serotype,sonneityping_final_genotype,spatyper_type,srst2_vibrio_serogroup,staphopiasccmec_types_and_mecA_presence,tbprofiler_main_lineage,tbprofiler_resistance_genes,ts_mlst_predicted_st,virulencefinder_hitsIf additional validation metrics are desired, the user has the ability to provide a validation_criteria_tsv file that specifies what type of comparison should be performed. There are several options for additional validation checks:
amrfinder_plus_genes which is a comma-delimited list of genes) for identical content \u2014 order does not matter; that is, mdsA,mdsB is determined to be same as mdsB,mdsA. The EXACT match does not consider these to be the same, but the SET match does.If a column consists of only GCP URIs (Google Cloud file paths), the files will be localized and compared with either an EXACT match or a SET match. In the SET match, the lines in the file are ordered before comparison. Results are returned to the summary table as expected. The results of each file comparison can be found in the theiavalidate_diffs output column.
Please note that all string inputs must be enclosed in quotation marks; for example, \"column1,column2\" or \"workspace1\"
Terra Task Name Variable Type Description Default Value Terra Status theiavalidate columns_to_compare String A comma-separated list of the columns the user wants to compare. Do not include whitespace. Required theiavalidate output_prefix String The prefix for the output files Required theiavalidate table1_name String The name of the first table Required theiavalidate table2_name String The name of the second table Required theiavalidate terra_project1_name String The name of the Terra project where table1_name can be found Required theiavalidate terra_workspace1_name String The name of the Terra workspace where table1_name can be found Required theiavalidate column_translation_tsv File If the user wants to link two columns of different names, they may supply a TSV file that provides a \"column translation\" between the two files (see the section below this table). Optional theiavalidate terra_project2_name String If the table2_name is located in a different Terra project, indicate it here. Otherwise, the workflow will look for table2_name in the Terra project indicated in terra_project1_name. value forterra_project1_name Optional theiavalidate terra_workspace2_name String If the table2_name is located in a different Terra workspace, indicate it here. Otherwise, the workflow will look for table2_name in the Terra workspace indicated in terra_workspace1_name. value for terra_workspace1_name Optional theiavalidate validation_criteria_tsv File If the user wants to specify a different comparison than the default exact string match, they may supply a TSV file that indicates the different options (see the section below this table). Optional compare_two_tsvs cpu Int Number of CPUs to allocate to the task 2 Optional compare_two_tsvs debug_output Boolean Set to true to enable more outputs; useful when debugging FALSE Optional compare_two_tsvs disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional compare_two_tsvs docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/theiavalidate:0.1.0 Optional compare_two_tsvs memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional compare_two_tsvs na_values String If the user knows a particular value in either table that they would like to be considered N/A, they can indicate those values in a comma-separated list here. Any changes here will overwrite the default and not append to the default list. Do not include whitespace. -1.#IND,1.#QNAN,1.#IND,-1.#QNAN,#N/A,N/A,n/a,,#NA,NULL,null,NaN,-NaN,nan,-nan,None Optional export_two_tsvs cpu Int Number of CPUs to allocate to the task 1 Optional export_two_tsvs disk_size Int Amount of storage (in GB) to allocate to the task 10 Optional The optional validation_criteria_tsv file takes the following format (tab-delimited; a header line is required):
column_name criteria\ncolumnB SET\ncolumnC IGNORE\ncolumnD 0.01\ncolumnE EXACT\nPlease see above for a description of all available criteria options (EXACT, IGNORE, SET, ).
The optional column_translation_tsv file takes the following format (tab-delimited; there can be no header line):
column_name_in_table1 column_name_in_table2\ncolumn_name_in_table2 column_name_in_table1\ninternal_column_name display_column_name\nPlease note that the name in the second column will be displayed and used in all output files.
Known Bug
There must be more than one line in the column_translation_tsv file or else this error will appear: AttributeError: 'str' object has no attribute 'to_dict'. To fix this error, add an additional line in the column_translation_tsv file, like the following: columnA columnA
Known Bug
If performing a comparison, all samples must have values for that column.
Call Caching Disabled
If using TheiaValidate workflow version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is compared fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/standalone/theiavalidate/#outputs","title":"Outputs","text":"Variable Type Description theiavalidate_criteria_differences File A TSV file that lists only the differences that fail to meet the validation criteria theiavalidate_date String The date the analysis was run theiavalidate_diffs Array[File] An array of files with a single file for each file comparison performed; only has values if a column with files is compared theiavalidate_exact_differences File A TSV file that lists all exact string match differences between samples theiavalidate_filtered_input_table1 File The first data table used for validation after removing unexamined columns and translating column names theiavalidate_filtered_input_table2 File The second data table used for validation after removing unexamined columns and translating column names theiavalidate_report File A PDF summary report theiavalidate_status String Indicates whether or not validation was attempted theiavalidate_version String The version of the TheiaValidate Python Docker theiavalidate_wf_version String The version of the PHB repository"},{"location":"workflows/standalone/theiavalidate/#example-data-and-outputs","title":"Example Data and Outputs","text":"To help demonstrate how TheiaValidate works, please observe the following example and outputs:
Table1 entity:example_table1_id columnA-string columnB-set columnC-ignore columnD-float columnE-missing sample1 option1 item1,item2,item3 cheese 1000 present sample2 option1 item1,item3,item2 cheesecake 12 present sample3 option2 item1,item2,item3 cake 14 present sample4 option1 item2,item1 cakebatter 3492 sample5 option2 item1,item2 batter 3 present Table2 entity:example_table2_id columnA-string columnB-set columnC-ignore columnD-float missing sample1 option1 item1,item3,item2 cheesecake 999 present sample2 option2 item1,item2,item3 batter 12 present sample3 option1 item1,item2 cheese 24 sample4 option1 item1,item2 cakebatter 728 sample5 option2 item1,item2,item3 batter 4 present Validation Criteria column criteria columnB-set SET columnC-ignore IGNORE columnD-float 0.01 columnE-missing EXACT Column Translation missing columnE-missing columnA-string columnA-stringNote: the second row translating columnA-string to itself is included to prevent the known bug explained above.
If the above inputs are provided, then the following output files will be generated:
filtered_example_table1.tsv
filtered_example_table2.tsv
example_summary.pdf
example_exact_differences.tsv
example_validation_criteria_differences.tsv
"},{"location":"workflows_overview/workflows_alphabetically/","title":"Alphabetical Workflows","text":"Sort by Workflow Type | Sort by Kingdom
Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Assembly_Fetch Download assemblies from NCBI, after optionally identifying the closest RefSeq reference genome to your own draft assembly Any taxa Sample-level Yes v1.3.0 Assembly_Fetch_PHB Augur Phylogenetic analysis for viral pathogens Viral Sample-level, Set-level Yes v2.1.0 Augur_Prep_PHB, Augur_PHB BaseSpace_Fetch Import data from BaseSpace into Terra Any taxa Sample-level Yes v2.0.0 BaseSpace_Fetch_PHB Cauris_CladeTyper C. auris clade assignment Mycotics Sample-level Yes v1.0.0 Cauris_CladeTyper_PHB Concatenate_Column_Content Concatenate contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Concatenate_Column_Content_PHB Core_Gene_SNP Pangenome analysis Bacteria Set-level Some optional features incompatible, Yes v2.1.0 Core_Gene_SNP_PHB Create_Terra_Table Upload data to Terra and then run this workflow to have the table automatically created Any taxa Yes v2.2.0 Create_Terra_Table_PHB CZGenEpi_Prep Prepare metadata and fasta files for easy upload to the CZ GEN EPI platform. Monkeypox virus, SARS-CoV-2, Viral Set-level No v1.3.0 CZGenEpi_Prep_PHB Find_Shared_Variants Combines and reshapes variant data from Snippy_Variants to illustrate variants shared across multiple samples Bacteria, Mycotics Set-level Yes v2.0.0 Find_Shared_Variants_PHB Freyja Workflow Series Recovers relative lineage abundances from mixed sample data and generates visualizations SARS-CoV-2, Viral Sample-level, Set-level Yes v2.2.0 Freyja_FASTQ_PHB, Freyja_Plot_PHB, Freyja_Dashboard_PHB, Freyja_Update_PHB GAMBIT_Query Taxon identification of genome assembly using GAMBIT Bacteria, Mycotics Sample-level Yes v2.0.0 Gambit_Query_PHB Kraken2 Taxa identification from reads Any taxa Sample-level Yes v2.0.0 Kraken2_PE_PHB, Kraken2_SE_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB Lyve_SET Alignment of reads to a reference genome, SNP calling, curation of high quality SNPs, phylogenetic analysis Bacteria Set-level Yes v2.1.0 Lyve_SET_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB Mercury_Prep_N_Batch Prepare metadata and sequence data for submission to NCBI and GISAID Influenza, Monkeypox virus, SARS-CoV-2, Viral Set-level No v2.2.0 Mercury_Prep_N_Batch_PHB NCBI-AMRFinderPlus Runs NCBI's AMRFinderPlus on genome assemblies (bacterial and fungal) Bacteria, Mycotics Sample-level Yes v2.0.0 NCBI-AMRFinderPlus_PHB NCBI_Scrub Runs NCBI's HRRT on Illumina FASTQs Any taxa Sample-level Yes v2.2.1 NCBI_Scrub_PE_PHB, NCBI_Scrub_SE_PHB Pangolin_Update Update Pangolin assignments SARS-CoV-2, Viral Sample-level Yes v2.0.0 Pangolin_Update_PHB RASUSA Randomly subsample sequencing reads to a specified coverage Any taxa Sample-level Yes v2.0.0 RASUSA_PHB Rename_FASTQ Rename paired-end or single-end read files in a Terra data table in a non-destructive way Any taxa Sample-level Yes v2.1.0 Rename_FASTQ_PHB Samples_to_Ref_Tree Use Nextclade to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Samples_to_Ref_Tree_PHB Snippy_Streamline Implementation of Snippy workflows for phylogenetic analysis from reads, with optional dynamic reference selection Bacteria Set-level Yes v2.2.0 Snippy_Streamline_PHB Snippy_Streamline_FASTA Implementation of Snippy workflows for phylogenetic analysis from assembled genomes (in FASTA format), with optional dynamic reference selection Bacteria Set-level Yes v2.2.0 Snippy_Streamline_FASTA_PHB Snippy_Tree SNP-based phylogenetic analysis from reads, with option to mask recombination Bacteria Set-level Some optional features incompatible, Yes v2.1.0 Snippy_Tree_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.2.0 Snippy_Variants_PHB SRA_Fetch Import publicly available reads from SRA using SRR#, ERR# or DRR# Any taxa Sample-level Yes v2.2.0 SRA_Fetch_PHB TBProfiler_tNGS Performs in silico antimicrobial susceptibility testing on Mycobacterium tuberculosis targeted-NGS samples with TBProfiler and tbp-parser Bacteria, TB Sample-level Yes v2.0.0 TBProfiler_tNGS_PHB Terra_2_GISAID Upload of assembly data to GISAID SARS-CoV-2, Viral Set-level Yes v1.2.1 Terra_2_GISAID_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.1.0 Terra_2_NCBI_PHB TheiaCov Workflow Series Viral genome assembly, QC and characterization from amplicon sequencing HIV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level, Set-level Some optional features incompatible, Yes v2.2.0 TheiaCoV_Illumina_PE_PHB, TheiaCoV_Illumina_SE_PHB, TheiaCoV_ONT_PHB, TheiaCoV_ClearLabs_PHB, TheiaCoV_FASTA_PHB, TheiaCoV_FASTA_Batch_PHB TheiaEuk Mycotic genome assembly, QC and characterization from WGS data Mycotics Sample-level Some optional features incompatible, Yes v2.0.1 TheiaEuk_Illumina_PE_PHB TheiaMeta Genome assembly and QC from metagenomic sequencing Any taxa Sample-level Yes v2.0.0 TheiaMeta_Illumina_PE_PHB TheiaProk Workflow Series Bacterial genome assembly, QC and characterization from WGS data Bacteria Sample-level Some optional features incompatible, Yes v2.2.0 TheiaProk_Illumina_PE_PHB, TheiaProk_Illumina_SE_PHB, TheiaProk_ONT_PHB, TheiaProk_FASTA_PHB TheiaValidate This workflow performs basic comparisons between user-designated columns in two separate tables. Any taxa No v2.0.0 TheiaValidate_PHB Transfer_Column_Content Transfer contents of a specified Terra data table column for many samples (\"entities\") to a GCP storage bucket location Any taxa Set-level Yes v1.3.0 Transfer_Column_Content_PHB Samples_to_Ref_Tree Use UShER to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Usher_PHB Fetch_SRR_Accession Update SRR metadata in a Terra data table at the sample level Any taxa Yes v2.3.0 *Fetch_SRR_Accession_PHB Usher_PHB Update VADR assignments HAV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level Yes v1.2.1 VADR_Update_PHB Zip_Column_Content Zip contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Zip_Column_Content_PHBCommand-line compatibility is determined if the workflow can be run on a local command-line environment, providing all dependencies are installed, with either miniwdl or cromwell.\u00a0\u21a9
Sort by Type | Sort Alphabetically
"},{"location":"workflows_overview/workflows_kingdom/#any-taxa","title":"Any Taxa","text":"Name Description Taxa Workflow Level Command-line Compatible1 Last known changes Dockstore Assembly_Fetch Download assemblies from NCBI, after optionally identifying the closest RefSeq reference genome to your own draft assembly Any taxa Sample-level Yes v1.3.0 Assembly_Fetch_PHB BaseSpace_Fetch Import data from BaseSpace into Terra Any taxa Sample-level Yes v2.0.0 BaseSpace_Fetch_PHB Concatenate_Column_Content Concatenate contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Concatenate_Column_Content_PHB Create_Terra_Table Upload data to Terra and then run this workflow to have the table automatically created Any taxa Yes v2.2.0 Create_Terra_Table_PHB Kraken2 Taxa identification from reads Any taxa Sample-level Yes v2.0.0 Kraken2_PE_PHB, Kraken2_SE_PHB NCBI_Scrub Runs NCBI's HRRT on Illumina FASTQs Any taxa Sample-level Yes v2.2.1 NCBI_Scrub_PE_PHB, NCBI_Scrub_SE_PHB RASUSA Randomly subsample sequencing reads to a specified coverage Any taxa Sample-level Yes v2.0.0 RASUSA_PHB Rename_FASTQ Rename paired-end or single-end read files in a Terra data table in a non-destructive way Any taxa Sample-level Yes v2.1.0 Rename_FASTQ_PHB SRA_Fetch Import publicly available reads from SRA using SRR#, ERR# or DRR# Any taxa Sample-level Yes v2.2.0 SRA_Fetch_PHB TheiaMeta Genome assembly and QC from metagenomic sequencing Any taxa Sample-level Yes v2.0.0 TheiaMeta_Illumina_PE_PHB TheiaValidate This workflow performs basic comparisons between user-designated columns in two separate tables. Any taxa No v2.0.0 TheiaValidate_PHB Transfer_Column_Content Transfer contents of a specified Terra data table column for many samples (\"entities\") to a GCP storage bucket location Any taxa Set-level Yes v1.3.0 Transfer_Column_Content_PHB Fetch_SRR_Accession Update SRR metadata in a Terra data table at the sample level Any taxa Set-level Yes v2.3.0 Fetch_SRR_Accession_PHB Zip_Column_Content Zip contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Zip_Column_Content_PHB"},{"location":"workflows_overview/workflows_kingdom/#bacteria","title":"Bacteria","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Core_Gene_SNP Pangenome analysis Bacteria Set-level Some optional features incompatible, Yes v2.1.0 Core_Gene_SNP_PHB Find_Shared_Variants Combines and reshapes variant data from Snippy_Variants to illustrate variants shared across multiple samples Bacteria, Mycotics Set-level Yes v2.0.0 Find_Shared_Variants_PHB GAMBIT_Query Taxon identification of genome assembly using GAMBIT Bacteria, Mycotics Sample-level Yes v2.0.0 Gambit_Query_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB Lyve_SET Alignment of reads to a reference genome, SNP calling, curation of high quality SNPs, phylogenetic analysis Bacteria Set-level Yes v2.1.0 Lyve_SET_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB NCBI-AMRFinderPlus Runs NCBI's AMRFinderPlus on genome assemblies (bacterial and fungal) Bacteria, Mycotics Sample-level Yes v2.0.0 NCBI-AMRFinderPlus_PHB Snippy_Streamline Implementation of Snippy workflows for phylogenetic analysis from reads, with optional dynamic reference selection Bacteria Set-level Yes v2.2.0 Snippy_Streamline_PHB Snippy_Streamline_FASTA Implementation of Snippy workflows for phylogenetic analysis from assembled genomes (in FASTA format), with optional dynamic reference selection Bacteria Set-level Yes v2.2.0 Snippy_Streamline_FASTA_PHB Snippy_Tree SNP-based phylogenetic analysis from reads, with option to mask recombination Bacteria Set-level Some optional features incompatible, Yes v2.1.0 Snippy_Tree_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.2.0 Snippy_Variants_PHB TBProfiler_tNGS Performs in silico antimicrobial susceptibility testing on Mycobacterium tuberculosis targeted-NGS samples with TBProfiler and tbp-parser Bacteria, TB Sample-level Yes v2.0.0 TBProfiler_tNGS_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.1.0 Terra_2_NCBI_PHB TheiaProk Workflow Series Bacterial genome assembly, QC and characterization from WGS data Bacteria Sample-level Some optional features incompatible, Yes v2.2.0 TheiaProk_Illumina_PE_PHB, TheiaProk_Illumina_SE_PHB, TheiaProk_ONT_PHB, TheiaProk_FASTA_PHB"},{"location":"workflows_overview/workflows_kingdom/#mycotics","title":"Mycotics","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Cauris_CladeTyper C. auris clade assignment Mycotics Sample-level Yes v1.0.0 Cauris_CladeTyper_PHB Find_Shared_Variants Combines and reshapes variant data from Snippy_Variants to illustrate variants shared across multiple samples Bacteria, Mycotics Set-level Yes v2.0.0 Find_Shared_Variants_PHB GAMBIT_Query Taxon identification of genome assembly using GAMBIT Bacteria, Mycotics Sample-level Yes v2.0.0 Gambit_Query_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB NCBI-AMRFinderPlus Runs NCBI's AMRFinderPlus on genome assemblies (bacterial and fungal) Bacteria, Mycotics Sample-level Yes v2.0.0 NCBI-AMRFinderPlus_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.2.0 Snippy_Variants_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.1.0 Terra_2_NCBI_PHB TheiaEuk Mycotic genome assembly, QC and characterization from WGS data Mycotics Sample-level Some optional features incompatible, Yes v2.0.1 TheiaEuk_Illumina_PE_PHB"},{"location":"workflows_overview/workflows_kingdom/#viral","title":"Viral","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Augur Phylogenetic analysis for viral pathogens Viral Sample-level, Set-level Yes v2.1.0 Augur_Prep_PHB, Augur_PHB CZGenEpi_Prep Prepare metadata and fasta files for easy upload to the CZ GEN EPI platform. Monkeypox virus, SARS-CoV-2, Viral Set-level No v1.3.0 CZGenEpi_Prep_PHB Freyja Workflow Series Recovers relative lineage abundances from mixed sample data and generates visualizations SARS-CoV-2, Viral Sample-level, Set-level Yes v2.2.0 Freyja_FASTQ_PHB, Freyja_Plot_PHB, Freyja_Dashboard_PHB, Freyja_Update_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB Mercury_Prep_N_Batch Prepare metadata and sequence data for submission to NCBI and GISAID Influenza, Monkeypox virus, SARS-CoV-2, Viral Set-level No v2.2.0 Mercury_Prep_N_Batch_PHB Pangolin_Update Update Pangolin assignments SARS-CoV-2, Viral Sample-level Yes v2.0.0 Pangolin_Update_PHB Samples_to_Ref_Tree Use Nextclade to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Samples_to_Ref_Tree_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.2.0 Snippy_Variants_PHB Terra_2_GISAID Upload of assembly data to GISAID SARS-CoV-2, Viral Set-level Yes v1.2.1 Terra_2_GISAID_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.1.0 Terra_2_NCBI_PHB TheiaCov Workflow Series Viral genome assembly, QC and characterization from amplicon sequencing HIV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level, Set-level Some optional features incompatible, Yes v2.2.0 TheiaCoV_Illumina_PE_PHB, TheiaCoV_Illumina_SE_PHB, TheiaCoV_ONT_PHB, TheiaCoV_ClearLabs_PHB, TheiaCoV_FASTA_PHB, TheiaCoV_FASTA_Batch_PHB Usher_PHB Use UShER to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Usher_PHB VADR_Update Update VADR assignments HAV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level Yes v1.2.1 VADR_Update_PHBCommand-line compatibility is determined if the workflow can be run on a local command-line environment, providing all dependencies are installed, with either miniwdl or cromwell.\u00a0\u21a9\u21a9\u21a9\u21a9
Sort by Kingdom | Sort Alphabetically
"},{"location":"workflows_overview/workflows_type/#data-import","title":"Data Import","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Assembly_Fetch Download assemblies from NCBI, after optionally identifying the closest RefSeq reference genome to your own draft assembly Any taxa Sample-level Yes v1.3.0 Assembly_Fetch_PHB BaseSpace_Fetch Import data from BaseSpace into Terra Any taxa Sample-level Yes v2.0.0 BaseSpace_Fetch_PHB Create_Terra_Table Upload data to Terra and then run this workflow to have the table automatically created Any taxa Yes v2.2.0 Create_Terra_Table_PHB SRA_Fetch Import publicly available reads from SRA using SRR#, ERR# or DRR# Any taxa Sample-level Yes v2.2.0 SRA_Fetch_PHB"},{"location":"workflows_overview/workflows_type/#genomic-characterization","title":"Genomic Characterization","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Freyja Workflow Series Recovers relative lineage abundances from mixed sample data and generates visualizations SARS-CoV-2, Viral Sample-level, Set-level Yes v2.2.0 Freyja_FASTQ_PHB, Freyja_Plot_PHB, Freyja_Dashboard_PHB, Freyja_Update_PHB Pangolin_Update Update Pangolin assignments SARS-CoV-2, Viral Sample-level Yes v2.0.0 Pangolin_Update_PHB TheiaCov Workflow Series Viral genome assembly, QC and characterization from amplicon sequencing HIV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level, Set-level Some optional features incompatible, Yes v2.2.0 TheiaCoV_Illumina_PE_PHB, TheiaCoV_Illumina_SE_PHB, TheiaCoV_ONT_PHB, TheiaCoV_ClearLabs_PHB, TheiaCoV_FASTA_PHB, TheiaCoV_FASTA_Batch_PHB TheiaEuk Mycotic genome assembly, QC and characterization from WGS data Mycotics Sample-level Some optional features incompatible, Yes v2.0.1 TheiaEuk_Illumina_PE_PHB TheiaMeta Genome assembly and QC from metagenomic sequencing Any taxa Sample-level Yes v2.0.0 TheiaMeta_Illumina_PE_PHB TheiaProk Workflow Series Bacterial genome assembly, QC and characterization from WGS data Bacteria Sample-level Some optional features incompatible, Yes v2.2.0 TheiaProk_Illumina_PE_PHB, TheiaProk_Illumina_SE_PHB, TheiaProk_ONT_PHB, TheiaProk_FASTA_PHB VADR_Update Update VADR assignments HAV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level Yes v1.2.1 VADR_Update_PHB"},{"location":"workflows_overview/workflows_type/#phylogenetic-construction","title":"Phylogenetic Construction","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Augur Phylogenetic analysis for viral pathogens Viral Sample-level, Set-level Yes v2.1.0 Augur_Prep_PHB, Augur_PHB Core_Gene_SNP Pangenome analysis Bacteria Set-level Some optional features incompatible, Yes v2.1.0 Core_Gene_SNP_PHB CZGenEpi_Prep Prepare metadata and fasta files for easy upload to the CZ GEN EPI platform. Monkeypox virus, SARS-CoV-2, Viral Set-level No v1.3.0 CZGenEpi_Prep_PHB Find_Shared_Variants Combines and reshapes variant data from Snippy_Variants to illustrate variants shared across multiple samples Bacteria, Mycotics Set-level Yes v2.0.0 Find_Shared_Variants_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB Lyve_SET Alignment of reads to a reference genome, SNP calling, curation of high quality SNPs, phylogenetic analysis Bacteria Set-level Yes v2.1.0 Lyve_SET_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB Snippy_Streamline Implementation of Snippy workflows for phylogenetic analysis from reads, with optional dynamic reference selection Bacteria Set-level Yes v2.2.0 Snippy_Streamline_PHB Snippy_Streamline_FASTA Implementation of Snippy workflows for phylogenetic analysis from assembled genomes (in FASTA format), with optional dynamic reference selection Bacteria Set-level Yes v2.2.0 Snippy_Streamline_FASTA_PHB Snippy_Tree SNP-based phylogenetic analysis from reads, with option to mask recombination Bacteria Set-level Some optional features incompatible, Yes v2.1.0 Snippy_Tree_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.2.0 Snippy_Variants_PHB"},{"location":"workflows_overview/workflows_type/#phylogenetic-placement","title":"Phylogenetic Placement","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Samples_to_Ref_Tree Use Nextclade to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Samples_to_Ref_Tree_PHB Usher_PHB Use UShER to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Usher_PHB"},{"location":"workflows_overview/workflows_type/#public-data-sharing","title":"Public Data Sharing","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Mercury_Prep_N_Batch Prepare metadata and sequence data for submission to NCBI and GISAID Influenza, Monkeypox virus, SARS-CoV-2, Viral Set-level No v2.2.0 Mercury_Prep_N_Batch_PHB Terra_2_GISAID Upload of assembly data to GISAID SARS-CoV-2, Viral Set-level Yes v1.2.1 Terra_2_GISAID_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.1.0 Terra_2_NCBI_PHB Fetch_SRR_Accession Update SRR metadata in a Terra data table at the sample level Any taxa Yes v2.3.0 Fetch_SRR_Accession_PHB"},{"location":"workflows_overview/workflows_type/#exporting-data-from-terra","title":"Exporting Data from Terra","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Concatenate_Column_Content Concatenate contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Concatenate_Column_Content_PHB Transfer_Column_Content Transfer contents of a specified Terra data table column for many samples (\"entities\") to a GCP storage bucket location Any taxa Set-level Yes v1.3.0 Transfer_Column_Content_PHB Zip_Column_Content Zip contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Zip_Column_Content_PHB"},{"location":"workflows_overview/workflows_type/#standalone","title":"Standalone","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Cauris_CladeTyper C. auris clade assignment Mycotics Sample-level Yes v1.0.0 Cauris_CladeTyper_PHB GAMBIT_Query Taxon identification of genome assembly using GAMBIT Bacteria, Mycotics Sample-level Yes v2.0.0 Gambit_Query_PHB Kraken2 Taxa identification from reads Any taxa Sample-level Yes v2.0.0 Kraken2_PE_PHB, Kraken2_SE_PHB NCBI-AMRFinderPlus Runs NCBI's AMRFinderPlus on genome assemblies (bacterial and fungal) Bacteria, Mycotics Sample-level Yes v2.0.0 NCBI-AMRFinderPlus_PHB NCBI_Scrub Runs NCBI's HRRT on Illumina FASTQs Any taxa Sample-level Yes v2.2.1 NCBI_Scrub_PE_PHB, NCBI_Scrub_SE_PHB RASUSA Randomly subsample sequencing reads to a specified coverage Any taxa Sample-level Yes v2.0.0 RASUSA_PHB Rename_FASTQ Rename paired-end or single-end read files in a Terra data table in a non-destructive way Any taxa Sample-level Yes v2.1.0 Rename_FASTQ_PHB TBProfiler_tNGS Performs in silico antimicrobial susceptibility testing on Mycobacterium tuberculosis targeted-NGS samples with TBProfiler and tbp-parser Bacteria, TB Sample-level Yes v2.0.0 TBProfiler_tNGS_PHB TheiaValidate This workflow performs basic comparisons between user-designated columns in two separate tables. Any taxa No v2.0.0 TheiaValidate_PHBCommand-line compatibility is determined if the workflow can be run on a local command-line environment, providing all dependencies are installed, with either miniwdl or cromwell.\u00a0\u21a9\u21a9\u21a9\u21a9\u21a9\u21a9\u21a9
The PHB repository contains workflows for the characterization, genomic epidemiology, and sharing of pathogen genomes of public health concern. Workflows are available for viruses, bacteria, and fungi.
All workflows in the PHB repository end with _PHB in order to differentiate them from earlier versions and from the original tools they incorporate.
Explore our workflows
Command-line Users
Learn how to use our workflows on the command-line!
Terra Users
Learn how to use our workflows on Terra!
Our Open Source Philosophy
PHB source code is publicly available on GitHub and available under GNU Affero General Public License v3.0!
All workflows can be imported directly to Terra via the Dockstore PHB collection!
You can also use our workflows on the command-line. Please see our guide on how to get started here!
When undertaking genomic analysis using the command-line, via Terra, or other data visualization platforms, it is essential to consider the necessary and appropriate workflows and resources for your analysis. To help you make these choices, take a look at the relationship between the most commonly used Theiagen workflows.
Analysis Approaches for Genomic Data
This diagram shows the Theiagen workflows (green boxes) available for analysis of genomic data in public health and the workflows that may be used consecutively (arrows). The blue boxes describe the major functions that these workflows undertake. The yellow boxes show functions that may be undertaken independently of workflows on Terra.
"},{"location":"#phb-development-is-a-cycle","title":"PHB development is a cycle","text":"We continuously work to improve our codebase and usability of our workflows by the public health community, so changes from version to version are expected. This documentation page reflects the state of the workflow at the version stated in the title.
What's new?
You can see the changes since PHB v2.2.0 here!
"},{"location":"#contributing-to-the-phb-repository","title":"Contributing to the PHB Repository","text":"We warmly welcome contributions to this repository! Our style guide may be found here for convenience of formatting.
If you would like to submit suggested code changes to our workflows, you may add or modify the WDL files and submit pull requests to the PHB GitHub repository.
You can expect a careful review of every PR and feedback as needed before merging, just like we do for PRs submitted by the Theiagen team. Our PR template can help prepare you for the review process. As always, reach out with any questions! We love recieving feedback and contributions from the community. When your PR is merged, we'll add your name to the contributors list below!
"},{"location":"#authorship-responsibility","title":"Authorship & Responsibility","text":""},{"location":"#authorship","title":"Authorship","text":"(Ordered by contribution [# of lines changed] as of 2024-12-04)
We would like to gratefully acknowledge the following individuals from the public health community for their contributions to the PHB repository:
The PHB repository would not be possible without its predecessors. We would like to acknowledge the following repositories, individuals, and contributors for their influence on the development of these workflows:
The PHB repository originated from collaborative work with Andrew Lang, PhD & his Genomic Analysis WDL workflows. The workflows and task development were influenced by The Broad's Viral Pipes repository. The TheiaCoV workflows for viral genomic characterization were influenced by UPHL's Cecret & StaPH-B's Monroe (now deprecated) workflows. The TheiaProk workflows for bacterial genomic characterization were influenced by Robert Petit's bactopia. Most importantly, the PHB user community drove the development of these workflows and we are grateful for their feedback and contributions.
If you would like to provide feedback, please raise a GitHub issue or contact us at support@theiagen.com.
"},{"location":"#maintaining-phb-pipelines","title":"Maintaining PHB Pipelines","text":"Theiagen Genomics has committed to maintaining these workflows for the forseeable future. These workflows are written using a standard workflow language (WDL) and uses Docker images based on the StaPHB-B Docker Builds. New versions that include bug fixes and additional features are released on a quarterly bases, with urgent bug fixes released as needed. Each version is accompanied by detailed release notes to lower the barrier of pipeline upkeep from the public health community at large.
"},{"location":"#point-of-contact","title":"Point of Contact","text":"If you have any questions or concerns, please raise a GitHub issue or email Theiagen's general support at support@theiagen.com.
"},{"location":"#conflict-of-interest","title":"Conflict of Interest","text":"The authors declare no conflict of interest.
"},{"location":"#citation","title":"Citation","text":"Please cite this paper if publishing work using any workflows:
Libuit, Kevin G., Emma L. Doughty, James R. Otieno, Frank Ambrosio, Curtis J. Kapsak, Emily A. Smith, Sage M. Wright, et al. 2023. \"Accelerating Bioinformatics Implementation in Public Health.\" Microbial Genomics 9 (7). https://doi.org/10.1099/mgen.0.001051.
Alternatively, please cite this paper if using the TheiaEuk workflow:
Ambrosio, Frank, Michelle Scribner, Sage Wright, James Otieno, Emma Doughty, Andrew Gorzalski, Danielle Siao, et al. 2023. \"TheiaEuk: A Species-Agnostic Bioinformatics Workflow for Fungal Genomic Characterization.\" Frontiers in Public Health 11. https://doi.org/10.3389/fpubh.2023.1198213.
"},{"location":"#about-theiagen","title":"About Theiagen","text":"Theiagen develops bioinformatics solutions for public health labs, and then trains and supports scientists to use these. If you would like to work with Theiagen, please\u00a0get in contact.
"},{"location":"assets/new_workflow_template/","title":"Workflow Name","text":""},{"location":"assets/new_workflow_template/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Link to Workflow Type Link to Applicable Kingdom PHB <version with last changes> <command-line compatibility> <workflow level on terra (set or sample)>"},{"location":"assets/new_workflow_template/#workflow_name_on_terra","title":"Workflow_Name_On_Terra","text":"Description of the workflow.
"},{"location":"assets/new_workflow_template/#inputs","title":"Inputs","text":"Input should be ordered as they appear on Terra
Terra Task Name Variable Type Description Default Value Terra Status task_name variable_name Type Description Default Value Required/Optional"},{"location":"assets/new_workflow_template/#workflow-tasks","title":"Workflow Tasks","text":"Description of the workflow tasks
tool_name: Description of tool Description of the task
Tool Name Technical Details
Links Task [link to task on GitHub] Software Source Code [link to tool's source code] Software Documentation [link to tool's documentation] Original Publication(s) [link to tool's publication]"},{"location":"assets/new_workflow_template/#outputs","title":"Outputs","text":"Variable Type Description variable_name Type Description"},{"location":"assets/new_workflow_template/#references-if-applicable","title":"References (if applicable)","text":"reference1
reference2
"},{"location":"contributing/code_contribution/","title":"PHB Code Contributions","text":"Theiagen Genomics\u2019 Public Health Bioinformatics (PHB) workflows are written in\u00a0WDL, a language for specifying data processing workflows with a human-readable and writable syntax. Contributions to the workflows contained in the repository are warmly welcomed.
This document gives coding conventions for the WDL code comprising the workflow and task development for PHB. This style guide evolves over time as additional conventions are identified and past conventions are rendered obsolete by changes in the language itself.
Style guide inspired by\u00a0Scott Frazer\u2019s\u00a0WDL Best Practices Style Guide.
"},{"location":"contributing/code_contribution/#general-guidelines","title":"General Guidelines","text":"Modularity and Metadata
Add a meta block to every task and workflow to provide a brief description of its purpose.
meta {\n description: \"This tool does X\"\n}\nDocker Containers
Use a specific Docker container version instead of 'latest' to ensure reproducibility and prevent unexpected changes in container behavior.
String docker = \"us-docker.pkg.dev/docker_image:version\"\nPreferentially use containers Google's Artifact Registry rather than those from quay.io or dockerhub
Indentation and Whitespace
Use 2-space indentation for all blocks. Avoid using tabs to ensure uniform formatting across editors:
# perform action\nif [ condition ]; then\n perform_action(variable)\nfi\nUse a single space when defining variables (this = that not this= that (unless a bash variable where this=that is required))
Bracket and Spacing Conventions
input { instead of input\\n{ # Correct\ninput {\n String input_variable\n}\n\n# Incorrect\ninput\n{\n String input_variable\n}\noutput { instead of output{)Command Block Syntax
Enclose command blocks in triple angle brackets (<<< ... >>>) for consistency and easier handling of multi-line scripts. It also avoids issues with unescaped special characters in the command block:
command <<<\n tool --input ~{input} --output ~{output}\n>>>\nA WDL task block defines a discrete, reusable step in a workflow. To ensure readability and consistency, follow these conventions when writing task blocks. Include single spaces between the input, command, output, and runtime sections and their enclosing curly brackets.
task example_task {\n input {\n\n }\n command <<<\n\n >>>\n output {\n\n }\n runtime {\n\n }\n}\ninput block","text":"input {\n Int cpu = 4 # Number of CPUs\n Int disk_size = 100 # Disk space in GB\n String docker = \"us-docker.pkg.dev/example:1.0.0\" # Docker container for the task\n Int memory = 16 # Memory in GB\n}\nInclude optional tool parameters as inputs to the task
input {\n Int? optional_tool_parameter1\n String optional_tool_parameter2_with_default = \"default_value\"\n}\nInput and output lists should not be formatted to have the equal sign aligned, but instead use a single space before and after the =
correct_output = \"output_file\"\nlong_variable_name = \"long_file_name\"\nExpose Docker as an input, an output (if versioning information not available), and runtime variable:
input {\n String docker = \"us-docker.pkg.dev/example:1.0.0\"\n}\n...\noutput {\n String used_docker = docker\n}\nruntime {\n docker: docker\n}\ncommand block","text":"Ensure use of line breaks between different sections of code to improve readability
# Perform task step 1\nif [ condition ]; then\n action1(variable)\nfi\n\n# Perform task step 2\nif [ another_condition ]; then\n action2(variable)\nfi\nSplit command calls into multiple lines if they have user input variables and/or if the length of the command is very long to avoid text wrapping and/or side-scrolling, e.g.
tool \\\n --input ~{input_file} \\\n --output ~{output_file} \\\n --option1 ~{option1} \\\n ...\n --optionN ~{optionN}\nAdd comments that
Explain what non-intuitive bash/python text wrangling actions do, e.g.
## awk for gene column ($6) to grab subtype ($15)\ncat ~{file} | awk -F '\\t' '{if ($6==\"M1\") print $15}' > FLU_TYPE\noutput block","text":"output {\n File result_csv = \"output.csv\" # CSV file generated\n File result_log = \"log.txt\" # Log file\n}\nEnsure the docker container is exposed as an output string, e.g.
input {\n String docker = \"us-docker.pkg.dev/general-theiagen/tool:version\"\n}\n...\noutput {\n String XX_docker = docker\n}\nruntime {\n docker: docker\n}\nruntime block","text":"Always specify a Docker:
runtime {\n docker: docker\n cpu: cpu\n memory: memory\n disk: disk_size\n}\nA WDL workflow block orchestrates the execution of tasks and subworkflows. It defines the inputs, calls tasks or subworkflows, and specifies the final outputs. To ensure readability and consistency, follow these conventions when writing workflow blocks:
"},{"location":"contributing/code_contribution/#the-import-section","title":"Theimport section","text":"import statements (sorted in alphabetical order).When a workflow imports a task, ensure it is imported under a unique name to avoid conflicts.
import \"../tasks/task_task1.wdl\" as task1_task\nimport \"../tasks/task_task2.wdl\" as task2_task\nOrder import statements alphabetically by the path of the imported file.
input block","text":"input {\n String input\n String task1_docker = \"us-docker.pkg.dev/general-theiagen/tool:version\"\n String? task1_optional_argument\n}\ncall sections","text":"call task1_task.task1 {\n input:\n input = input,\n docker = task1_docker\n}\noutput block","text":"output {\n # Task 1 outputs\n File task1_out_csv = task1.output_csv\n String task1_version = task1.version\n\n # Subworkflow outputs\n File subworkflow_out_tsv = subworkflow.task3_out_tsv\n String subworkflow_version = subworkflow.task3_version\n}\nimport \"../tasks/task_task1.wdl\" as task1_task\nimport \"../tasks/task_task2.wdl\" as task2_task\n\nimport \"../workflows/wf_subworkflow.wdl\" as subworkflow\n\nworkflow example_wf {\n input {\n String input\n String task1_docker = \"us-docker.pkg.dev/general-theiagen/task_1:version\"\n String task2_docker = \"us-docker.pkg.dev/general-theiagen//task_2:version\"\n String? hidden_task3_argument \n String? hidden_task3_docker\n String? hidden_task4_docker\n }\n call task1_task.task1 {\n input:\n input = input,\n docker = task1_docker\n }\n call task2_task.task2 {\n input: \n input = input,\n docker = task2_docker\n }\n call subworkflow.subworkflow {\n input:\n input = input,\n task3_argument = hidden_task3_argument,\n task3_docker = hidden_task3_docker\n task4_docker = hidden_task4_docker\n }\n output {\n # Task 1 outputs\n File task1_out_csv = task1.output_csv\n String task1_version = task1.version\n String task1_docker = task1.docker\n # Task 2 outputs\n File task2_out_tsv = task2.output_tsv\n String task2_version = task2.version\n String task2_docker = task2.docker\n # Subworkflow outputs for task 3\n File task3_out_tsv = subworkflow.task3_out_tsv\n String task3_version = subworkflow.task3_version\n String task3_docker = subworkflow.task3_docker\n # Subworkflow outputs for task 4\n String task4_output = subworkflow.task4_output\n String task4_version = subworkflow.task4_version\n } \n}\nimport \"../tasks/task_task3.wdl\" as task3_task\nimport \"../tasks/task_task4.wdl\" as task4_task\n\nworkflow subworkflow {\n input {\n String input\n\n # optional inputs for tasks inside subworkflows cannot\n # be seen on Terra, so make them available at the subworkflow\n # level so they can be modified by a Terra user\n String? task3_argument \n String? task3_docker\n String? task4_docker\n }\n call task3_task.task3 {\n input:\n input = input,\n args = task3_argument,\n docker = task3_docker\n }\n call task4_task.task4 {\n input:\n input = task3.output_tsv,\n docker = task4_docker\n }\n output {\n File task3_out_tsv = task3.output_tsv\n String task3_version = task3.version\n String task3_docker = task3.docker\n String task4_output = task4.output\n String task4_version = task4.version\n }\n}\nThe documentation for PHB is hosted in the docs/ directory. This documentation is written in Markdown and is built using MkDocs and the Material for MkDocs theme.
This guide is intended to provide a brief overview of the documentation structure and how to contribute to the documentation, including standard language and formatting conventions.
"},{"location":"contributing/doc_contribution/#local-installation-live-previews","title":"Local Installation & Live Previews","text":"Since the documentation is built off of the main branch, it is highly recommended to preview your changes before making a PR. You can do this by installing the necessary packages and previewing (\"serving\") the documentation locally.
To test your documentation changes, you will need to have the following packages installed on your local VM:
pip install mkdocs-material mkdocs-material-extensions mkdocs-git-revision-date-localized-plugin mike mkdocs-glightbox\nOnce installed, navigate to the top directory in PHB. The live preview server can be activated by running the following command:
mkdocs serve\nThis will prompt you to open your browser to the appropriate local host address (by default, localhost:8000). Every time you save a change, the documentation will automatically update in the browser.
"},{"location":"contributing/doc_contribution/#vscode-extensions","title":"VSCode Extensions","text":"Here are some VSCode Extensions can help you write and edit your markdown files (and allow you preview changes without running the server, though formatting will suffer):
** or _ characters.In order to maintain cohesive documentation, the following language and formatting conventions should be followed:
"},{"location":"contributing/doc_contribution/#language-conventions","title":"Language Conventions","text":"The following language conventions should be followed when writing documentation:
cpu - Number of CPUs to allocate to the taskdisk_size - Amount of storage (in GB) to allocate to the taskdocker or docker_image - The Docker container to use for the taskmemory - Amount of memory/RAM (in GB) to allocate to the task**bold text** to indicate text that should be bolded._italicized text_ to indicate text that should be italicized.==highlighted text== to indicate text that should be highlighted.Code - Use `code` (backticks) to indicate text that should be formatted as code.^^underlined text^^ to indicate text that should be underlined (works with our theme; not all Markdown renderers support this).Citations
> to activate quote formatting for a citation. Make sure to separate multiple citations with a comment line (<!-- -->) to prevent the citations from running together.Callouts/Admonitions - These features are called \"call-outs\" in Notion, but are \"Admonitions\" in MkDocs. I highly recommend referring to the Material for MkDocs documentation page on Admonitions to learn how best to use this feature. Use the following syntax to create a callout:
!!! note\n This is a note. Observe I am indented with four spaces.\nPlease see the Admonition documentation for more information on how to change the title, enable toggles, and more.
The following custom callout types are supported in addition to the standard admonitions supported by our theme more information here:
Dna
This is a DNA admonition. Admire the cute green DNA emoji. You can create this with the !!! dna syntax.
Use this admonition when wanting to convey general information or highlight specific facts.
ToggleThis is a toggle-able section. The emoji is an arrow pointing to the right downward. You can create this with the ??? toggle syntax. I have added a + at the end of the question marks to make it open by default.
Use this admonition when wanting to provide additional optional information or details that are not strictly necessary, or take up a lot of space.
TaskThis is a toggle-able section for a workflow task. The emoji is a gear. Use the ??? task syntax to create this admonition. Use !!! task if you want to have it be permanently expanded. I have add a + at the end of the question marks to make this admonition open by default and still enable its collapse.
Use this admonition when providing details on a workflow, task, or tool.
Caption
This is a caption. The emoji is a painting. You can create this with the !!! caption syntax. A caption can be added beneath the picture and will also look nice.
Use this admonition when including images or diagrams in the documentation.
Techdetails
This is where you will put technical details for a workflow task. You can create this by !!! techdetails syntax.
Use this admonition when providing technical details for a workflow task or tool. These admonitions should include the following table:
Links Task [link to the task file in the PHB repository on GitHub] Software Source Code [link to tool's source code] Software Documentation [link to tool's documentation] Original Publication(s) [link to tool's publication]If any of these items are unfillable, delete the row.
Images - Use the following syntax to insert an image:
!!! caption \"Image Title\"\n \nIndentation - FOUR spaces are required instead of the typical two. This is a side effect of using this theme. If you use two spaces, the list and/or indentations will not render correctly. This will make your linter sad :(
- first item\n - second item\n - third item\nTables - Use the following syntax to create a table
| Header 1 | Header 2 | Header 3 |\n|---|---|---|\n| value 1 | value2 | value3 |\nNote that this is not a \"pretty\" markdown table. This is because the spacing would be crazy in the markdown file, especially for tables with a lot of text and/or columns. The table will render correctly in the documentation.
Links - Use the following syntax to create a link. This is works for both files and websites. If linking a file, use the relative path.
[Link Text](https://www.example.com)\nEnd all pages with an empty line
A brief description of the documentation structure is as follows:
docs/ - Contains the Markdown files for the documentation.assets/ - Contains images and other files used in the documentation.figures/ - Contains images, figures, and workflow diagrams used in the documentation. For workflows that contain many images (such as BaseSpace_Fetch), it is recommended to create a subdirectory for the workflow.files/ - Contains files that are used in the documentation. This may include example outputs or templates. For workflows that contain many files (such as TheiaValidate), it is recommended to create a subdirectory for the workflow.logos/ - Contains Theiagen logos and symbols used in the documentation.metadata_formatters/ - Contains the most up-to-date metadata formatters for our submission workflows.new_workflow_template.md - A template for adding a new workflow page to the documentation. You can see this template herecontributing/ - Contains the Markdown files for our contribution guides, such as this filejavascripts/ - Contains JavaScript files used in the documentation.tablesort.js - A JavaScript file used to enable table sorting in the documentation.overrides/ - Contains HTMLs used to override theme defaultsmain.html - Contains the HTML used to display a warning when the latest version is not selectedstylesheets/ - Contains CSS files used in the documentation.extra.css - A custom CSS file used to style the documentation; contains all custom theme elements (scrollable tables, resizable columns, Theiagen colors), and custom admonitions.workflows/ - Contains the Markdown files for each workflow, organized into subdirectories by workflow categoryworkflows_overview/ - Contains the Markdown files for the overview tables for each display type: alphabetically, by applicable kingdom, and by workflow type.index.md - The home/landing page for our documentation.If you are adding a new workflow, there are a number of things to do in order to include the page in the documentation:
docs/workflows/. Feel free to use the template found in the assets/ folder.docs/workflows/ subdirectorydocs/workflows_overview/:workflows_alphabetically.md - Add the workflow in the appropriate spot based on the workflow name.workflows_kingdom.md - Add the workflow in the appropriate spot(s) based on the kingdom(s) the workflow is applicable to. Make sure it is added alphabetically within the appropriate subsection(s).workflows_type.md - Add the workflow in the appropriate spot based on the workflow type. Make sure it is added alphabetically within the appropriate subsection.mkdocs.yml file (under the nav: section) in the main directory of this repository. These should be the exact same spots as in the overview tables but without additional information. This ensures the workflow can be accessed from the navigation sidebar.What is WDL?
Running workflows on the command-line requires the direct use of the WDL (Workflow Development Language). As the name suggests, this is the workflow management language that is used to write and execute workflows. Frank has put together a great video describing \ud83d\udcfa WDL Task and Workflow Files and you can find full instructions below on running these WDL workflows.
"},{"location":"getting_started/commandline/#step-1-obtain-the-workflow-and-data","title":"Step 1: Obtain the Workflow and Data","text":"You will need to have access to the WDL workflow file (.wdl) and any associated input files (such as reference genomes, input data files, etc.). To do this, complete the following steps:
"},{"location":"getting_started/commandline/#1-install-git-if-not-already-installed","title":"1. Install Git (if not already installed)","text":"If you don't already have Git installed on your system, you will need to install it. Here's how you can install Git on some common operating systems:
Linux (Ubuntu/Debian)sudo apt update\nsudo apt install git\nGit is usually pre-installed on macOS. However, you can install or update it using Homebrew:
brew install git\nDownload and install Git from the official website: https://git-scm.com/download/win
"},{"location":"getting_started/commandline/#2-clone-the-repository","title":"2. Clone the Repository","text":"Create a directory where you want to store the cloned repository and navigate to it.
mkdir /path/to/your/desired/new/directory\ncd /path/to/your/desired/new/directory\nClone the https://github.com/theiagen/public_health_bioinformatics repository from GitHub using the following command:
git clone https://github.com/theiagen/public_health_bioinformatics.git\nAfter running the command, Git will download all the repository files and set up a local copy in the directory you specified.
Change your working directory to the newly cloned repository:
cd public_health_bioinformatics\nYou're now inside the cloned repository's directory. Here, you should find all the files and directories from the GitHub repository.
You can verify that the repository has been cloned successfully by listing the contents of the current directory using the ls (on Linux/macOS) or dir (on Windows) command:
ls\nThis should display the files and directories within the https://github.com/theiagen/public_health_bioinformatics.git repository.
Congratulations! You've successfully cloned the https://github.com/theiagen/public_health_bioinformatics.git repository from GitHub to your local command-line environment. You're now ready to proceed with running the bioinformatics analysis workflows using WDL as described in subsequent steps.
"},{"location":"getting_started/commandline/#step-2-install-docker-and-miniwdl","title":"Step 2: Install docker and miniWDL","text":"Docker and miniwdl will be required for command-line execution. We will check if these are installed on your system and if not, install them now.
Navigate to the directory where your workflow and input files are located using the cd command:
cd /path/to/your/workflow/directory\nCheck if Docker is installed:
docker --version\nIf Docker is not installed, follow the official installation guide for your operating system: https://docs.docker.com/get-docker/
Check if miniwdl is installed:
miniwdl --version\nIf miniwdl is not installed, you can install it using pip:
pip install miniwdl\nIn a WDL (Workflow Description Language) workflow, an input JSON file is used to provide attributes (values/files etc) for input variables into the workflow. The names of the input variables must match the names of inputs specified in the workflow file. The workflow files can be found within the git repository that you cloned. Each input variable can have a specific type of attribute, such as String, File, Int, Boolean, Array, etc. Here's a detailed outline of how to specify different types of input variables in an input JSON file:
String InputTo specify a string input, use the name of the input variable as the key and provide the corresponding string value. Example:
{\n \"sampleName\": \"VirusSample1\",\n \"primerSequence\": \"ACGTGTCAG\"\n}\nTo specify a file input, provide the path to the input file relative to the directory where you run the miniwdl command. Example:
{\n \"inputFastq\": \"data/sample.fastq\",\n \"referenceGenome\": \"reference/genome.fasta\"\n}\nTo specify an integer input, provide the integer value. These do not require quotation marks. Example:
{\n \"minReadLength\": 50,\n \"maxThreads\": 8\n}\nTo specify a boolean input, use true or false (lowercase). Example:
{\n \"useQualityFiltering\": true,\n \"useDuplicateRemoval\": false\n}\nTo specify an array input, provide the values as an array. Example:
{\n \"sampleList\": [\"Sample1\", \"Sample2\", \"Sample3\"],\n \"thresholds\": [0.1, 0.05, 0.01]\n}\nRun the workflow using miniwdl with the following command, replacing your_workflow.wdl with the actual filename of your WDL workflow and input.json with the filename of your input JSON file.
miniwdl run your_workflow.wdl --input input.json\nYou can monitor the progress of the workflow by checking the console output for updates and log messages. This can help you identify any potential issues or errors during execution.
Tips for monitoring your workflow What to do if you need to cancel a run"},{"location":"getting_started/commandline/#tips-for-monitoring","title":"Tips for monitoring workflow progress","text":"After you've started the workflow using the miniwdl run command, you'll see various messages appearing in the terminal. These messages provide information about the various steps of the workflow as they are executed. Monitoring this output is crucial for ensuring that the workflow is progressing as expected.
The console output will typically show:
Example Console Output:
Here's an example of what the console output might look like while the workflow is running:
Running: task1\nRunning: task2\nCompleted: task1 (Duration: 5s)\nRunning: task3\nError: task2 (Exit Code: 1)\nRunning: task4\n...\nIn this example, you can see that task1 completed successfully in 5 seconds, but task2 encountered an error and exited with a non-zero exit code. This kind of output provides insight into the progress and status of the workflow.
What to Look For:
As you monitor the console output, pay attention to:
Early Troubleshooting:
If you encounter errors or unexpected behavior, the console output can provide valuable information for troubleshooting. You can search for the specific error messages to understand the problem and take appropriate action, such as correcting input values, adjusting parameters, or addressing software dependencies.
Monitoring the workflow progress through the console output is an essential practice for successful execution. It allows you to track the status of individual tasks, identify errors, and ensure that your analysis is proceeding as planned. Regularly reviewing the output will help you address any issues and improve the efficiency of your bioinformatics workflow.
"},{"location":"getting_started/commandline/#canceling-a-run","title":"Canceling a Running Workflow","text":"Canceling a running workflow is an important step in case you need to stop the execution due to errors, unexpected behavior, or any other reason. If you're using miniwdl to run your workflow, here's how you can cancel a workflow run while it's in progress:
Ctrl + C. This sends an interrupt signal to the running process, which should gracefully terminate it. However, keep in mind that this might not work for all scenarios, and some tasks might not be able to cleanly terminate.docker ps command to list running containers and docker stop <container_id> to stop a specific container.Kill the miniwdl Process: If the Ctrl + C approach doesn't work, you might need to explicitly kill the miniwdl process running in the terminal. To do this, you can use the kill command. First, find the process ID (PID) of the miniwdl process by running:
ps aux | grep miniwdl\nIdentify the PID in the output and then run:
kill -9 <PID>\nThis forcefully terminates the process.
Clean Up Intermediate Files: Depending on the workflow and how tasks are structured, there might be intermediate files or resources that were generated before the cancellation. You might need to manually clean up these files to free up disk space.
Remember that canceling a workflow might leave the system in an inconsistent state, especially if some tasks were partially executed. After canceling, it's a good idea to review the output and logs to identify any cleanup actions you might need to take.
It's important to approach workflow cancellation carefully, as abruptly terminating processes can potentially lead to data loss or other unintended consequences. Always make sure you understand the workflow's behavior and any potential side effects of cancellation before proceeding.
"},{"location":"getting_started/commandline/#step-6-review-output","title":"Step 6: Review Output","text":"Once the workflow completes successfully, you will find the output files and results in the designated output directory as defined in your WDL workflow.
Substep 1: Locate the Output DirectoryBefore you begin reviewing outputs, make sure you know where the output directory of your workflow is located. This is typically specified in the workflow configuration or input JSON file. Navigate to this directory using the cd command in your terminal.
cd /path/to/your/output/directory\nLogs are a valuable source of information about what happened during each step of the workflow. Each task in the workflow might generate its own log file. Here's how to review logs:
Use the ls command to list the files in the output directory:
ls\nLook for log files with names that correspond to the tasks in your workflow. These files often have a .log extension.
Open a log file using a text editor like less or cat:
less task_name.log\nUse the arrow keys to navigate through the log, and press q to exit.
Inspect the log for messages related to the task's execution, input values, software versions, and any errors or warnings that might have occurred.
stderr and stdout are streams where processes write error messages and standard output, respectively. These are often redirected to files during workflow execution. Here's how to review them:
ls command to list the files in the output directory.task_name.err (for stderr) and task_name.out (for stdout).Open the files using a text editor:
less task_name.err\nless task_name.out\nThese files might contain additional information about the task's execution, errors, and output generated during the analysis.
Workflow tasks might generate various types of output files, such as plots, reports, or data files. Here's how to review them:
ls command to list the files in the output directory.less or a text editor for text-based files, or an image viewer for image files.As you review the outputs, keep these points in mind:
As you review the outputs, make notes of any issues, errors, or unexpected behavior you encounter. Depending on the severity of the issues, you might need to:
Output Review Conclusion
Reviewing the outputs of your bioinformatics workflow is a critical step to ensure the quality of your analysis. Logs, stderr, stdout, and generated output files provide valuable insights into the execution process and results. By carefully reviewing these outputs and addressing any issues, you can enhance the reliability and accuracy of your bioinformatics analysis.
"},{"location":"getting_started/commandline/#step-7-troubleshooting-and-debugging","title":"Step 7: Troubleshooting and Debugging","text":"Congratulations! You have successfully executed a bioinformatics analysis workflow using WDL on the command-line. This tutorial covered the basic steps to run a WDL workflow using the miniwdl command-line tool.
Remember that the specific steps and commands might vary depending on the details of your workflow, software versions, and environment. Be sure to consult the documentation for miniwdl, WDL, and any other tools you're using for more advanced usage and troubleshooting.
Happy analyzing!
"},{"location":"getting_started/terra/","title":"Getting Started with Terra","text":"Our Approach
Theiagen\u2019s approach to genomic analysis in public health typically uses the Terra platform to run workflows that undertake bioinformatic analysis, then uses other platforms for visualization of the resulting data. This is described in more depth in our paper Accelerating bioinformatics implementation in public health, and the application of this approach for genomic surveillance of SARS-CoV-2 in California is described in the paper Pathogen genomics in public health laboratories: successes, challenges, and lessons learned from California\u2019s SARS-CoV-2 Whole-Genome Sequencing Initiative, California COVIDNet.
When undertaking genomic analysis using Terra and other data visualization platforms, it is essential to consider the necessary and appropriate workflows and resources for your analysis. To help you make these choices, take a look at the relationship between the most commonly used Theiagen workflows, and the descriptions of the major stages in genomic data analysis below.
Analysis Approaches for Genomic Data
This diagram shows the Theiagen workflows (green boxes) available for analysis of genomic data in public health and the workflows that may be used consecutively (arrows). The blue boxes describe the major functions that these workflows undertake. The yellow boxes show functions that may be undertaken independently of workflows on Terra.
"},{"location":"getting_started/terra/#data-import-to-terra","title":"Data Import to Terra","text":"To start using Terra for data analysis, you will first need to import your data into your workspace. There are multiple ways to do this:
SOPs for importing data into a Terra workspace
SOP SOP Version PHB Version Compatibility Uploading Data, Creating Metadata Tables and TSV files, and Importing Workflows v3 v1.3.0, v2+ Linking BaseSpace and Importing BaseSpace Reads to Terra v3 v1.3.0, v2+"},{"location":"getting_started/terra/#genome-assembly-qc-and-characterization","title":"Genome assembly, QC, and characterization","text":""},{"location":"getting_started/terra/#theiax-workflows","title":"TheiaX workflows","text":"The TheiaX workflows are used for genome assembly, quality control, and characterization. The TheiaCoV Workflow Series, TheiaProk Workflow Series, and TheiaEuk Workflow Series workflows are intended for viral, bacterial, and fungal pathogens, respectively. TheiaMeta Workflow Series is intended for the analysis of a single taxon from metagenomic data.
SOPs for the TheiaX workflows
For analyzing SARS-CoV-2 SOP SOP Version PHB Version Compatibility Analyze SARS-COV-2 using TheiaCoV_Illumina_PE_PHB v3 v2+ Analyze SARS-COV-2 using TheiaCoV_Illumina_SE_PHB v3 v2+ Analyze SARS-COV-2 using TheiaCoV_ClearLabs v3 v2+ Analyze SARS-COV-2 using TheiaCoV_ONT v2 v1.x+ Analyzing SARS-CoV-2 using TheiaCoV_FASTA v2 v1.x+ For analyzing influenza SOP SOP Version PHB Version Compatibility Analyzing Flu Data in Terra using TheiaCov_Illumina_PE and Augur Workflows v1 v1.x+"},{"location":"getting_started/terra/#quality-evaluation","title":"Quality evaluation","text":"The TheiaX workflows will generate various quality metrics. These should be evaluated relative to quality thresholds that have been agreed upon within your laboratory or sequencing program and define the sufficient quality characteristics for a genome and sequence data to be used. For the TheiaCoV Workflow Series, TheiaProk Workflow Series, and TheiaEuk Workflow Series workflows, this quality evaluation may be undertaken using the optional QC_check task. Full instructions for the use of this task may be found on the relevant workflow page. Some quality metrics are not evaluated by the QC_check task and should be evaluated manually.
Genomes that fail to meet agreed quality thresholds should not be used. Results for characterization of these genomes may be inaccurate or unreliable. The inclusion of poor-quality genomes in downstream comparative analyses will bias their results. Samples that fail to meet QC thresholds will need to be re-sequenced and sample processing may need to be repeated (e.g. culture-based isolation of clonal bacteria, DNA/RNA extraction, and processing for sequencing).
"},{"location":"getting_started/terra/#update-workflows-for-sars-cov-2-genomes","title":"Update workflows for SARS-CoV-2 genomes","text":"Workflows are available for updating the Pangolin and VADR assignments made to SARS-CoV-2 genomes. The Pangolin Update workflow accounts for the delay in assigning names to newly emerging lineages that you may have already sequenced. The VADR_Update workflow similarly accounts for features that have been newly identified in SARS-CoV-2 genomes when assessing genome quality with VADR.
"},{"location":"getting_started/terra/#phylogenetics","title":"Phylogenetics","text":""},{"location":"getting_started/terra/#phylogenetic-construction","title":"Phylogenetic construction","text":"Phylogenetic trees are constructed to assess the evolutionary relationships between sequences in the tree. These evolutionary relationships are often used as a proxy for epidemiological relationships, and sometimes for inferring transmission between isolation sources.
There are various methods for constructing phylogenetic trees, depending on the sequencing data being used, the organism being analyzed and how it evolved, what you would like to infer from the tree, and the computational resources available for the tree construction. Theiagen has a number of workflows for constructing phylogenetic trees. For full details of these workflows, please see Guide to Phylogenetics which includes advice on the appropriate tree-building workflows and phylogenetic visualization approaches.
SOPs for phylogenetic construction
SOP SOP Version PHB Version Compatibility Analyzing Flu Data in Terra using TheiaCov_Illumina_PE and Augur Workflows v1 v1.x+ Analyzing Phylogenetic Relationships in Terra using Theiagen\u2019s Augur Workflows v1 v1.x+"},{"location":"getting_started/terra/#phylogenetic-placement","title":"Phylogenetic placement","text":"Phylogenetic placement is used to place your own sequences onto an existing phylogenetic tree. This may be used to find the closest relatives to your sequence(s). More details, including phylogenetic visualization approaches can be found in Guide to Phylogenetics
"},{"location":"getting_started/terra/#public-data-sharing","title":"Public Data Sharing","text":"SOPs for data submissions
SOP SOP Version PHB Version Compatibility Submitting SC2 Sequence Data to GISAID using Theiagen\u2019s Terra 2 GISAID Workflow v2 v2+"},{"location":"getting_started/terra/#sars-cov-2-metagenomic-analysis","title":"SARS-CoV-2 Metagenomic Analysis","text":"SOPs for SARS-CoV-2 metagenomic data analysis
SOP SOP Version PHB Version Compatibility Analyzing SARS-CoV-2 Metagenomic Samples using Freyja FASTQ v2 v2+ Plotting SARS-CoV-2 Metagenomic Sample Data using Freyja Plot v3 v2+ Creating a Dashboard Visualization of SARS-CoV-2 Metagenomic Samples using Freyja Dashboard v2 v2+ Creating Static Reference Files for Freyja Analysis in Terra using Freyja Update v2 v2+"},{"location":"workflows/data_export/concatenate_column_content/","title":"Concatenate_Column_Content","text":""},{"location":"workflows/data_export/concatenate_column_content/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Exporting Data From Terra Any taxa PHB v2.1.0 Yes Set-level"},{"location":"workflows/data_export/concatenate_column_content/#concatenate_column_content_phb","title":"Concatenate_Column_Content_PHB","text":"This set-level workflow will create a file containing all of the items from a given column in a Terra Data Table. This is useful when you want to investigate many results files. There is a video available with more information about the Concatenate_Column_Content workflow: \ud83d\udcfa Workflow Focus: Concatenate_Column_Content
"},{"location":"workflows/data_export/concatenate_column_content/#inputs","title":"Inputs","text":"This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status concatenate_column_content concatenated_file_name String The name of the output file. Include the extension, such as \".fasta\" or \".txt\". Required concatenate_column_content files_to_cat Array[File] The column that has the files you want to concatenate. Required cat_files cpu Int Number of CPUs to allocate to the task 2 Optional cat_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional cat_files docker_image String The Docker container to use for the task s-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional cat_files memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional cat_files skip_extra_headers Boolean If the files you are concatenating have identical headers, you can include only the first instance of the header and skip all of the others so they do not appear duplicated in the concatenated file. To activate this, set to true. false Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_export/concatenate_column_content/#outputs","title":"Outputs","text":"Info
Please note that if you run this workflow on the same Terra set (the same group of samples can be included in multiple Terra sets), the results will overwrite each other. We recommend either (1) renaming the output variable, or (2) creating a new set every time you run the workflow.
Variable Type Description concatenated_files File The file containing all of the items from the column you selected. concatenate_column_content_version String The version of the repository the workflow is hosted in concatenate_column_content_analysis_date String The date the workflow was run"},{"location":"workflows/data_export/transfer_column_content/","title":"Transfer_Column_Content","text":""},{"location":"workflows/data_export/transfer_column_content/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Exporting Data From Terra Any taxa PHB v1.3.0 Yes Set-level"},{"location":"workflows/data_export/transfer_column_content/#transfer_column_content_phb","title":"Transfer_Column_Content_PHB","text":"This set-level workflow will transfer all of the items from a given column in a Terra Data Table to a single GCP storage bucket location. This is useful when you want to transfer many files to another GCP storage bucket (can be a Terra workspace storage bucket or a non-Terra storage bucket).
Note
This workflow requires that the user's Terra pet-service account has sufficient privileges to read and write to the target storage bucket.
Note
If using Transfer_column_content workflow version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is transferred fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/data_export/transfer_column_content/#inputs","title":"Inputs","text":"This workflow runs on the set level.
Terra Task name input_variable Type Description Default attribute Status transfer_column_content files_to_transfer Array[File] The column that has the files you want to concatenate. Required transfer_column_content target_bucket String The GS URI of the target storage bucket. Note: Do not include spaces, but do include thegs:// at the beginning of the bucket URI Required transfer_files cpu Int Number of CPUs to allocate to the task 4 Optional transfer_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional transfer_files docker_image String The docker image used to perform the file transfer. us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional transfer_files memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_export/transfer_column_content/#outputs","title":"Outputs","text":"Info
Please note that if you run this workflow on the same Terra set (the same group of samples can be included in multiple Terra sets), the results will overwrite each other. We recommend either (1) renaming the output variable, or (2) creating a new set every time you run the workflow.
Variable Type Description transferred_files File A list of all of the files now located at the target bucket location (GSURI) transfer_column_content_version String The version of the repository the workflow is hosted in transfer_column_content_analysis_date String The date the workflow was run"},{"location":"workflows/data_export/zip_column_content/","title":"Zip_Column_Content","text":""},{"location":"workflows/data_export/zip_column_content/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Exporting Data From Terra Any taxa PHB v2.1.0 Yes Set-level"},{"location":"workflows/data_export/zip_column_content/#zip_column_content_phb","title":"Zip_Column_Content_PHB","text":"This workflow will create a zip file that contains all of the items in a column in a Terra Table.
"},{"location":"workflows/data_export/zip_column_content/#inputs","title":"Inputs","text":"This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status zip_column_content files_to_zip Array[File] The column that has the files you want to zip. Required zip_column_content zipped_file_name String The name you want your zipped file to have. The .zip file extension will be added to this name. Required zip_files cpu Int Number of CPUs to allocate to the task 2 Optional zip_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional zip_files docker_image String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional zip_files memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_export/zip_column_content/#outputs","title":"Outputs","text":"Info
Please note that if you run this workflow on the same Terra set (the same group of samples can be included in multiple Terra sets), the results will overwrite each other. We recommend either (1) renaming the output variable, or (2) creating a new set every time you run the workflow.
Variable Type Description zipped_files File The zipped file containing all of the items from the column you selected. zip_column_content_version String The version of the repository the workflow is hosted in zip_column_content_analysis_date String The date the workflow was run"},{"location":"workflows/data_import/assembly_fetch/","title":"Assembly Fetch","text":""},{"location":"workflows/data_import/assembly_fetch/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Data Import Any taxa PHB v1.3.0 Yes Sample-level"},{"location":"workflows/data_import/assembly_fetch/#assembly_fetch_phb","title":"Assembly_Fetch_PHB","text":"The Assembly_Fetch workflow downloads assemblies from NCBI. This is particularly useful when you need to align reads against a reference genome, for example during a reference-based phylogenetics workflow. This workflow can be run in two ways:
Assembly_Fetch will run only the NCBI genome download task to download this assembly,Assembly_Fetch will first use the ReferenceSeeker task to first find the closest reference genome in RefSeq to your query assembly and then will run the NCBI genome download task to download that reference assembly.Tip
NOTE: If using Assembly_Fetch workflow version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is downloaded fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/data_import/assembly_fetch/#inputs","title":"Inputs","text":"Assembly_Fetch requires the input samplename, and either the accession for a reference genome to download (ncbi_accession) or an assembly that can be used to query RefSeq for the closest reference genome to download (assembly_fasta).
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status reference_fetch samplename String Your sample's name Required reference_fetch assembly_fasta File Assembly FASTA file of your sample Optional reference_fetch ncbi_accession String NCBI accession passed to the NCBI datasets task to be downloaded. Example: GCF_000006945.2 (Salmonella enterica subsp. enterica, serovar Typhimurium str. LT2 reference genome) Optional ncbi_datasets_download_genome_accession cpu Int Number of CPUs to allocate to the task 1 Optional ncbi_datasets_download_genome_accession disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ncbi_datasets_download_genome_accession docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/ncbi-datasets:14.13.2\" Optional ncbi_datasets_download_genome_accession include_gbff Boolean set to true if you would like the GenBank Flat File (GBFF) file included in the output. It contains nucleotide sequence, metadata, and annotations. FALSE Optional ncbi_datasets_download_genome_accession include_gff3 Boolean set to true if you would like the Genomic Feature File v3 (GFF3) file included in the output. It contains nucleotide sequence, metadata, and annotations FALSE Optional ncbi_datasets_download_genome_accession memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional referenceseeker cpu Int Number of CPUs to allocate to the task 4 Optional referenceseeker disk_size Int Amount of storage (in GB) to allocate to the task 200 Optional referenceseeker docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/biocontainers/referenceseeker:1.8.0--pyhdfd78af_0\" Optional referenceseeker memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional referenceseeker referenceseeker_ani_threshold Float ANI threshold used to exclude ref genomes when ANI value less than this value. 0.95 Optional referenceseeker referenceseeker_conserved_dna_threshold Float Conserved DNA threshold used to exclude ref genomes when conserved DNA value is less than this value. 0.69 Optional referenceseeker referenceseeker_db File Database used by the referenceseeker tool that contains bacterial genomes from RefSeq release 205. Downloaded from referenceseeker GitHub repo. \"gs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-bacteria-refseq-205.v20210406.tar.gz\" Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_import/assembly_fetch/#analysis-tasks","title":"Analysis Tasks","text":"ReferenceSeeker (optional) Details NCBI Datasets Details"},{"location":"workflows/data_import/assembly_fetch/#referenceseeker","title":"ReferenceSeeker","text":"ReferenceSeeker uses your draft assembly to identify closely related bacterial, viral, fungal, or plasmid genome assemblies in RefSeq.
Databases for use with ReferenceSeeker are as follows, and can be used by pasting the gs uri in double quotation marks \" \" into the referenceseeker_db optional input:
gs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-archaea-refseq-205.v20210406.tar.gzgs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-bacteria-refseq-205.v20210406.tar.gzgs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-fungi-refseq-205.v20210406.tar.gzgs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-plasmids-refseq-205.v20210406.tar.gzgs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-viral-refseq-205.v20210406.tar.gzFor ReferenceSeeker to identify a genome, it must meet user-specified thresholds for sequence coverage (referenceseeker_conserved_dna_threshold) and identity (referenceseeker_ani_threshold). The default values for these are set according to community standards (conserved DNA >= 69 % and ANI >= 95 %). A list of closely related genomes is provided in referenceseeker_tsv. The reference genome that ranks highest according to ANI and conserved DNA values is considered the closest match and will be downloaded, with information about this provided in the assembly_fetch_referenceseeker_top_hit_ncbi_accession output.
ReferenceSeeker Technical Details
Links Task task_referenceseeker.wdl Software version 1.8.0 (\"us-docker.pkg.dev/general-theiagen/biocontainers/referenceseeker:1.8.0--pyhdfd78af_0\") Software Source Code https://github.com/oschwengers/referenceseeker Software Documentation https://github.com/oschwengers/referenceseeker Original Publication(s) ReferenceSeeker: rapid determination of appropriate reference genomes"},{"location":"workflows/data_import/assembly_fetch/#ncbi-datasets","title":"NCBI Datasets","text":"The NCBI Datasets task downloads specified assemblies from NCBI using either the virus or genome (for all other genome types) package as appropriate.
NCBI Datasets Technical Details
Links Task task_ncbi_datasets.wdl Software version 14.13.2 (us-docker.pkg.dev/general-theiagen/staphb/ncbi-datasets:14.13.2) Software Source Code https://github.com/ncbi/datasets Software Documentation https://github.com/ncbi/datasets Original Publication(s) Not known to be published"},{"location":"workflows/data_import/assembly_fetch/#outputs","title":"Outputs","text":"Variable Type Description assembly_fetch_analysis_date String Date of assembly download assembly_fetch_ncbi_datasets_assembly_data_report_json File JSON file containing report about assembly downloaded by Asembly_Fetch assembly_fetch_ncbi_datasets_assembly_fasta File FASTA file downloaded by Assembly_Fetch assembly_fetch_ncbi_datasets_docker String Docker file used for NCBI datasets assembly_fetch_ncbi_datasets_gff File Assembly downloaded by Assembly_Fetch in GFF3 format assembly_fetch_ncbi_datasets_gff3 File Assembly downloaded by Assembly_Fetch in GFF format assembly_fetch_ncbi_datasets_version String NCBI datasets version used assembly_fetch_referenceseeker_database String ReferenceSeeker database used assembly_fetch_referenceseeker_docker String Docker file used for ReferenceSeeker assembly_fetch_referenceseeker_top_hit_ncbi_accession String NCBI Accession for the top hit identified by Assembly_Fetch assembly_fetch_referenceseeker_tsv File TSV file of the top hits between the query genome and the Reference Seeker database assembly_fetch_referenceseeker_version String ReferenceSeeker version used assembly_fetch_version String The version of the repository the Assembly Fetch workflow is in"},{"location":"workflows/data_import/assembly_fetch/#references","title":"References","text":"ReferenceSeeker: Schwengers O, Hain T, Chakraborty T, Goesmann A. ReferenceSeeker: rapid determination of appropriate reference genomes. J Open Source Softw. 2020 Feb 4;5(46):1994.
NCBI datasets: datasets: NCBI Datasets is an experimental resource for finding and building datasets [Internet]. Github; [cited 2023 Apr 19]. Available from: https://github.com/ncbi/datasets
"},{"location":"workflows/data_import/basespace_fetch/","title":"BaseSpace_Fetch","text":""},{"location":"workflows/data_import/basespace_fetch/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Data Import Any taxa PHB v1.3.0 Yes Sample-level"},{"location":"workflows/data_import/basespace_fetch/#setting-up-basespace_fetch","title":"Setting up BaseSpace_Fetch","text":"The BaseSpace_Fetch workflow facilitates the transfer of Illumina sequencing data from BaseSpace (a cloud location) to a workspace on the Terra.bio platform. Rather than downloading the files to a local drive and then re-uploading them to another location, we can perform a cloud-to-cloud transfer with the BaseSpace_Fetch workflow.
Some initial set-up is required to use the workflow. To access one's BaseSpace account from within a workflow on Terra.bio, it is necessary to retrieve an access token and the API server address using the BaseSpace command-line tool. The access token is unique to a BaseSpace account. If it is necessary to transfer data from multiple BaseSpace accounts, multiple access tokens will need to be retrieved. Please see the \"Retrieving BaseSpace Access Credentials\" section below.
In this document, we provide instructions for both the retrieval of the BaseSpace access token and running the BaseSpace_Fetch workflow.
"},{"location":"workflows/data_import/basespace_fetch/#retrieving-basespace-access-credentials","title":"Retrieving BaseSpace Access Credentials","text":"This process must be performed on a command-line (ideally on a Linux or MacOS computer) before using the BaseSpace_Fetch workflow for the first time. This can be set up in Terra, however it will work in any command-line environment that has access to the internet to install & run the BaseSpace command-line tool: bs.
If you already have a command-line environment available, you can skip ahead to Step 2.
"},{"location":"workflows/data_import/basespace_fetch/#step-1-setup-jupyter-cloud-environment","title":"Step 1: Setup Jupyter Cloud Environment","text":"Click for more informationSelect the \"Environment configuration\" cloud icon on the right side of the workspace dashboard tab
Select the \"Settings\" button under Jupyter
Click \"CREATE\" in the \"Use default environment section\". There is no need to alter the default environment configuration.
Undertaking steps 1 and 2 again, you will see options to configure the environment.
gsutilOpen the \"Terminal\" app in the right side-bar of the Terra dashboard
Download and setup BaseSpace (BS) CLI (as per Illumina documentation) by following the commands below. The lines beginning with # are comments, the following lines are the commands to be copy/pasted into the termina
# create bin dir\nmkdir ~/bin\n\n# download bs cli\nwget \"https://launch.basespace.illumina.com/CLI/latest/amd64-linux/bs\" -O $HOME/bin/bs\n\n# provide proper permissions to bs cli executable \nchmod u+x $HOME/bin/bs\n\n# add the 'bs' command-line tool to the $PATH variable so that you can call the command-line tool from any directory\nexport PATH=\"$PATH:$HOME/bin/\"\n\n# authenticate with BaseSpace credentials\nbs auth\n\n# navigate to the link provided in stdout and accept the authentication request through BaseSpace\n\n# Print the api server and access token to stdout (replace the path below with the appropriate path returned by the find command above)\ncat ~/.basespace/default.cfg\nCopy and paste the contents (access_token & API server) of the default.cfg file into Terra as workspace data elements.
Best Practices for Sample Identifiers
Download the sample sheet from BaseSpace.
In Excel, set up a metadata sheet for Terra, with a row for each sample. Please feel free to use our BaseSpace_Fetch Template to help ensure the file is formatted correctly.
Create a column called basespace_sample_name and populate this with the data found under the Sample_Name column in the BaseSpace sample sheet.
Watch out
If the contents of the Sample_Name and Sample_ID columns in the BaseSpace sample sheet are different, make a basespace_sample_id column in your spreadsheet and populate this with the data found under the Sample_ID column in the BaseSpace sample sheet.
Create a basespace_collection_id column, and populate it with the BaseSpace Project or Run identifier
In Terra, navigate to the \"DATA\" tab, click \"IMPORT DATA\" then \"Upload TSV\"
Copy and paste the contents of the whole spreadsheet into the \"TEXT IMPORT\" tab and click \"START IMPORT JOB\"
BaseSpace_Fetch workflow ORBaseSpace_Fetch workflow from Dockstore via this link.BaseSpace_Fetch workflow by selecting the:Set up the BaseSpace_Fetch \"INPUTS\" form as below. Don't forget to fill out this.basespace_sample_id if your basespace sample IDs are different from the basespace sample names in the SampleSheet.csv file.
In the \"OUTPUTS\" tab, select \"use defaults\", then click \"SAVE\".
Call Caching Disabled
If using BaseSpace_Fetch workflow version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is downloaded fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
Sample_Name and Sample_ID
If the Sample_Name and Sample_ID in the BaseSpace sample sheet are different, set the basespace_sample_id input attribute to \"this.basespace_sample_id\".
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status basespace_fetch access_token String The access token is used in place of a username and password to allow the workflow to access the user account in BaseSpace from which the data is to be transferred. It is an alphanumeric string that is 32 characters in length. Example: 9e08a96471df44579b72abf277e113b7 Required basespace_fetch api_server String The API server is the web address to which data transfer requests can be sent by the workflow. Use this API server if you are unsure:\"https://api.basespace.illumina.com\" (this is the default set by the command-line tool) Required basespace_fetch basespace_collection String The collection ID is the BaseSpace Run or Project where the data to be transferred is stored. Required basespace_fetch basespace_sample_name String The BaseSpace sample name is the sample identifier used in BaseSpace. This identifier is set on the sample sheet at the onset of an Illumina sequencing run. Required basespace_fetch sample_name String The sample name is the sample identifier used in the Terra.bio data table corresponding to the metadata associated with the sample to be transferred from BaseSpace Required basespace_fetch basespace_sample_id String The BaseSpace sample ID is an optional additional identifier used in BaseSpace. If a sample has a BaseSpace sample ID it should be available on the sample sheet and must be included in the metadata sheet upload prior to running BaseSpace_Fetch. Optional fetch_bs cpu Int This input is the number of CPU's used in the data transfer. To facilitate the transfer of many files this runtime parameter may be increased. 2 Optional fetch_bs disk_size Int The disk size is the amount of storage in GigaBytes (GB) requested for the VM to run the data transfer task. 100 Optional fetch_bs docker_image String The Docker image used to run BaseSpace_Fetch task. \"us-docker.pkg.dev/general-theiagen/theiagen/basespace_cli:1.2.1\" Optional fetch_bs memory Int The memory is the amount of RAM/memory requested for running the data transfer task. 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/data_import/basespace_fetch/#outputs","title":"Outputs","text":"The outputs of this workflow will be the fastq files imported from BaseSpace into the data table where the sample ID information had originally been uploaded.
Variable Type Description basespace_fetch_analysis_date String Date of download basespace_fetch_version String Version of the workflow read1 File File with forward-facing reads read2 File File with reverse-facing read"},{"location":"workflows/data_import/create_terra_table/","title":"Create_Terra_Table","text":""},{"location":"workflows/data_import/create_terra_table/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Data Import Any taxa PHB v2.2.0 Yes Sample-level"},{"location":"workflows/data_import/create_terra_table/#create_terra_table_phb","title":"Create_Terra_Table_PHB","text":"The manual creation of Terra tables can be tedious and error-prone. This workflow will automatically create your Terra data table when provided with the location of the files.
"},{"location":"workflows/data_import/create_terra_table/#inputs","title":"Inputs","text":"Default Behavior
Files with underscores and/or decimals in the sample name are not recognized; please use dashes instead.
For example, name.banana.hello_yes_please.fastq.gz will become \"name\". This means that se-test_21.fastq.gz and se-test_22.fastq.gz will not be recognized as separate samples.
This can be changed by providing information in the file_ending optional input parameter. See below for more information.
data_location_path","text":""},{"location":"workflows/data_import/create_terra_table/#using-the-terra-data-uploader","title":"Using the Terra data uploader","text":"Click for more information Once you have named your new collection, you will see the collection name directly above where you can drag-and-drop your data files, or on the same line as the Upload button. Right-click the collection name and select \"Copy link address.\" Paste the copied link into the data_location_path variable, remembering to enclose it in quotes.
Note
If you click \"Next\" after uploading your files, it will ask for a metadata TSV. You do not have to provide this, and can instead exit the window. Your data will still be uploaded.
"},{"location":"workflows/data_import/create_terra_table/#using-the-files-section-in-the-data-tab","title":"Using the \"Files\" section in the Data tab","text":"Click for more informationNavigate to the folder where your data is (\"example_upload\" in this example) and right-click on the folder name and select \"Copy link address.\"
If you uploaded data with the Terra data uploader, your collection will be nested in the \"uploads\" folder.
"},{"location":"workflows/data_import/create_terra_table/#how-to-determine-the-appropriate-file_ending-for-your-data","title":"How to determine the appropriatefile_ending for your data","text":"The file_ending should be a substring of your file names that is held in common. See the following examples:
One or more elements in common
If you have the following files:
The default behavior would result in a single entry in the table called \"sample\" which is incorrect. You can rectify this by providing an appropriate file_ending for your samples.
In this group, the desired sample names are \"sample_01\" and \"sample_02\". For all the files following the desired names, the text contains _R. If we provide \"_R\" as our file_ending, then \"sample_01\" and \"sample_02\" will appear in our table with the appropriate read files.
No elements in common
If you have the following files:
The default behavior would result in a single entry in the table called \"sample\" which is incorrect. You can rectify this by providing an appropriate file_ending for your samples.
In this group, the desired sample names are \"sample_01\" and \"sample_02\". However, in this example, there is no common text following the sample name. Providing \"_\" would result in the same behavior as default. We can provide two different patterns in the file_ending variable: \"_1,_2\" to capture all possible options. By doing this, \"sample_01\" and \"sample_02\" will appear in our table with the appropriate read files.
To include multiple file endings, please separate them with commas, as shown in the \"no elements in common\" section.
"},{"location":"workflows/data_import/create_terra_table/#outputs","title":"Outputs","text":"Your table will automatically appear in your workspace with the following fields:
new_table_name_id column), which will be the section of the file's name before any decimals or underscores (unless file_ending is provided)sample01.lane2_flowcell3.fastq.gz will be represented by sample01 in the tablesample02_negativecontrol.fastq.gz will be represented by sample02 in the tablefile_ending for your data\" above to learn how to change this default behaviorYour data in the appropriate columns, dependent on the values of assembly_data and paired_end
assembly_data is true paired_end is true assembly_data AND paired_end are false read1 \u274c \u2705 \u2705 read2 \u274c \u2705 \u274c assembly_fasta \u2705 \u274c \u274c The date of upload under the upload_date column
table_created_by, to indicate the table was made by the Create_Terra_Table_PHB workflow.The SRA_Fetch workflow downloads sequence data from NCBI's Sequence Read Archive (SRA). It requires an SRA run accession then populates the associated read files to a Terra data table.
Read files associated with the SRA run accession provided as input are copied to a Terra-accessible Google bucket. Hyperlinks to those files are shown in the \"read1\" and \"read2\" columns of the Terra data table.
"},{"location":"workflows/data_import/sra_fetch/#inputs","title":"Inputs","text":"This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status fetch_sra_to_fastq sra_accession String SRA, ENA, or DRA accession number Required fetch_sra_to_fastq cpu Int Number of CPUs to allocate to the task 2 Optional fetch_sra_to_fastq disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional fetch_sra_to_fastq docker_image String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/biocontainers/fastq-dl:2.0.4--pyhdfd78af_0\" Optional fetch_sra_to_fastq fastq_dl_options String Additional parameters to pass to fastq_dl from here \"--provider sra\" Optional fetch_sra_to_fastq memory Int Amount of memory/RAM (in GB) to allocate to the task 8 OptionalThe only required input for the SRA_Fetch workflow is an SRA run accession beginning \"SRR\", an ENA run accession beginning \"ERR\", or a DRA run accession which beginning \"DRR\".
Please see the NCBI Metadata and Submission Overview for assistance with identifying accessions. Briefly, NCBI-accessioned objects have the following naming scheme:
STUDY SRP# SAMPLE SRS# EXPERIMENT SRX# RUN SRR#"},{"location":"workflows/data_import/sra_fetch/#outputs","title":"Outputs","text":"Read data are available either with full base quality scores (SRA Normalized Format) or with simplified quality scores (SRA Lite). The\u00a0SRA Normalized Format\u00a0includes full, per-base quality scores, whereas base quality scores have been simplified in SRA Lite files. This means that all quality scores have been artificially set to Q-30 or Q3. More information about these files can be found here.
Given the lack of usefulness of SRA Lite formatted FASTQ files, we try to avoid these by selecting as provided SRA directly (SRA-Lite is more probably to be the file synced to other repositories), but some times downloading these files is unavoidable. To make the user aware of this, a warning column is present that is populated when an SRA-Lite file is detected.
Variable Type Description Production Status read1 File File containing the forward reads Always produced read2 File File containing the reverse reads (not availablae for single-end or ONT data) Produced only for paired-end data fastq_dl_date String The date of download Always produced fastq_dl_docker String The docker used Always produced fastq_dl_metadata File File containing metadata of the provided accession such as submission_accession, library_selection, instrument_platform, among others Always produced fastq_dl_version String Fastq_dl version used Always produced fastq_dl_warning String This warning field is populated if SRA-Lite files are detected. These files contain all quality encoding as Phred-30 or Phred-3. Depends on internal workflow logic"},{"location":"workflows/data_import/sra_fetch/#references","title":"References","text":"This workflow relies on fastq-dl, a very handy bioinformatics tool by Robert A. Petit III
"},{"location":"workflows/genomic_characterization/freyja/","title":"Freyja Workflow Series","text":""},{"location":"workflows/genomic_characterization/freyja/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Viral PHB v2.3.0 Yes Sample-level, Set-level"},{"location":"workflows/genomic_characterization/freyja/#freyja-overview","title":"Freyja Overview","text":"Freyja is a tool for analysing viral mixed sample genomic sequencing data. Developed by Joshua Levy from the Andersen Lab, it performs two main steps:
Additional post-processing steps can produce visualizations of aggregated samples.
Wastewater and more
The typical use case of Freyja is to analyze mixed SARS-CoV-2 samples from a sequencing dataset, most often wastewater.
Default Values
The defaults included in the Freyja workflows reflect this use case but can be adjusted for other pathogens. See the Running Freyja on other pathogens section for more information.
Figure 1: Workflow Diagram for Freyja_FASTQ_PHB workflow
Four workflows have been created that perform different parts of Freyja:
The main workflow is Freyja_FASTQ_PHB (Figure 1). Depending on the type of input data (Illumina paired-end, Illumina single-end or ONT), it runs various QC modules before aligning the sample with either BWA (Illumina) or minimap2 (ONT) to the provided reference file, followed by iVar for primer trimming. After the preprocessing is completed, Freyja is run to generate relative lineage abundances (demix) from the sample. Optional bootstrapping may be performed.
Data Compatability
The Freyja_FASTQ_PHB workflow is compatible with the following input data types:
- Ilumina Single-End\n- Illumina Paired-End\n- Oxford Nanopore\nFreyja_Update_PHB will copy the SARS-CoV-2 reference files (curated_lineages.json and usher_barcodes.feather) from the source repository to a user-specific Google Cloud Storage (GCP) location (often a Terra.bio workspace-associated bucket). These files can then be used as input for the Freyja_FASTQ_PHB workflow.
Two options are available to visualize the Freyja results: Freyja_Plot_PHB and Freyja_Dashboard_PHB. Freyja_Plot_PHB aggregates multiple samples using output from Freyja_FASTQ_PHB to generate a plot that shows fractional abundance estimates for all samples. including the option to plot sample collection date information. Alternatively, Freyja_Dashboard_PHB aggregates multiple samples using output from Freyja_FASTQ to generate an interactive visualization. This workflow requires an additional input field called viral load, which is the number of viral copies per liter.
"},{"location":"workflows/genomic_characterization/freyja/#figure1","title":"Figure 1","text":"Depending on the type of data (Illumina or Oxford Nanopore), the Read QC and Filtering steps, as well as the Read Alignment steps use different software. The user can specify if the barcodes and lineages file should be updated with freyja update before running Freyja or if bootstrapping is to be performed with freyja boot.
This workflow will copy the Freyja reference files (usher_barcodes.feather and curated_lineages.json) to a GCP URI of your choice for usage in Freyja_FASTQ_PHB.
We recommend running this workflow with \"Run inputs defined by file paths\" selected since no information from a Terra data table is actually being used. We also recommend turning off call caching so new information is retrieved every time.
Terra Task Name Variable Type Description Default Value Terra Status freyja_update gcp_uri String The path where you want the Freyja reference files to be stored. Include gs:// at the beginning of the string. Full example with a Terra workspace bucket: \"gs://fc-87ddd67a-c674-45a8-9651-f91e3d2f6bb7\" Required freyja_update_refs cpu Int Number of CPUs to allocate to the task 4 Optional freyja_update_refs disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional freyja_update_refs docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.2-11_30_2024-02-00-2024-12-02\" Optional freyja_update_refs memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional transfer_files cpu Int Number of CPUs to allocate to the task 2 Optional transfer_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional transfer_files docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional transfer_files memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional"},{"location":"workflows/genomic_characterization/freyja/#outputs","title":"Outputs","text":"This workflow does not produce any outputs that appear in a Terra data table. The reference files will appear at the location specified with the gcp_uri input variable.
Freyja measures SNV frequency and sequencing depth at each position in the genome to return an estimate of the true lineage abundances in the sample. The method uses lineage-defining \"barcodes\" that, for SARS-CoV-2, are derived from the UShER global phylogenetic tree as a base set for demixing. Freyja_FASTQ_PHB returns as output a TSV file that includes the lineages present and their corresponding abundances, along with other values.
The Freyja_FASTQ_PHB workflow is compatible with the multiple input data types: Ilumina Single-End, Illumina Paired-End and Oxford Nanopore. Depending on the type of input data, different input values are used.
Table 1: Freyja_FASTQ_PHB input configuration for different types of input data.
Table Columns Illumina Paired-End Illumina Single-End Oxford Nanopore read1 \u2705 \u2705 \u2705 read2 \u2705 \u274c \u274c ontfalse false true"},{"location":"workflows/genomic_characterization/freyja/#freyja_fastq-inputs","title":"Freyja_FASTQ Inputs","text":"This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status freyja_fastq primer_bed File The bed file containing the primers used when sequencing was performed Required freyja_fastq read1 File The raw forward-facing FASTQ file (Illumina or ONT) Required freyja_fastq reference_genome File The reference genome to use; should match the reference used for alignment (Wuhan-Hu-1) Required freyja_fastq samplename String The name of the sample Required bwa cpu Int Number of CPUs to allocate to the task 6 Optional bwa disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional bwa docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan Optional bwa memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional freyja bootstrap Boolean Perform bootstrapping FALSE Optional freyja confirmed_only Boolean Include only confirmed SARS-CoV-2 lineages FALSE Optional freyja cpu Int Number of CPUs to allocate to the task 2 Optional freyja disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional freyja docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.2-11_30_2024-02-00-2024-12-02\" Optional freyja eps Float The minimum lineage abundance cut-off value 0.001 Optional freyja freyja_barcodes String Custom barcode file. Does not need to be provided if update_db is true if the freyja_pathogen is provided. None Optional freyja freyja_lineage_metadata File File containing the lineage metadata; the \"curated_lineages.json\" file found https://github.com/andersen-lab/Freyja/tree/main/freyja/data can be used for this variable. Does not need to be provided if update_db is true or if the freyja_pathogen is provided. None Optional, Required freyja freyja_pathogen String Pathogen of interest, used if not providing the barcodes and lineage metadata files. Options: SARS-CoV-2, MPXV, H5NX, H1N1pdm, FLU-B-VIC, MEASLESN450, MEASLES, RSVa, RSVb None Optional freyja memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional freyja number_bootstraps Int The number of bootstraps to perform (only used if bootstrap = true) 100 Optional freyja update_db Boolean Updates the Freyja reference files (the usher barcodes and lineage metadata files) but will not save them as output (use Freyja_Update for that purpose). If set to true, thefreyja_lineage_metadata and freyja_barcodes files are not required. FALSE Optional freyja_fastq depth_cutoff Int The minimum coverage depth with which to exclude sites below this value and group identical barcodes 10 Optional freyja_fastq kraken2_target_organism String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. \"Severe acute respiratory syndrome coronavirus 2\" Optional freyja_fastq ont Boolean Indicates if the input data is derived from an ONT instrument. FALSE Optional freyja_fastq read2 File The raw reverse-facing FASTQ file (Illumina only) Optional freyja_fastq trimmomatic_minlen Int The minimum length cut-off when performing read cleaning 25 Optional get_fasta_genome_size cpu Int Number of CPUs to allocate to the task 1 Optional get_fasta_genome_size disk_size Int Amount of storage (in GB) to allocate to the task 10 Optional get_fasta_genome_size docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/biocontainers/seqkit:2.4.0--h9ee0642_0\" Optional get_fasta_genome_size memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional minimap2 cpu Int Number of CPUs to allocate to the task 2 Optional minimap2 disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional minimap2 docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/staphb/minimap2:2.22\" Optional minimap2 memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional minimap2 query2 File Internal component. Do not modify None Do not modify, Optional nanoplot_clean cpu Int Number of CPUs to allocate to the task 4 Optional nanoplot_clean disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional nanoplot_clean docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0\" Optional nanoplot_clean max_length Int Maximum read length for nanoplot 100000 Optional nanoplot_clean memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional nanoplot_raw cpu Int Number of CPUs to allocate to the task 4 Optional nanoplot_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional nanoplot_raw docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0\" Optional nanoplot_raw max_length Int Maximum read length for nanoplot 100000 Optional nanoplot_raw memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional primer_trim cpu Int Number of CPUs to allocate to the task 2 Optional primer_trim disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional primer_trim docker String Docker image used for this task. \"us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan\" Optional primer_trim keep_noprimer_reads Boolean Include reads with no primers TRUE Optional primer_trim memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_pe adapters File A FASTA file containing adapter sequence None Optional read_QC_trim_pe bbduk_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_pe call_kraken Boolean By default this is set to false to skip kraken2; set to true to run kraken2 but a database must be also provided via the kraken_db input parameter for this to run successfully FALSE Optional read_QC_trim_pe call_midas Boolean By default this is set to true to run MIDAS; set to false to skip MIDAS FALSE Optional read_QC_trim_pe fastp_args String Additional arguments to use with fastp \"--detect_adapter_for_pe -g -5 20 -3 20\" Optional read_QC_trim_pe kraken_cpu Int Number of CPUs to allocate to the task 4 Optional read_QC_trim_pe kraken_db File A kraken2 database to use with the kraken2 optional task. The file must be a .tar.gz kraken2 database. None Optional, Sometimes required read_QC_trim_pe kraken_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional read_QC_trim_pe kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_pe midas_db File Database to use with MIDAS. Not required as one will be auto-selected when running the MIDAS task. None Optional, Sometimes required read_QC_trim_pe phix File The file containing the phix sequence to be used during bbduk task None Optional read_QC_trim_pe read_processing String Options: \"trimmomatic\" or \"fastp\" to indicate which read trimming module to use \"trimmomatic\" Optional read_QC_trim_pe read_qc String Allows the user to decide between fastq_scan (default) and fastqc for the evaluation of read quality. fastq_scan Optional read_QC_trim_pe target_organism String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. None Optional read_QC_trim_pe trim_quality_trim_score Int The minimum quality score to keep during trimming 30 Optional read_QC_trim_pe trim_window_size Int The window size to use during trimming 4 Optional read_QC_trim_pe trimmomatic_args String Additional command-line arguments to use with trimmomatic None Optional read_QC_trim_ont call_kraken Boolean By default this is set to false to skip kraken2; set to true to run kraken2 but a database must be also provided via the kraken_db input parameter for this to run successfully FALSE Optional read_QC_trim_ont downsampling_coverage Float The depth to downsample to with Rasusa. Internal component. Do not modify. 150 Do not modify, Optional read_QC_trim_ont genome_length Int Internal component. Do not modify None Do not modify, Optional read_QC_trim_ont kraken_cpu Int Number of CPUs to allocate to the task 4 Optional read_QC_trim_ont kraken_db File A kraken2 database to use with the kraken2 optional task. The file must be a .tar.gz kraken2 database. None Optional read_QC_trim_ont kraken_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional read_QC_trim_ont kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_ont max_length Int Internal component, do not modify Do not modify, Optional read_QC_trim_ont min_length Int Internal component, do not modify Do not modify, Optional read_QC_trim_ont run_prefix String Internal component, do not modify Do not modify, Optional read_QC_trim_ont target_organism String This string is searched for in the kraken2 outputs to extract the read percentage Optional read_QC_trim_se adapters File A FASTA file containing adapter sequence None Optional read_QC_trim_se bbduk_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_se call_kraken Boolean By default this is set to false to skip kraken2; set to true to run kraken2 but a database must be also provided via the kraken_db input parameter for this to run successfully FALSE Optional read_QC_trim_se call_midas Boolean By default this is set to true to run MIDAS; set to false to skip MIDAS FALSE Optional read_QC_trim_se fastp_args String Additional arguments to use with fastp \"--detect_adapter_for_pe -g -5 20 -3 20\" Optional read_QC_trim_se kraken_cpu Int Number of CPUs to allocate to the task 4 Optional read_QC_trim_se kraken_db File A kraken2 database to use with the kraken2 optional task. The file must be a .tar.gz kraken2 database. None Optional read_QC_trim_se kraken_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional read_QC_trim_se kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional read_QC_trim_se midas_db File Database to use with MIDAS. Not required as one will be auto-selected when running the MIDAS task. None Optional, Sometimes required read_QC_trim_se phix File The file containing the phix sequence to be used during bbduk task None Optional read_QC_trim_se read_processing String Options: \"trimmomatic\" or \"fastp\" to indicate which read trimming module to use \"trimmomatic\" Optional read_QC_trim_se read_qc String Allows the user to decide between fastq_scan (default) and fastqc for the evaluation of read quality. fastq_scan Optional read_QC_trim_se target_organism String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. None Optional read_QC_trim_se trim_quality_min_score Int The minimum quality score to keep during trimming 30 Optional read_QC_trim_se trim_window_size Int The window size to use during trimming 4 Optional read_QC_trim_se trimmomatic_args String Additional command-line arguments to use with trimmomatic None Optional sam_to_sorted_bam cpu Int Number of CPUs to allocate to the task 2 Optional sam_to_sorted_bam disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional sam_to_sorted_bam docker String Docker image used for this task. us-docker.pkg.dev/general-theiagen/staphb/samtools:1.17 Optional sam_to_sorted_bam memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/freyja/#freyja_fastq-analysis-tasks","title":"Freyja_FASTQ Analysis Tasks","text":"read_QC_trim_pe Details read_QC_trim_se Details read_QC_trim_ont Details bwa Details minimap2 Details primer_trim Details freyja Details"},{"location":"workflows/genomic_characterization/freyja/#read_QC_trim_pe","title":"read_QC_trim_pe","text":"This task runs a sub-workflow that gathers basic QC information, trimming (either with trimmomatic or fastp), human read scrubbing, and taxonomic identification (Kraken2). Optional parameters do not need to be modified. For information regarding the individual tasks performed during this, please visit the TheiaCoV documentation.
Read_QC_Trim_PE Technical Details
Links Task wf_read_QC_trim_pe.wdl"},{"location":"workflows/genomic_characterization/freyja/#read_QC_trim_se","title":"read_QC_trim_se","text":"This task runs a sub-workflow that gathers basic QC information, trimming (either with trimmomatic or fastp), human read scrubbing, and taxonomic identification (Kraken2). Optional parameters do not need to be modified. For information regarding the individual tasks performed during this, please visit the TheiaCoV documentation.
Read_QC_Trim_SE Technical Details
Links Task wf_read_QC_trim_se.wdl"},{"location":"workflows/genomic_characterization/freyja/#read_QC_trim_ont","title":"read_QC_trim_ont","text":"This task runs a sub-workflow that gathers basic QC information, trimming (nanoplot), human read scrubbing, and taxonomic identification (Kraken2). Optional parameters do not need to be modified. For information regarding the individual tasks performed during this, please visit the TheiaCoV documentation.
Read_QC_Trim_ONT Technical Details
Links Task wf_read_QC_trim_ont.wdl"},{"location":"workflows/genomic_characterization/freyja/#bwa","title":"bwa","text":"This task aligns the cleaned short reads (Illumina) to the reference genome provided by the user.
BWA Technical Details
Links Task task_bwa.wdl Software Source Code https://github.com/lh3/bwa Software Documentation https://bio-bwa.sourceforge.net/ Original Publication(s) Fast and accurate short read alignment with Burrows-Wheeler transform"},{"location":"workflows/genomic_characterization/freyja/#minimap2","title":"minimap2","text":"This task aligns the cleaned long reads (Oxford Nanopore) to the reference genome provided by the user.
Minimap2 Technical Details
Links Task task_minimap2.wdl Software Source Code https://github.com/lh3/minimap2 Software Documentation https://lh3.github.io/minimap2/ Original Publication(s) Minimap2: pairwise alignment for nucleotide sequences"},{"location":"workflows/genomic_characterization/freyja/#primer_trim","title":"primer_trim","text":"This task trims the primer sequences from the aligned bam file with iVar. The optional input, keep_noprimer_reads, does not have to be modified.
Primer Trim Technical Details
Links Task task_ivar_primer_trim.wdl Software Source Code https://github.com/andersen-lab/ivar Software Documentation https://andersen-lab.github.io/ivar/html/manualpage.html Original Publication(s) An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar"},{"location":"workflows/genomic_characterization/freyja/#freyja","title":"freyja","text":"The Freyja task will call variants and capture sequencing depth information to identify the relative abundance of lineages present. Optionally, if bootstrap is set to true, bootstrapping will be performed. After the optional bootstrapping step, the variants are demixed.
Freyja Technical Details
Links Task task_freyja_one_sample.wdl Software Source Code https://github.com/andersen-lab/Freyja Software Documentation https://andersen-lab.github.io/Freyja/index.html#"},{"location":"workflows/genomic_characterization/freyja/#freyja_fastq-outputs","title":"Freyja_FASTQ Outputs","text":"The main output file used in subsequent Freyja workflows is found under the freyja_demixed column. This TSV file takes on the following format:
summarized\u00a0array denotes a sum of all lineage abundances in a particular WHO designation (i.e. B.1.617.2 and AY.6 abundances are summed in the above example), otherwise they are grouped into \"Other\".lineage\u00a0array lists the identified lineages in descending orderabundances\u00a0array contains the corresponding abundances estimates.resid\u00a0corresponds to the residual of the weighted least absolute deviation problem used to estimate lineage abundances.coverage\u00a0value provides the 10x coverage estimate (percent of sites with 10 or greater reads)Click \"Ignore empty outputs\"
When running the Freyja_FASTQ_PHB workflow, it is recommended to select the \"Ignore empty outputs\" option in the Terra UI. This will hide the output columns that will not be generated for your input data type.
Variable Type Description Input Data Type aligned_bai File Index companion file to the bam file generated during the consensus assembly process ONT, PE, SE aligned_bam File Primer-trimmed BAM file; generated during consensus assembly process ONT, PE, SE alignment_method String The method used to generate the alignment ONT, PE, SE bbduk_docker String Docker image used to run BBDuk PE, SE bwa_version String Version of BWA used to map read data to the reference genome PE, SE fastp_html_report File The HTML report made with fastp PE, SE fastp_version String Version of fastp software used PE, SE fastq_scan_clean1_json File JSON file output fromfastq-scan containing summary stats about clean forward read quality and length PE, SE fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length PE fastq_scan_num_reads_clean_pairs String Number of clean read pairs PE fastq_scan_num_reads_clean1 Int Number of clean forward reads PE, SE fastq_scan_num_reads_clean2 Int Number of clean reverse reads PE fastq_scan_num_reads_raw_pairs String Number of raw read pairs PE fastq_scan_num_reads_raw1 Int Number of raw forward reads PE, SE fastq_scan_num_reads_raw2 Int Number of raw reverse reads PE fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length PE, SE fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length PE fastq_scan_version String Version of fastq_scan used for read QC analysis PE, SE fastqc_clean1_html File Graphical visualization of clean forward read quality from fastqc to open in an internet browser PE, SE fastqc_clean2_html File Graphical visualization of clean reverse read quality from fastqc to open in an internet browser PE fastqc_docker String Docker container used for fastqc PE, SE fastqc_num_reads_clean_pairs String Number of read pairs after cleaning by fastqc PE fastqc_num_reads_clean1 Int Number of forward reads after cleaning by fastqc PE, SE fastqc_num_reads_clean2 Int Number of reverse reads after cleaning by fastqc PE fastqc_num_reads_raw_pairs String Number of input read pairs by fastqc PE fastqc_num_reads_raw1 Int Number of input forward reads by fastqc PE, SE fastqc_num_reads_raw2 Int Number of input reverse reads by fastqc PE fastqc_raw1_html File Graphical visualization of raw forward read quality from fastqc to open in an internet browser PE, SE fastqc_raw2_html File Graphical visualization of raw reverse read qualityfrom fastqc to open in an internet browser PE fastqc_version String Version of fastqc software used PE, SE freyja_barcode_file File Barcode file used ONT, PE, SE freyja_barcode_version String Name of barcode file used, or the date if update_db is true ONT, PE, SE freyja_bootstrap_lineages File A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each lineage ONT, PE, SE freyja_bootstrap_lineages_pdf File A boxplot of the bootstrap lineages CSV file ONT, PE, SE freyja_bootstrap_summary File A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each WHO designated VOI/VOC ONT, PE, SE freyja_bootstrap_summary_pdf File A boxplot of the bootstrap summary CSV file ONT, PE, SE freyja_coverage Float Coverage identified by Freyja and parsed from freyja_demixed file ONT, PE, SE freyja_demixed File The main output TSV; see the section directly above this table for an explanation ONT, PE, SE freyja_depths File A TSV listing the depth of every position ONT, PE, SE freyja_fastq_wf_analysis_date String Date of analysis ONT, PE, SE freyja_fastq_wf_version String The version of the Public Health Bioinformatics (PHB) repository used ONT, PE, SE freyja_lineage_metadata_file File Lineage metadata JSON file used. Can be the one provided as input or downloaded by Freyja if update_db is true ONT, PE, SE freyja_metadata_version String Name of lineage metadata file used, or the date if update_db is true ONT, PE, SE freyja_barcode_file File Barcode feather file used. Can be the one provided as input or downloaded by Freyja if update_db is true ONT, PE, SE freyja_variants File The TSV file containing the variants identified by Freyja ONT, PE, SE freyja_version String version of Freyja used ONT, PE, SE ivar_version_primtrim String Version of iVar for running the iVar trim command ONT, PE, SE kraken_human Float Percent of human read data detected using the Kraken2 software ONT, PE, SE kraken_human_dehosted Float Percent of human read data detected using the Kraken2 software after host removal ONT, PE, SE kraken_report File Full Kraken report ONT, PE, SE kraken_report_dehosted File Full Kraken report after host removal ONT, PE, SE kraken_sc2 String Percent of SARS-CoV-2 read data detected using the Kraken2 software ONT, PE, SE kraken_sc2_dehosted String Percent of SARS-CoV-2 read data detected using the Kraken2 software after host removal ONT, PE, SE kraken_version String Version of Kraken software used ONT, PE, SE minimap2_docker String Docker image used to run minimap2 ONT minimap2_version String Version of minimap2 used ONT nanoplot_html_clean File Clean read file ONT nanoplot_html_raw File Raw read file ONT nanoplot_num_reads_clean1 Int Number of clean reads for the forward-facing file ONT nanoplot_num_reads_raw1 Int Number of reads for the forward-facing file ONT nanoplot_r1_est_coverage_clean Float Estimated coverage on the clean reads by nanoplot ONT nanoplot_r1_est_coverage_raw Float Estimated coverage on the raw reads by nanoplot ONT nanoplot_r1_mean_q_clean Float Mean quality score of clean forward reads ONT nanoplot_r1_mean_q_raw Float Mean quality score of raw forward reads ONT nanoplot_r1_mean_readlength_clean Float Mean read length of clean forward reads ONT nanoplot_r1_mean_readlength_raw Float Mean read length of raw forward reads ONT nanoplot_r1_median_q_clean Float Median quality score of clean forward reads ONT nanoplot_r1_median_q_raw Float Median quality score of raw forward reads ONT nanoplot_r1_median_readlength_clean Float Median read length of clean forward reads ONT nanoplot_r1_median_readlength_raw Float Median read length of raw forward reads ONT nanoplot_r1_n50_clean Float N50 of clean forward reads ONT nanoplot_r1_n50_raw Float N50 of raw forward reads ONT nanoplot_r1_stdev_readlength_clean Float Standard deviation read length of clean forward reads ONT nanoplot_r1_stdev_readlength_raw Float Standard deviation read length of raw forward reads ONT nanoplot_tsv_clean File Output TSV file created by nanoplot ONT nanoplot_tsv_raw File Output TSV file created by nanoplot ONT nanoq_version String Version of nanoq used in analysis ONT primer_bed_name String Name of the primer bed file used for primer trimming ONT, PE, SE primer_trimmed_read_percent Float Percentage of read data with primers trimmed as determined by iVar trim ONT, PE, SE read1_clean File Forward read file after quality trimming and adapter removal ONT, PE, SE read1_dehosted File Dehosted forward reads ONT, PE, SE read2_clean File Reverse read file after quality trimming and adapter removal PE read2_dehosted File Dehosted reverse reads PE samtools_version String The version of SAMtools used to sort and index the alignment file ONT, PE, SE samtools_version_primtrim String The version of SAMtools used to create the pileup before running iVar trim ONT, PE, SE trimmomatic_docker String Docker container for Trimmomatic PE, SE trimmomatic_version String The version of Trimmomatic used PE, SE"},{"location":"workflows/genomic_characterization/freyja/#freyja_plot","title":"Freyja_Plot_PHB","text":"This workflow visualizes aggregated freyja_demixed output files produced by Freyja_FASTQ in a single plot (pdf format) which provides fractional abundance estimates for all aggregated samples.
Options exist to provide lineage-specific breakdowns and/or sample collection time information.
"},{"location":"workflows/genomic_characterization/freyja/#freyja_plot-inputs","title":"Freyja_Plot Inputs","text":"This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status freyja_plot freyja_demixed Array[File] An array containing the output files (freyja_demixed) made by Freyja_FASTQ Required freyja_plot freyja_plot_name String The name of the plot to be produced. Example: \"my-freyja-plot\" Required freyja_plot samplename Array[String] An array containing the names of the samples Required freyja_plot collection_date Array[String] An array containing the collection dates for the sample (YYYY-MM-DD format) Optional freyja_plot_task cpu Int Number of CPUs to allocate to the task 2 Optional freyja_plot_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional freyja_plot_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.2-11_30_2024-02-00-2024-12-02 Optional freyja_plot_task memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional freyja_plot_task mincov Int The minimum genome coverage used as a cut-off of data to include in the plot 60 Optional freyja_plot_task plot_day_window Int The width of the rolling average window; only used if plot_time_interval is \"D\" 14 Optional freyja_plot_task plot_lineages Boolean If true, will plot a lineage-specific breakdown FALSE Optional freyja_plot_task plot_time Boolean If true, will plot sample collection time information (requires the collection_date input variable) FALSE Optional freyja_plot_task plot_time_interval String Options: \"MS\" for month, \"D\" for day MS Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/freyja/#analysis-tasks","title":"Analysis Tasks","text":"freyja_plot_task Details"},{"location":"workflows/genomic_characterization/freyja/#freyja_plot_task","title":"freyja_plot_task","text":"This task will aggregate multiple samples together, and then creates a plot. Several optional inputs dictate the plot appearance (see each variable's description for more information).
Freyja Plot Technical Details
Links Task wf_freyja_plot.wdl Software Source Code https://github.com/andersen-lab/Freyja Software Documentation https://github.com/andersen-lab/Freyja"},{"location":"workflows/genomic_characterization/freyja/#freyja_plot-outputs","title":"Freyja_Plot Outputs","text":"Variable Type Description freyja_demixed_aggregate File A TSV file that summarizes thefreyja_demixed otuputs for all samples freyja_plot File A PDF of the plot produced by the workflow freyja_plot_metadata File The metadata used to create the plot freyja_plot_version String The version of Freyja used freyja_plot_wf_analysis_date String The date of analysis freyja_plot_wf_version String The version of the Public Health Bioinformatics (PHB) repository used"},{"location":"workflows/genomic_characterization/freyja/#freyja_dashboard","title":"Freyja_Dashboard_PHB","text":"This workflow creates a group of interactive visualizations based off of the aggregated freyja_demixed output files produced by Freyja_FASTQ called a \"dashboard.\" Creating this dashboard requires knowing the viral load of your samples (viral copies/L).
This dashboard is not \"live\" \u2014 that is, you must rerun the workflow every time you want new data to be included in the visualizations.
"},{"location":"workflows/genomic_characterization/freyja/#freyja_dashboard-inputs","title":"Freyja_Dashboard Inputs","text":"This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status freyja_dashboard collection_date Array[String] An array containing the collection dates for the sample (YYYY-MM-DD format) Required freyja_dashboard freyja_dashboard_title String The name of the dashboard to be produced. Example: \"my-freyja-dashboard\" Required freyja_dashboard freyja_demixed Array[File] An array containing the output files (freyja_demixed) made by Freyja_FASTQ workflow Required freyja_dashboard samplename Array[String] An array containing the names of the samples Required freyja_dashboard viral_load Array[String] An array containing the number of viral copies per liter Required freyja_dashboard dashboard_intro_text File A file containing the text to be contained at the top of the dashboard. SARS-CoV-2 lineage de-convolution performed by the Freyja workflow (https://github.com/andersen-lab/Freyja). Optional freyja_dashboard_task config File (found in the optional section, but is required) A yaml file that applies various configurations to the dashboard, such as grouping lineages together, applying colorings, etc. See also https://github.com/andersen-lab/Freyja/blob/main/freyja/data/plot_config.yml. None Optional, Required freyja_dashboard_task cpu Int Number of CPUs to allocate to the task 2 Optional freyja_dashboard_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.2-11_30_2024-02-00-2024-12-02 Optional freyja_dashboard_task headerColor String A hex color code to change the color of the header Optional freyja_dashboard_task memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional freyja_dashboard_task mincov Float The minimum genome coverage used as a cut-off of data to include in the dashboard. Default is set to 60 by the freyja command-line tool (not a WDL task default, per se) None Optional freyja_dashboard_task scale_by_viral_load Boolean If set to true, averages samples taken the same day while taking viral load into account FALSE Optional freyja_dashboard_task thresh Float The minimum lineage abundance cut-off value None Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/freyja/#freyja_dashboard-tasks","title":"Freyja_Dashboard Tasks","text":"freyja_dashboard_task Details This task will aggregate multiple samples together, and then create an interactive HTML visualization. Several optional inputs dictate the dashboard appearance (see each variable's description for more information).
Freyja Dashboard Technical Details
Links Task wf_freyja_dashboard.wdl Software Source Code https://github.com/andersen-lab/Freyja Software Documentation https://github.com/andersen-lab/Freyja"},{"location":"workflows/genomic_characterization/freyja/#freyja_dashboard-outputs","title":"Freyja_Dashboard Outputs","text":"Variable Type Description freyja_dashboard File The HTML file of the dashboard created freyja_dashboard_metadata File The metadata used to create the dashboard freyja_dashboard_version String The version of Freyja used freyja_dashboard_wf_analysis_date String The date of analysis freyja_dashboard_wf_version String The version of the Public Health Bioinformatics (PHB) repository used freyja_demixed_aggregate File A TSV file that summarizes thefreyja_demixed outputs for all samples"},{"location":"workflows/genomic_characterization/freyja/#running-freyja-on-other-pathogens","title":"Running Freyja on other pathogens","text":"The main requirement to run Freyja on other pathogens is the existence of a barcode file for your pathogen of interest. Currently, barcodes exist for the following organisms
Freyja barcodes for other pathogens
Data for various pathogens can be found in the following repository:\u00a0Freyja Barcodes
Folders are organized by pathogen, with each subfolder named after the date the barcode was generated, using the format YYYY-MM-DD. Barcode files are named barcode.csv, and reference genome files are named reference.fasta.
The appropriate barcode file and reference sequence need to be downloaded and uploaded to your Terra.bio workspace.
When running Freyja_FASTQ_PHB, the appropriate reference and barcodes file need to be passed as inputs. The first is a required input and will show up at the top of the workflows inputs page on Terra.bio (Figure 2).
Figure 2: Required input for Freyja_FASTQ_PHB to provide the reference genome to be used by Freyja
The barcodes file can be passed directly to Freyja by the freyja_barcodes optional input (Figure 3).
Figure 3: Optional input for Freyja_FASTQ_PHB to provide the barcodes file to be used by Freyja
"},{"location":"workflows/genomic_characterization/freyja/#figure2","title":"Figure 2","text":""},{"location":"workflows/genomic_characterization/freyja/#figure3","title":"Figure 3","text":""},{"location":"workflows/genomic_characterization/freyja/#references","title":"References","text":"If you use any of the Freyja workflows, please cite:
Karthikeyan, S., Levy, J.I., De Hoff, P.\u00a0et al.\u00a0Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission.\u00a0Nature 609, 101\u2013108 (2022). https://doi.org/10.1038/s41586-022-05049-6
Freyja source code can be found at https://github.com/andersen-lab/Freyja
Freyja barcodes (non-SARS-CoV-2): https://github.com/gp201/Freyja-barcodes
"},{"location":"workflows/genomic_characterization/pangolin_update/","title":"Pangolin_Update","text":""},{"location":"workflows/genomic_characterization/pangolin_update/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Viral, SARS-Cov-2 PHB v2.0.0 Yes Sample-level"},{"location":"workflows/genomic_characterization/pangolin_update/#pangolin_update_phb","title":"Pangolin_Update_PHB","text":"The Pangolin_Update workflow re-runs Pangolin updating prior lineage calls from one docker image to meet the lineage calls specified in an alternative docker image. The most common use case for this is updating lineage calls to be up-to-date with the latest Pangolin nomenclature by using the latest available Pangolin docker image (found\u00a0here).
"},{"location":"workflows/genomic_characterization/pangolin_update/#inputs","title":"Inputs","text":"This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status pangolin_update assembly_fasta File SARS-CoV-2 assembly file in FASTA format Required pangolin_update old_lineage String The Pangolin lineage previously assigned to the sample Required pangolin_update old_pangolin_assignment_version String Version of the Pangolin software previously used for lineage assignment. Required pangolin_update old_pangolin_docker String The Pangolin docker image previously used for lineage assignment. Required pangolin_update old_pangolin_versions String All pangolin software and database versions previously used for lineage assignment. Required pangolin_update samplename String The name of the sample being analyzed. Required pangolin_update lineage_log File TSV file detailing previous lineage assignments and software versions for this sample. Optional pangolin_update new_pangolin_docker String The Pangolin docker image used to update the Pangolin lineage assignments. Optional pangolin4 analysis_mode String Pangolin inference engine for lineage designations (usher or pangolearn) None Optional pangolin4 cpu Int Number of CPUs to allocate to the task 4 Optional pangolin4 disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pangolin4 expanded_lineage Boolean True/False that determines if a lineage should be expanded without aliases (e.g., BA.1 \u2192 B.1.1.529.1) TRUE Optional pangolin4 max_ambig Float Maximum proportion of Ns allowed for Pangolin to attempt assignment 0.5 Optional pangolin4 memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional pangolin4 min_length Int Minimum query length allowed for pangolin to attempt assignment 10000 Optional pangolin4 pangolin_arguments String Optional arguments for pangolin e.g. \"--skip-scorpio\" None Optional pangolin4 skip_designation_cache Boolean True/False that determines if the designation cache should be used FALSE Optional pangolin4 skip_scorpio Boolean True/False that determines if scorpio should be skipped. FALSE Optional pangolin_update_log cpu Int Number of CPUs to allocate to the task 4 Optional pangolin_update_log disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pangolin_update_log docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional pangolin_update_log memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional pangolin_update_log timezone String Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/pangolin_update/#outputs","title":"Outputs","text":"Variable Type Description pango_lineage String Pango lineage as determined by Pangolin pango_lineage_expanded String Pango lineage without use of aliases; e.g., BA.1 \u2192 B.1.1.529.1 pango_lineage_log File TSV file listing Pangolin lineage assignments and software versions for this sample pango_lineage_report File Full Pango lineage report generated by Pangolin pangolin_assignment_version String Version of the Pangolin software (e.g. PANGO or PUSHER) used for lineage assignment pangolin_conflict String Number of lineage conflicts as determined by Pangolin pangolin_docker String The Docker container to use for the task pangolin_notes String Lineage notes as determined by Pangolin pangolin_update_analysis_date String Date of analysis pangolin_update_version String Version of the Public Health Bioinformatics (PHB) repository used pangolin_updates String Result of Pangolin Update (lineage changed versus unchanged) with lineage assignment and date of analysis pangolin_versions String All Pangolin software and database versions"},{"location":"workflows/genomic_characterization/pangolin_update/#references","title":"References","text":"Pangolin: RRambaut A, Holmes EC, O'Toole \u00c1, Hill V, McCrone JT, Ruis C, du Plessis L, Pybus OG. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat Microbiol. 2020 Nov;5(11):1403-1407. doi: 10.1038/s41564-020-0770-5. Epub 2020 Jul 15. PMID: 32669681; PMCID: PMC7610519.
"},{"location":"workflows/genomic_characterization/theiacov/","title":"TheiaCoV Workflow Series","text":""},{"location":"workflows/genomic_characterization/theiacov/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Viral PHB v2.3.0 Yes, some optional features incompatible Sample-level"},{"location":"workflows/genomic_characterization/theiacov/#theiacov-workflows","title":"TheiaCoV Workflows","text":"The TheiaCoV workflows are for the assembly, quality assessment, and characterization of viral genomes. There are currently five TheiaCoV workflows designed to accommodate different kinds of input data:
Additionally, the TheiaCoV_FASTA_Batch workflow is available to process several hundred SARS-CoV-2 assemblies at the same time.
Key Resources
Reference Materials for SARS-CoV-2
Reference Materials for Mpox
HIV Input JSONsTheiaCoV Workflow Diagram
"},{"location":"workflows/genomic_characterization/theiacov/#supported-organisms","title":"Supported Organisms","text":"These workflows currently support the following organisms:
\"sars-cov-2\", \"SARS-CoV-2\") - default organism input\"MPXV\", \"mpox\", \"monkeypox\", \"Monkeypox virus\", \"Mpox\")\"HIV\")\"WNV\", \"wnv\", \"West Nile virus\")\"flu\", \"influenza\", \"Flu\", \"Influenza\")\"rsv_a\", \"rsv-a\", \"RSV-A\", \"RSV_A\")\"rsv_b\", \"rsv-b\", \"RSV-B\", \"RSV_B\")The compatibility of each workflow with each pathogen is shown below:
SARS-CoV-2 Mpox HIV WNV Influenza RSV-A RSV-B Illumina_PE \u2705 \u2705 \u2705 \u2705 \u2705 \u2705 \u2705 Illumina_SE \u2705 \u2705 \u274c \u2705 \u274c \u2705 \u2705 ClearLabs \u2705 \u274c \u274c \u274c \u274c \u274c \u274c ONT \u2705 \u2705 \u2705 \u274c \u2705 \u2705 \u2705 FASTA \u2705 \u2705 \u274c \u2705 \u2705 \u2705 \u2705We've provided the following information to help you set up the workflow for each organism in the form of input JSONs.
"},{"location":"workflows/genomic_characterization/theiacov/#inputs","title":"Inputs","text":"All TheiaCoV Workflows (not TheiaCoV_FASTA_Batch)
TheiaCoV_Illumina_PE Input Read DataThe TheiaCoV_Illumina_PE workflow takes in Illumina paired-end read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip before Terra uploads to minimize data upload time.
By default, the workflow anticipates\u00a02 x 150bp\u00a0reads (i.e. the input reads were generated using a 300-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate shorter read data, such as the 2 x 75bp reads generated using a 150-cycle sequencing kit.
TheiaCoV_Illumina_SE takes in Illumina single-end reads. Read file names should end with .fastq or .fq, with the optional addition of .gz. Theiagen highly recommends zipping files with gzip before uploading to Terra to minimize data upload time & save on storage costs.
By default, the workflow anticipates 1 x 35 bp reads (i.e. the input reads were generated using a 70-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate longer read data.
The TheiaCoV_ONT workflow takes in base-called ONT read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip before uploading to Terra to minimize data upload time.
The ONT sequencing kit and base-calling approach can produce substantial variability in the amount and quality of read data. Genome assemblies produced by the TheiaCoV_ONT workflow must be quality assessed before reporting results.
TheiaCoV_FASTA Input Assembly DataThe TheiaCoV_FASTA workflow takes in assembly files in FASTA format.
TheiaCoV_ClearLabs Input Read DataThe TheiaCoV_ClearLabs workflow takes in read data produced by the Clear Dx platform from ClearLabs. However, many users use the TheiaCoV_FASTA workflow instead of this one due to a few known issues when generating assemblies with this pipeline that are not present when using ClearLabs-generated FASTA files.
Terra Task Name Variable Type Description Default Value Terra Status * Organism theiacov_clearlabs primer_bed File The bed file containing the primers used when sequencing was performed Required CL sars-cov-2 theiacov_clearlabs read1 File Read data produced by the Clear Dx platform from ClearLabs Required CL sars-cov-2 theiacov_fasta assembly_fasta File Input assembly FASTA file Required FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 theiacov_fasta input_assembly_method String Method used to generate the assembly file Required FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 theiacov_illumina_pe read1 File Forward Illumina read in FASTQ file format (compression optional) Required PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 theiacov_illumina_pe read2 File Reverse Illumina read in FASTQ file format (compression optional) Required PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 theiacov_illumina_se read1 File Forward Illumina read in FASTQ file format (compression optional) Required SE MPXV, WNV, sars-cov-2 theiacov_ont read1 File Demultiplexed ONT read in FASTQ file format (compression optional) Required ONT HIV, MPXV, WNV, flu, sars-cov-2 workflow name samplename String Name of the sample being analyzed Required CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name seq_method String The sequencing methodology used to generate the input read data Required FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 clean_check_reads cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 clean_check_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 clean_check_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/bactopia/gather_samples:2.0.2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 clean_check_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 consensus cpu Int Number of CPUs to allocate to the task 8 Optional CL, ONT sars-cov-2 consensus disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, ONT sars-cov-2 consensus docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/artic:1.2.4-1.12.0 Optional CL, ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 consensus medaka_model String In order to obtain the best results, the appropriate model must be set to match the sequencer's basecaller model; this string takes the format of {pore}{device}. See the list of available models in the }_{caller_versionartic_consensus documentation section. See also https://github.com/nanoporetech/medaka?tab=readme-ov-file#models. r941_min_high_g360 Optional CL, ONT sars-cov-2 consensus memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional CL, ONT sars-cov-2 consensus_qc cpu Int Number of CPUs to allocate to the task 1 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 consensus_qc disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 consensus_qc docker String The Docker container to use for the task ngolin Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 consensus_qc genome_length Int Internal component, do not modify Do not modify, Optional CL, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 consensus_qc memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 fastq_scan_clean_reads cpu Int Number of CPUs to allocate to the task 1 Optional CL sars-cov-2 fastq_scan_clean_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 fastq_scan_clean_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional CL sars-cov-2 fastq_scan_clean_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional CL sars-cov-2 fastq_scan_clean_reads read1_name Int Internal component, do not modify Do not modify, Optional CL sars-cov-2 fastq_scan_raw_reads cpu Int Number of CPUs to allocate to the task 1 Optional CL sars-cov-2 fastq_scan_raw_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 fastq_scan_raw_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional CL sars-cov-2 fastq_scan_raw_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional CL sars-cov-2 fastq_scan_raw_reads read1_name Int Internal component, do not modify Do not modify, Optional CL sars-cov-2 flu_track abricate_flu_cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE flu flu_track abricate_flu_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE flu flu_track abricate_flu_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/abricate:1.0.1-insaflu-220727 Optional FASTA, ONT, PE flu flu_track abricate_flu_memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional FASTA, ONT, PE flu flu_track abricate_flu_mincov Int Minimum DNA % coverage 60 Optional FASTA, ONT, PE flu flu_track abricate_flu_minid Int Minimum DNA % identity 70 Optional FASTA, ONT, PE flu flu_track antiviral_aa_subs String Additional list of antiviral resistance associated amino acid substitutions of interest to be searched against those called on the sample segments. They take the format of :, e.g. NA:A26V Optional ONT, PE flu flu_track assembly_metrics_cpu Int Number of CPUs to allocate to the task 2 Optional PE flu flu_track assembly_metrics_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE flu flu_track assembly_metrics_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional PE flu flu_track assembly_metrics_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE flu flu_track flu_h1_ha_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_h1n1_m2_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_h3_ha_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_h3n2_m2_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_n1_na_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_n2_na_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_pa_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_pb1_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_pb2_ref File Internal component, do not modify Do not modify, Optional ONT, PE flu flu_track flu_subtype String The influenza subtype being analyzed. Used for picking nextclade datasets. Options: \"Yamagata\", \"Victoria\", \"H1N1\", \"H3N2\", \"H5N1\". Only use to override the subtype call from IRMA and ABRicate. Optional CL, ONT, PE, SE flu flu_track genoflu_cpu Int Number of CPUs to allocate to the task 1 Optional FASTA, ONT, PE flu flu_track genoflu_cross_reference File An Excel file to cross-reference BLAST findings; probably useful if novel genotypes are not in the default file used by genoflu.py Optional FASTA, ONT, PE flu_track genoflu_disk_size Int Amount of storage (in GB) to allocate to the task 25 Optional FASTA, ONT, PE flu_track genoflu_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/genoflu:1.03 Optional FASTA, ONT, PE flu_track genoflu_memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional FASTA, ONT, PE flu_track irma_cpu Int Number of CPUs to allocate to the task 4 Optional ONT, PE flu flu_track irma_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE flu flu_track irma_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/cdcgov/irma:v1.1.5 Optional ONT, PE flu flu_track irma_keep_ref_deletions Boolean True/False variable that determines if sites missed during read gathering should be deleted by ambiguation. TRUE Optional ONT, PE flu flu_track irma_memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT, PE flu flu_track nextclade_cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE flu flu_track nextclade_disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ONT, PE flu flu_track nextclade_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/nextstrain/nextclade:3.3.1 Optional ONT, PE flu flu_track nextclade_memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional ONT, PE flu flu_track nextclade_output_parser_cpu Int Number of CPUs to allocate to the task 2 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 flu_track nextclade_output_parser_disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 flu_track nextclade_output_parser_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/python/python:3.8.18-slim Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 flu_track nextclade_output_parser_memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 flu_track read2 File Internal component. Do not use. Optional ONT flu gene_coverage cpu Int Number of CPUs to allocate to the task 2 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage min_depth Int The minimum depth to determine if a position was covered. 10 Optional ONT, PE, SE MPXV, sars-cov-2 gene_coverage sc2_s_gene_start Int start nucleotide position of the SARS-CoV-2 Spike gene 21563 Optional CL, ONT, PE, SE MPXV, sars-cov-2 gene_coverage sc2_s_gene_stop Int End/Last nucleotide position of the SARS-CoV-2 Spike gene 25384 Optional CL, ONT, PE, SE MPXV, sars-cov-2 ivar_consensus ivar_bwa_cpu Int Number of CPUs to allocate to the task 6 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_bwa_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_bwa_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_bwa_memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_consensus_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_consensus_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_consensus_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_consensus_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_trim_primers_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_trim_primers_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_trim_primers_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_trim_primers_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_variant_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_variant_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_variant_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus ivar_variant_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus read2 File Internal component, do not modify Do not modify, Optional SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus skip_N Boolean True/False variable that determines if regions with depth less than minimum depth should not be added to the consensus sequence FALSE Optional PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_primtrim_cpu Int Number of CPUs to allocate to the task 2 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_primtrim_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_primtrim_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 ivar_consensus stats_n_coverage_primtrim_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional SE,PE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 kraken2_dehosted cpu Int Number of CPUs to allocate to the task 4 Optional CL sars-cov-2 kraken2_dehosted disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 kraken2_dehosted docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/kraken2:2.1.2-no-db Optional CL sars-cov-2 kraken2_dehosted kraken2_db File The database used to run Kraken2. Must contain viral and human sequences. \"gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz\" Optional CL sars-cov-2 kraken2_dehosted memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL sars-cov-2 kraken2_dehosted read2 File Internal component, do not modify Do not modify, Optional CL sars-cov-2 kraken2_raw cpu Int Number of CPUs to allocate to the task 4 Optional CL sars-cov-2 kraken2_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 kraken2_raw docker_image Int Docker container used in this task us-docker.pkg.dev/general-theiagen/staphb/kraken2:2.1.2-no-db Optional CL sars-cov-2 kraken2_raw kraken2_db File The database used to run Kraken2. Must contain viral and human sequences. \"gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz\" Optional CL sars-cov-2 kraken2_raw memory String Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL sars-cov-2 kraken2_raw read_processing String The tool used for trimming of primers from reads. Options are trimmomatic and fastp trimmomatic Optional HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 kraken2_raw read2 File Internal component, do not modify Do not modify, Optional CL sars-cov-2 nanoplot_clean cpu Int Number of CPUs to allocate to the task 4 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_clean disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_clean docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_clean max_length Int The maximum length of clean reads, for which reads longer than the length specified will be hidden. 100000 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_clean memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw cpu Int Number of CPUs to allocate to the task 4 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw max_length Int The maximum length of clean reads, for which reads longer than the length specified will be hidden. 100000 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nanoplot_raw memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 ncbi_scrub_se cpu Int Number of CPUs to allocate to the task 4 Optional CL sars-cov-2 ncbi_scrub_se disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL sars-cov-2 ncbi_scrub_se docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/ncbi/sra-human-scrubber:2.2.1 Optional CL sars-cov-2 ncbi_scrub_se memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL sars-cov-2 nextclade_output_parser cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_output_parser disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_output_parser docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/python/python:3.8.18-slim Optional ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_output_parser memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 auspice_reference_tree_json File An Auspice JSON phylogenetic reference tree which serves as a target for phylogenetic placement. Inherited from nextclade dataset Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 cpu Int Number of CPUs to allocate to the task 2 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/nextstrain/nextclade:3.3.1 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 gene_annotations_gff File A genome annotation to specify how to translate the nucleotide sequence to proteins (genome_annotation.gff3). specifying this enables codon-informed alignment and protein alignments. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/03-genome-annotation.html Inherited from nextclade dataset Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 input_ref File A nucleotide sequence which serves as a reference for the pairwise alignment of all input sequences. This is also the sequence which defines the coordinate system of the genome annotation. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/02-reference-sequence.html Inherited from nextclade dataset Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 nextclade_pathogen_json File General dataset configuration file. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/05-pathogen-config.html Inherited from nextclade dataset Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 nextclade_v3 verbosity String other options are: \"off\" , \"error\" , \"info\" , \"debug\" , and \"trace\" (highest level of verbosity) warn Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 organism_parameters auspice_config File Auspice config file used in Augur_PHB workflow.Defaults set for various organisms & Flu segments. A minimal auspice config file is set in cases where organism is not specified and user does not provide an optional input config file. Optional Augur, CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 organism_parameters flu_segment String Influenza genome segment being analyzed. Options: \"HA\" or \"NA\". Automatically determined. This input is ignored if provided for TheiaCoV_Illumina_SE and TheiaCoV_ClearLabs N/A Optional CL, ONT, PE, SE flu organism_parameters flu_subtype String The influenza subtype being analyzed. Options: \"Yamagata\", \"Victoria\", \"H1N1\", \"H3N2\", \"H5N1\". Automatically determined. This input is ignored if provided for TheiaCoV_Illumina_SE and TheiaCoV_ClearLabs N/A Optional CL, ONT, PE, SE flu organism_parameters gene_locations_bed_file File Use to provide locations of interest where average coverage will be calculated Default provided for SARS-CoV-2 (\"gs://theiagen-public-files-rp/terra/sars-cov-2-files/sc2_gene_locations.bed\") and mpox (\"gs://theiagen-public-files/terra/mpxv-files/mpox_gene_locations.bed\") Optional CL, FASTA organism_parameters genome_length_input Int Use to specify the expected genome length; provided by default for all supported organisms Default provided for SARS-CoV-2 (29903), mpox (197200), WNV (11000), flu (13000), RSV-A (16000), RSV-B (16000), HIV (primer versions 1 [9181] and 2 [9840]) Optional CL organism_parameters hiv_primer_version String The version of HIV primers used. Options are \"https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl#L156\" and \"https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl#L164\". This input is ignored if provided for TheiaCoV_Illumina_SE and TheiaCoV_ClearLabs v1 Optional CL, FASTA, ONT, PE, SE HIV organism_parameters kraken_target_organism_input String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. Default provided for mpox (Monkeypox virus), WNV (West Nile virus), and HIV (Human immunodeficiency virus 1) Optional FASTA, ONT, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 organism_parameters pangolin_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.29 Optional CL, FASTA organism_parameters primer_bed_file File The bed file containing the primers used when sequencing was performed REQUIRED FOR SARS-CoV-2, MPOX, WNV, RSV-A & RSV-B. Provided by default only for HIV primer versions 1 (\"gs://theiagen-public-files/terra/hivgc-files/HIV-1_v1.0.primer.hyphen.bed\" and 2 (\"gs://theiagen-public-files/terra/hivgc-files/HIV-1_v2.0.primer.hyphen400.1.bed\") Optional, Sometimes required CL, FASTA organism_parameters reference_gff_file File Reference GFF file for the organism being analyzed Default provided for mpox (\"gs://theiagen-public-files/terra/mpxv-files/Mpox-MT903345.1.reference.gff3\") and HIV (primer versions 1 [\"gs://theiagen-public-files/terra/hivgc-files/NC_001802.1.gff3\"] and 2 [\"gs://theiagen-public-files/terra/hivgc-files/AY228557.1.gff3\"]) Optional CL, FASTA, ONT organism_parameters vadr_max_length Int Maximum length for the fasta-trim-terminal-ambigs.pl VADR script Default provided for SARS-CoV-2 (30000), mpox (210000), WNV (11000), flu (0), RSV-A (15500) and RSV-B (15500). Optional CL organism_parameters vadr_memory Int Amount of memory/RAM (in GB) to allocate to the task 32 (RSV-A and RSV-B) and 8 (all other TheiaCoV organisms) Optional CL, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 organism_parameters vadr_options String Options for the v-annotate.pl VADR script Default provided for SARS-CoV-2 (\"--noseqnamemax --glsearch -s -r --nomisc --mkey sarscov2 --lowsim5seq 6 --lowsim3seq 6 --alt_fail lowscore,insertnn,deletinn --out_allfasta\"), mpox (\"--glsearch -s -r --nomisc --mkey mpxv --r_lowsimok --r_lowsimxd 100 --r_lowsimxl 2000 --alt_pass discontn,dupregin --out_allfasta --minimap2 --s_overhang 150\"), WNV (\"--mkey flavi --mdir /opt/vadr/vadr-models-flavi/ --nomisc --noprotid --out_allfasta\"), flu (\"\"), RSV-A (\"-r --mkey rsv --xnocomp\"), and RSV-B (\"-r --mkey rsv --xnocomp\") Optional CL organism_parameters vadr_skip_length Int Minimum assembly length (unambiguous) to run VADR 10000 Optional CL MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 pangolin4 analysis_mode String Pangolin inference engine for lineage designations (usher or pangolearn). Default is Usher. Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 cpu Int Number of CPUs to allocate to the task 4 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 expanded_lineage Boolean True/False that determines if a lineage should be expanded without aliases (e.g., BA.1 \u2192 B.1.1.529.1) TRUE Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 max_ambig Float The maximum proportion of Ns allowed for pangolin to attempt an assignment 0.5 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 min_length Int Minimum query length allowed for pangolin to attempt an assignment 10000 Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 pangolin_arguments String Optional arguments for pangolin e.g. ''--skip-scorpio'' Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 skip_designation_cache Boolean A True/False option that determines if the designation cache should be used FALSE Optional CL, FASTA, ONT, PE, SE sars-cov-2 pangolin4 skip_scorpio Boolean A True/False option that determines if scorpio should be skipped. FALSE Optional CL, FASTA, ONT, PE, SE sars-cov-2 qc_check_task ani_highest_percent Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task ani_highest_percent_bases_aligned Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task assembly_length Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task assembly_mean_coverage Int Internal component, do not modify Do not modify, Optional FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task busco_results String Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task combined_mean_q_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task combined_mean_q_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task combined_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task combined_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task cpu Int Number of CPUs to allocate to the task 4 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task est_coverage_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task est_coverage_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task gambit_predicted_taxon String Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task kraken_human String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task kraken_human_dehosted String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task kraken_sc2 String Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task kraken_sc2_dehosted String Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task kraken_target_organism Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task kraken_target_organism_dehosted Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task midas_secondary_genus_abundance Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task midas_secondary_genus_coverage Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task minbaseq_trim Int Internal component, do not modify Do not modify, Optional FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task n50_value Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task num_reads_clean2 Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, SE qc_check_task num_reads_raw2 Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, SE qc_check_task number_contigs Int Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task quast_gc_percent Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r1_mean_q_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r1_mean_q_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r1_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r1_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r2_mean_q_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r2_mean_q_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r2_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task r2_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task sc2_s_gene_mean_coverage Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 qc_check_task sc2_s_gene_percent_coverage Float Internal component, do not modify Do not modify, Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 quasitools_illumina_pe cpu Int Number of CPUs to allocate to the task 2 Optional PE HIV quasitools_illumina_pe disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional PE HIV quasitools_illumina_pe docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/quasitools:0.7.0--pyh864c0ab_1 Optional PE HIV quasitools_illumina_pe memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional PE HIV quasitools_ont cpu Int Number of CPUs to allocate to the task 2 Optional ONT HIV quasitools_ont disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ONT HIV quasitools_ont docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/quasitools:0.7.0--pyh864c0ab_1 Optional ONT HIV quasitools_ont memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional ONT HIV quasitools_ont read2 File Internal component. Do not use. Do not modify, Optional ONT HIV raw_check_reads cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 raw_check_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 raw_check_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/bactopia/gather_samples:2.0.2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 raw_check_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim bbduk_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim call_kraken Boolean True/False variable that determines if the Kraken2 task should be called. FALSE Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim call_midas Boolean True/False variable that determines if the MIDAS task should be called. TRUE Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim downsampling_coverage Float The desired coverage to sub-sample the reads to with RASUSA 150 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim fastp_args String Additional fastp task arguments --detect_adapter_for_pe -g -5 20 -3 20 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim kraken_db File The database used to run Kraken2. Must contain viral and human sequences. \"gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz\" Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim kraken_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim midas_db File The database used by the MIDAS task gs://theiagen-public-files-rp/terra/theiaprok-files/midas/midas_db_v1.2.tar.gz Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim read_processing String The name of the tool to perform basic read processing; options: \"trimmomatic\" or \"fastp\" trimmomatic Optional PE, SE read_QC_trim read_qc String The tool used for quality control (QC) of reads. Options are fastq_scan and fastqc fastq_scan Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim target_organism String Organism to search for in Kraken Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 read_QC_trim trimmomatic_args String Additional arguments to pass to trimmomatic -phred33 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 set_flu_ha_nextclade_values reference_gff_file File Reference GFF file for flu HA Do not modify, Optional ONT flu set_flu_na_nextclade_values reference_gff_file Int Reference GFF file for flu NA Do not modify, Optional ONT flu set_flu_na_nextclade_values vadr_mem Int Memory, in GB, allocated to this task 8 Do not modify, Optional ONT flu stats_n_coverage cpu Int Number of CPUs to allocate to the task 2 Optional CL, ONT stats_n_coverage disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, ONT stats_n_coverage docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional CL, ONT stats_n_coverage memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, ONT stats_n_coverage_primtrim cpu Int Number of CPUs to allocate to the task 2 Optional CL, ONT stats_n_coverage_primtrim disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, ONT stats_n_coverage_primtrim docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/samtools:1.15 Optional CL, ONT stats_n_coverage_primtrim memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional CL, ONT vadr cpu Int Number of CPUs to allocate to the task 2 Optional CL, FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional CL, FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/vadr:1.5.1 Optional CL, FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr max_length Int Maximum length of contig allowed to run VADR Optional CL HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr memory Int Amount of memory/RAM (in GB) to allocate to the task 32 (RSV-A and RSV-B) and 8 (all other TheiaCoV organisms) Optional CL MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr min_length Int Minimum length subsequence to possibly replace Ns for the http://fasta-trim-terminal-ambigs.pl/ VADR script 50 Optional CL, FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr skip_length Int Minimum assembly length (unambiguous) to run VADR 10000 Optional CL MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 vadr vadr_opts String Additional options to provide to VADR Optional CL HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional ONT, PE, SE, FASTA, CL HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional ONT, PE, SE, FASTA, CL HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name adapters File File that contains the adapters used /bbmap/resources/adapters.fa Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name consensus_min_freq Float The minimum frequency for a variant to be called a SNP in consensus genome 0.6 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name flu_segment String Influenza genome segment being analyzed. Options: \"HA\" or \"NA\". HA Optional, Required FASTA workflow name flu_subtype String The influenza subtype being analyzed. Options: \"Yamagata\", \"Victoria\", \"H1N1\", \"H3N2\", \"H5N1\". Automatically determined. Optional FASTA workflow name genome_length Int Use to specify the expected genome length Optional FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name max_genome_length Int Maximum genome length able to pass read screening 2673870 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name max_length Int Maximum length for a read based on the SARS-CoV-2 primer scheme 700 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name medaka_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/artic-ncov2019:1.3.0-medaka-1.4.3 Optional CL workflow name min_basepairs Int Minimum base pairs to pass read screening 34000 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_coverage Int Minimum coverage to pass read screening 10 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_depth Int Minimum depth of reads required to call variants and generate a consensus genome. This value is passed to the iVar software. 100 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_genome_length Int Minimum genome length to pass read screening 1700 Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_length Int Minimum length of a read based on the SARS-CoV-2 primer scheme 400 Optional ONT HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_proportion Int Minimum read proportion to pass read screening 40 Optional PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name min_reads Int Minimum reads to pass read screening 113 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name nextclade_dataset_name String Nextclade organism dataset names. However, if organism input is set correctly, this input will be automatically assigned the corresponding dataset name. See organism defaults for more information Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name nextclade_dataset_tag String Nextclade dataset tag. Used for pulling up-to-date reference genomes and associated information specific to nextclade datasets (QC thresholds, organism-specific information like SARS-CoV-2 clade & lineage information, etc.) that is required for running the Nextclade tool. Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name normalise Int Used to normalize the amount of reads to the indicated level before variant calling 20000 for CL, 200 for ONT Optional CL, ONT workflow name organism String The organism that is being analyzed. Options: \"sars-cov-2\", \"MPXV\", \"WNV\", \"HIV\", \"flu\", \"rsv_a\", \"rsv_b\". However, \"flu\" is not available for TheiaCoV_Illumina_SE sars-cov-2 Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name pangolin_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.29 Do not modify, Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name phix File File that contains the phix used /bbmap/resources/phix174_ill.ref.fa.gz Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name primer_bed File The bed file containing the primers used when sequencing was performed Optional ONT, PE, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 workflow name qc_check_table File A TSV file with optional user input QC values to be compared against the default workflow value Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name reference_gene_locations_bed File Use to provide locations of interest where average coverage will be calculated Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name reference_genome File An optional reference genome used for consensus assembly and QC Optional CL, FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name reference_gff File The general feature format (gff) of the reference genome. Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name seq_method String The sequencing methodology used to generate the input read data ILLUMINA Optional CL, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name skip_mash Boolean A True/False option that determines if mash should be skipped in the screen task. FALSE Optional ONT, SE HIV, MPXV, WNV, rsv_a, rsv_b, sars-cov-2 workflow name skip_screen Boolean A True/False option that determines if the screen task should be skipped. FALSE Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name target_organism String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. Optional CL, ONT, PE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name trim_min_length Int The minimum length of each read after trimming 75 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name trim_primers Boolean A True/False option that determines if primers should be trimmed. TRUE Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name trim_quality_min_score Int The minimum quality score to keep during trimming 30 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name trim_window_size Int Specifies window size for trimming (the number of bases to average the quality across) 4 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_max_length Int Maximum length of contig allowed to run VADR Optional FASTA, ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_memory Int Amount of memory/RAM (in GB) to allocate to the task 32 (RSV-A and RSV-B) and 8 (all other TheiaCoV organisms) Optional FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_options String Additional options to provide to VADR Optional ONT, PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_opts String Additional options to provide to VADR Optional FASTA HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name vadr_skip_length Int Minimum assembly length (unambiguous) to run VADR 10000 Optional FASTA, ONT, PE, SE MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 workflow name variant_min_freq Float Minimum frequency for a variant to be reported in ivar outputs 0.6 Optional PE, SE HIV, MPXV, WNV, flu, rsv_a, rsv_b, sars-cov-2 TheiaCoV_FASTA_Batch_PHB Inputs"},{"location":"workflows/genomic_characterization/theiacov/#theiacov-fasta-batch-inputs","title":"TheiaCoV_FASTA_Batch Inputs","text":"Input Data The TheiaCoV_FASTA_Batch workflow takes in a set of assembly files in FASTA format.
Terra Task Name Variable Type Description Default Value Terra Status theiacov_fasta_batch assembly_fastas Array[File] Genome assembly files in fasta format. Example: this.sars-cov-2-samples.assembly_fasta Required theiacov_fasta_batch bucket_name String The GCP bucket for the workspace where the TheiaCoV_FASTA_Batch output files are saved. We recommend using a unique GSURI for the bucket associated with your Terra workspace. The root GSURI is accessible in the Dashboard page of your workspace in the \"Cloud Information\" section.Do not include the prefix gs:// in the stringExample: \"\"fc-c526190d-4332-409b-8086-be7e1af9a0b6/theiacov_fasta_batch-2024-04-15-seq-run-1/ Required theiacov_fasta_batch project_name String The name of the Terra project where the data can be found. Example: \"my-terra-project\" Required theiacov_fasta_batch samplenames Array[String] The names of the samples to be analyzed. Example: this.sars-cov-2-samples.sars-cov-2-sample_id Required theiacov_fasta_batch table_name String The name of the Terra table where the data can be found. Example: \"sars-cov-2-sample\" Required theiacov_fasta_batch workspace_name String The name of the Terra workspace where the data can be found. Example \"my-terra-workspace\" Required cat_files_fasta cpu Int Number of CPUs to allocate to the task 2 Optional cat_files_fasta disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional cat_files_fasta docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional cat_files_fasta memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional nextclade_v3 auspice_reference_tree_json File The phylogenetic reference tree which serves as a target for phylogenetic placement default is inherited from NextClade dataset Optional nextclade_v3 cpu Int Number of CPUs to allocate to the task 2 Optional nextclade_v3 disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional nextclade_v3 docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/nextstrain/nextclade:3.3.1 Optional nextclade_v3 gene_annotations_gff File A genome annotation to specify how to translate the nucleotide sequence to proteins (genome_annotation.gff3). specifying this enables codon-informed alignment and protein alignments. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/03-genome-annotation.html None Optional nextclade_v3 input_ref File A nucleotide sequence which serves as a reference for the pairwise alignment of all input sequences. This is also the sequence which defines the coordinate system of the genome annotation. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/02-reference-sequence.html None Optional nextclade_v3 memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional nextclade_v3 nextclade_pathogen_json File General dataset configuration file. See here for more info: https://docs.nextstrain.org/projects/nextclade/en/latest/user/input-files/05-pathogen-config.html None Optional nextclade_v3 verbosity String other options are: \"off\" , \"error\" , \"info\" , \"debug\" , and \"trace\" (highest level of verbosity) warn Optional organism_parameters flu_segment String Optional organism_parameters flu_subtype String Optional organism_parameters gene_locations_bed_file File Optional organism_parameters genome_length_input Int Optional organism_parameters hiv_primer_version String Optional organism_parameters kraken_target_organism_input String Optional organism_parameters primer_bed_file File Optional organism_parameters reference_genome File Optional organism_parameters reference_gff_file File Optional organism_parameters vadr_max_length Int Optional organism_parameters vadr_mem Int Optional organism_parameters vadr_options String Optional pangolin4 analysis_mode String Used to switch between usher and pangolearn analysis modes. Only use usher because pangolearn is no longer supported as of Pangolin v4.3 and higher versions. None Optional pangolin4 cpu Int Number of CPUs to allocate to the task 4 Optional pangolin4 disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pangolin4 expanded_lineage Boolean True/False that determines if a lineage should be expanded without aliases (e.g., BA.1 \u2192 B.1.1.529.1) TRUE Optional pangolin4 max_ambig Float The maximum proportion of Ns allowed for pangolin to attempt an assignment 0.5 Optional pangolin4 memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional pangolin4 skip_designation_cache Boolean True/False that determines if the designation cache should be used FALSE Optional pangolin4 skip_scorpio Boolean True/False that determines if scorpio should be skipped. FALSE Optional sm_theiacov_fasta_wrangling cpu Int Number of CPUs to allocate to the task 8 Optional sm_theiacov_fasta_wrangling disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional sm_theiacov_fasta_wrangling docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-08-28-v4 Optional sm_theiacov_fasta_wrangling memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional theiacov_fasta_batch nextclade_dataset_name String Nextclade organism dataset name. Options: \"nextstrain/sars-cov-2/wuhan-hu-1/orfs\" However, if organism input is set correctly, this input will be automatically assigned the corresponding dataset name. sars-cov-2 Optional theiacov_fasta_batch nextclade_dataset_tag String Nextclade dataset tag. Used for pulling up-to-date reference genomes and associated information specific to nextclade datasets (QC thresholds, organism-specific information like SARS-CoV-2 clade & lineage information, etc.) that is required for running the Nextclade tool. 2024-06-13--23-42-47Z Optional theiacov_fasta_batch organism String The organism that is being analyzed. Options: \"sars-cov-2\" sars-cov-2 Optional theiacov_fasta_batch pangolin_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.27 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/theiacov/#org-specific","title":"Organism-specific parameters and logic","text":"The organism_parameters sub-workflow is the first step in all TheiaCoV workflows. This step automatically sets the different parameters needed for each downstream tool to the appropriate value for the user-designated organism (by default, \"sars-cov-2\" is the default organism).
The following tables include the relevant organism-specific parameters; all of these default values can be overwritten by providing a value for the \"Overwrite Variable Name\" field.
SARS-CoV-2 Defaults Overwrite Variable Name Organism Default Value gene_locations_bed_file sars-cov-2\"gs://theiagen-public-files-rp/terra/sars-cov-2-files/sc2_gene_locations.bed\" genome_length_input sars-cov-2 29903 kraken_target_organism_input sars-cov-2 \"Severe acute respiratory syndrome coronavirus 2\" nextclade_dataset_name_input sars-cov-2 \"nextstrain/sars-cov-2/wuhan-hu-1/orfs\" nextclade_dataset_tag_input sars-cov-2 \"2024-11-19--14-18-53Z\" pangolin_docker_image sars-cov-2 \"us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.31 \" reference_genome sars-cov-2 \"gs://theiagen-public-files-rp/terra/augur-sars-cov-2-references/MN908947.fasta\" vadr_max_length sars-cov-2 30000 vadr_mem sars-cov-2 8 vadr_options sars-cov-2 \"--noseqnamemax --glsearch -s -r --nomisc --mkey sarscov2 --lowsim5seq 6 --lowsim3seq 6 --alt_fail lowscore,insertnn,deletinn --out_allfasta\" Mpox Defaults Overwrite Variable Name Organism Default Value gene_locations_bed_file MPXV \"gs://theiagen-public-files/terra/mpxv-files/mpox_gene_locations.bed\" genome_length_input MPXV 197200 kraken_target_organism_input MPXV \"Monkeypox virus\" nextclade_dataset_name_input MPXV \"nextstrain/mpox/lineage-b.1\" nextclade_dataset_tag_input MPXV \"2024-11-19--14-18-53Z\" primer_bed_file MPXV \"gs://theiagen-public-files/terra/mpxv-files/MPXV.primer.bed\" reference_genome MPXV \"gs://theiagen-public-files/terra/mpxv-files/MPXV.MT903345.reference.fasta\" reference_gff_file MPXV \"gs://theiagen-public-files/terra/mpxv-files/Mpox-MT903345.1.reference.gff3\" vadr_max_length MPXV 210000 vadr_mem MPXV 8 vadr_options MPXV \"--glsearch -s -r --nomisc --mkey mpxv --r_lowsimok --r_lowsimxd 100 --r_lowsimxl 2000 --alt_pass discontn,dupregin --out_allfasta --minimap2 --s_overhang 150\" WNV Defaults Overwrite Variable Name Organism Default Value Notes genome_length_input WNV 11000 kraken_target_organism_input WNV \"West Nile virus\" nextclade_dataset_name_input WNV \"NA\" TheiaCoV's Nextclade currently does not support WNV nextclade_dataset_tag_input WNV \"NA\" TheiaCoV's Nextclade currently does not support WNV primer_bed_file WNV \"gs://theiagen-public-files/terra/theiacov-files/WNV/WNV-L1_primer.bed\" reference_genome WNV \"gs://theiagen-public-files/terra/theiacov-files/WNV/NC_009942.1_wnv_L1.fasta\" vadr_max_length WNV 11000 vadr_mem WNV 8 vadr_options WNV \"--mkey flavi --mdir /opt/vadr/vadr-models-flavi/ --nomisc --noprotid --out_allfasta\" Flu Defaults Overwrite Variable Name Organism Flu Segment Flu Subtype Default Value Notes flu_segment flu all all N/A TheiaCoV will attempt to automatically assign a flu segment flu_subtype flu all all N/A TheiaCoV will attempt to automatically assign a flu subtype genome_length_input flu all all 13500 vadr_max_length flu all all 13500 vadr_mem flu all all 8 vadr_options flu all all \"--atgonly --xnocomp --nomisc --alt_fail extrant5,extrant3 --mkey flu\" nextclade_dataset_name_input flu ha h1n1 \"nextstrain/flu/h1n1pdm/ha/MW626062\" nextclade_dataset_tag_input flu ha h1n1 \"2024-11-27--02-51-00Z\" reference_genome flu ha h1n1 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h1n1pdm_ha.fasta\" nextclade_dataset_name_input flu ha h3n2 \"nextstrain/flu/h3n2/ha/EPI1857216\" nextclade_dataset_tag_input flu ha h3n2 \"2024-11-27--02-51-00Z\" reference_genome flu ha h3n2 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h3n2_ha.fasta\" nextclade_dataset_name_input flu ha victoria \"nextstrain/flu/vic/ha/KX058884\" nextclade_dataset_tag_input flu ha victoria \"2024-11-05--09-19-52Z\" reference_genome flu ha victoria \"gs://theiagen-public-files-rp/terra/flu-references/reference_vic_ha.fasta\" nextclade_dataset_name_input flu ha yamagata \"nextstrain/flu/yam/ha/JN993010\" nextclade_dataset_tag_input flu ha yamagata \"2024-01-30--16-34-55Z\" reference_genome flu ha yamagata \"gs://theiagen-public-files-rp/terra/flu-references/reference_yam_ha.fasta\" nextclade_dataset_name_input flu ha h5n1 \"community/moncla-lab/iav-h5/ha/all-clades\" nextclade_dataset_tag_input flu ha h5n1 \"2024-12-04--17-05-31Z\" reference_genome flu ha h5n1 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h5n1_ha.fasta\" nextclade_dataset_name_input flu na h1n1 \"nextstrain/flu/h1n1pdm/na/MW626056\" nextclade_dataset_tag_input flu na h1n1 \"2024-11-05--09-19-52Z\" reference_genome flu na h1n1 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h1n1pdm_na.fasta\" nextclade_dataset_name_input flu na h3n2 \"nextstrain/flu/h3n2/na/EPI1857215\" nextclade_dataset_tag_input flu na h3n2 \"2024-11-05--09-19-52Z\" reference_genome flu na h3n2 \"gs://theiagen-public-files-rp/terra/flu-references/reference_h3n2_na.fasta\" nextclade_dataset_name_input flu na victoria \"nextstrain/flu/vic/na/CY073894\" nextclade_dataset_tag_input flu na victoria \"2024-11-05--09-19-52Z\" reference_genome flu na victoria \"gs://theiagen-public-files-rp/terra/flu-references/reference_vic_na.fasta\" nextclade_dataset_name_input flu na yamagata \"NA\" nextclade_dataset_tag_input flu na yamagata \"NA\" reference_genome flu na yamagata \"gs://theiagen-public-files-rp/terra/flu-references/reference_yam_na.fasta\" RSV-A Defaults Overwrite Variable Name Organism Default Value genome_length_input rsv_a 16000 kraken_target_organism rsv_a \"Human respiratory syncytial virus A\" nextclade_dataset_name_input rsv_a nextstrain/rsv/a/EPI_ISL_412866 nextclade_dataset_tag_input rsv_a \"2024-11-27--02-51-00Z\" reference_genome rsv_a gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_a.fasta vadr_max_length rsv_a 15500 vadr_mem rsv_a 32 vadr_options rsv_a -r --mkey rsv --xnocomp RSV-B Defaults Overwrite Variable Name Organism Default Value genome_length_input rsv_b 16000 kraken_target_organism rsv_b \"human respiratory syncytial virus\" nextclade_dataset_name_input rsv_b nextstrain/rsv/b/EPI_ISL_1653999 nextclade_dataset_tag_input rsv_b \"2024-11-27--02-51-00Z\" reference_genome rsv_b gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_b.fasta vadr_max_length rsv_b 15500 vadr_mem rsv_b 32 vadr_options rsv_b -r --mkey rsv --xnocomp HIV Defaults Overwrite Variable Name Organism Default Value Notes kraken_target_organism_input HIV Human immunodeficiency virus 1 genome_length_input HIV-v1 9181 This version of HIV originates from Oregon primer_bed_file HIV-v1 gs://theiagen-public-files/terra/hivgc-files/HIV-1_v1.0.primer.hyphen.bed This version of HIV originates from Oregon reference_genome HIV-v1 gs://theiagen-public-files/terra/hivgc-files/NC_001802.1.fasta This version of HIV originates from Oregon reference_gff_file HIV-v1 gs://theiagen-public-files/terra/hivgc-files/NC_001802.1.gff3 This version of HIV originates from Oregon genome_length_input HIV-v2 9840 This version of HIV originates from Southern Africa primer_bed_file HIV-v2 gs://theiagen-public-files/terra/hivgc-files/HIV-1_v2.0.primer.hyphen400.1.bed This version of HIV originates from Southern Africa reference_genome HIV-v2 gs://theiagen-public-files/terra/hivgc-files/AY228557.1.headerchanged.fasta This version of HIV originates from Southern Africa reference_gff_file HIV-v2 gs://theiagen-public-files/terra/hivgc-files/AY228557.1.gff3 This version of HIV originates from Southern Africa"},{"location":"workflows/genomic_characterization/theiacov/#workflow-tasks","title":"Workflow Tasks","text":"All input reads are processed through \"core tasks\" in the TheiaCoV Illumina, ONT, and ClearLabs workflows. These undertake read trimming and assembly appropriate to the input data type. TheiaCoV workflows subsequently launch default genome characterization modules for quality assessment, and additional taxa-specific characterization steps. When setting up the workflow, users may choose to use \"optional tasks\" as additions or alternatives to tasks run in the workflow by default.
"},{"location":"workflows/genomic_characterization/theiacov/#core-tasks","title":"Core tasks","text":"These tasks are performed regardless of organism, and perform read trimming and various quality control steps.
versioning: Version capture for TheiaCoV The versioning task captures the workflow version from the GitHub (code repository) version.
Version Capture Technical details
Links Task task_versioning.wdlscreen: Total Raw Read Quantification and Genome Size Estimation The screen task ensures the quantity of sequence data is sufficient to undertake genomic analysis. It uses fastq-scan and bash commands for quantification of reads and base pairs, and mash sketching to estimate the genome size and its coverage. At each step, the results are assessed relative to pass/fail criteria and thresholds that may be defined by optional user inputs. Samples that do not meet these criteria will not be processed further by the workflow:
min_reads.min_proportion basepairs are in either the reads1 or read2 files.min_basepairs basepairsmin_genome_size or bigger than max_genome_size.min_coverage.Read screening is undertaken on both the raw and cleaned reads. The task may be skipped by setting the skip_screen variable to true.
Default values vary between the PE and SE workflow. The rationale for these default values can be found below.
Variable Rationaleskip_screen Set to true to skip the read screen from running min_reads Minimum number of base pairs for 10x coverage of the Hepatitis delta (of the Deltavirus genus) virus divided by 300 (longest Illumina read length) min_basepairs Greater than 10x coverage of the Hepatitis delta (of the Deltavirus genus) virus min_genome_size Based on the Hepatitis delta (of the Deltavirus genus) genome- the smallest viral genome as of 2024-04-11 (1,700 bp) max_genome_size Based on the Pandoravirus salinus genome, the biggest viral genome, (2,673,870 bp) with 2 Mbp added min_coverage A bare-minimum coverage for genome characterization. Higher coverage would be required for high-quality phylogenetics. min_proportion Greater than 50% reads are in the read1 file; others are in the read2 file Screen Technical Details
There is a single WDL task for read screening. The screen task is run twice, once for raw reads and once for clean reads.
read_QC_trim_pe and read_QC_trim_se: Read Quality Trimming, Host and Adapter Removal, Quantification, and Identification for Illumina workflows read_QC_trim is a sub-workflow within TheiaCoV that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below. The differences between TheiaCoV PE and SE in the read_QC_trim sub-workflow lie in the default parameters, the use of two or one input read file(s), and the different output files.
All reads of human origin are removed, including their mates, by using NCBI's human read removal tool (HRRT).
HRRT is based on the SRA Taxonomy Analysis Tool and employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records with any k-mers found in non-Eukaryota RefSeq records subtracted from the database.
NCBI-Scrub Technical Details
Links Task task_ncbi_scrub.wdl Software Source Code NCBI Scrub on GitHub Software Documentation https://github.com/ncbi/sra-human-scrubber/blob/master/README.md Read quality trimmingEither trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.
If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:
Parameter Explanation -g enables polyG tail trimming -5 20 enables read end-trimming -3 20 enables read end-trimming --detect_adapter_for_pe enables adapter-trimming only for paired-end reads Adapter removalThe BBDuk task removes adapters from sequence reads. To do this:
Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.
Read QuantificationThere are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. In TheiaProk_Illumina_PE, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.
Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
Kraken2 is run on the set of raw reads, provided as input, as well as the set of clean reads that are resulted from the read_QC_trim workflow
Database-dependent
TheiaCoV automatically uses a viral-specific Kraken2 database. This database was generated in-house from RefSeq's viral sequence collection and human genome GRCh38. It's available at gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz
Kraken2 Technical Details
Links Task task_kraken2.wdl Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/wiki Original Publication(s) Improved metagenomic analysis with Kraken 2read_QC_trim Technical Details
Links Sub-workflow wf_read_QC_trim_pe.wdlwf_read_QC_trim_se.wdl Tasks task_fastp.wdltask_trimmomatic.wdltask_bbduk.wdltask_fastq_scan.wdltask_midas.wdltask_kraken2.wdl Software Source Code fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2 Software Documentation fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2 Original Publication(s) Trimmomatic: a flexible trimmer for Illumina sequence datafastp: an ultra-fast all-in-one FASTQ preprocessorAn integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeographyImproved metagenomic analysis with Kraken 2read_QC_trim_ONT: Read Quality Trimming, Host Removal, and Identification for ONT data read_QC_trim is a sub-workflow within TheiaCoV that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below.
All reads of human origin are removed, including their mates, by using NCBI's human read removal tool (HRRT).
HRRT is based on the SRA Taxonomy Analysis Tool and employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records with any k-mers found in non-Eukaryota RefSeq records subtracted from the database.
NCBI-Scrub Technical Details
Links Task task_ncbi_scrub.wdl Software Source Code NCBI Scrub on GitHub Software Documentation https://github.com/ncbi/sra-human-scrubber/blob/master/README.md Read quality filteringRead filtering is performed using artic guppyplex which performs a quality check by filtering the reads by length to remove chimeric reads.
Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
Kraken2 is run on the set of raw reads, provided as input, as well as the set of clean reads that are resulted from the read_QC_trim workflow
Database-dependent
TheiaCoV automatically uses a viral-specific Kraken2 database. This database was generated in-house from RefSeq's viral sequence collection and human genome GRCh38. It's available at gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz
Kraken2 Technical Details
Links Task task_kraken2.wdl Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/wiki Original Publication(s) Improved metagenomic analysis with Kraken 2read_QC_trim Technical Details
Each TheiaCoV workflow calls a sub-workflow listed below, which then calls the individual tasks:
Workflow TheiaCoV_ONT Sub-workflow wf_read_QC_trim_ont.wdl Tasks task_ncbi_scrub.wdl (SE subtask)task_artic_guppyplex.wdltask_kraken2.wdl Software Source Code NCBI Scrub on GitHubArtic on GitHubKraken2 on GitHub Software Documentation NCBI ScrubArtic pipelineKraken2 Original Publication(s) STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissionsImproved metagenomic analysis with Kraken 2"},{"location":"workflows/genomic_characterization/theiacov/#assembly-tasks","title":"Assembly tasks","text":"Either one of these tasks is run depending on the organism and workflow type.
ivar_consensus: Alignment, Consensus, Variant Detection, and Assembly Statistics for non-flu organisms in Illumina workflows ivar_consensus is a sub-workflow within TheiaCoV that performs reference-based consensus assembly using the iVar tool by Nathan Grubaugh from the Andersen lab.
The following steps are performed as part of this sub-workflow:
trim_primers is set to true, primers will be removed using ivar trim.ivar consensus command is run to generate a consensus assembly.iVar Consensus Technical Details
Workflow TheiaCoV_Illumina_PE & TheiaCoV_Illumina_SE Sub-workflow wf_ivar_consensus.wdl Tasks task_bwa.wdltask_ivar_primer_trim.wdltask_assembly_metrics.wdltask_ivar_variant_call.wdltask_ivar_consensus.wdl Software Source Code BWA on GitHub, iVar on GitHub Software Documentation BWA on SourceForge, iVar on GitHub Original Publication(s) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEMAn amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVarartic_consensus: Alignment, Primer Trimming, Variant Detection, and Consensus for non-flu organisms in ONT & ClearLabs workflows Briefly, input reads are aligned to the appropriate reference with\u00a0minimap2\u00a0to generate a Binary Alignment Mapping (BAM) file. Primer sequences are then removed from the BAM file and a consensus assembly file is generated using the\u00a0Artic minion Medaka argument.
Read-trimming is performed on raw read data generated on the ClearLabs instrument and thus not a required step in the TheiaCoV_ClearLabs workflow.
Availablemedaka models The medaka models available in the default docker container are as follows:
r103_fast_g507, r103_fast_snp_g507, r103_fast_variant_g507, r103_hac_g507,\nr103_hac_snp_g507, r103_hac_variant_g507, r103_min_high_g345, r103_min_high_g360,\nr103_prom_high_g360, r103_prom_snp_g3210, r103_prom_variant_g3210, r103_sup_g507,\nr103_sup_snp_g507, r103_sup_variant_g507, r1041_e82_260bps_fast_g632,\nr1041_e82_260bps_fast_variant_g632, r1041_e82_260bps_hac_g632,\nr1041_e82_260bps_hac_v4.0.0, r1041_e82_260bps_hac_v4.1.0,\nr1041_e82_260bps_hac_variant_g632, r1041_e82_260bps_hac_variant_v4.1.0,\nr1041_e82_260bps_joint_apk_ulk_v5.0.0, r1041_e82_260bps_sup_g632,\nr1041_e82_260bps_sup_v4.0.0, r1041_e82_260bps_sup_v4.1.0,\nr1041_e82_260bps_sup_variant_g632, r1041_e82_260bps_sup_variant_v4.1.0,\nr1041_e82_400bps_fast_g615, r1041_e82_400bps_fast_g632,\nr1041_e82_400bps_fast_variant_g615, r1041_e82_400bps_fast_variant_g632,\nr1041_e82_400bps_hac_g615, r1041_e82_400bps_hac_g632, r1041_e82_400bps_hac_v4.0.0,\nr1041_e82_400bps_hac_v4.1.0, r1041_e82_400bps_hac_v4.2.0, r1041_e82_400bps_hac_v4.3.0,\nr1041_e82_400bps_hac_v5.0.0, r1041_e82_400bps_hac_variant_g615,\nr1041_e82_400bps_hac_variant_g632, r1041_e82_400bps_hac_variant_v4.1.0,\nr1041_e82_400bps_hac_variant_v4.2.0, r1041_e82_400bps_hac_variant_v4.3.0,\nr1041_e82_400bps_hac_variant_v5.0.0, r1041_e82_400bps_sup_g615,\nr1041_e82_400bps_sup_v4.0.0, r1041_e82_400bps_sup_v4.1.0, r1041_e82_400bps_sup_v4.2.0,\nr1041_e82_400bps_sup_v4.3.0, r1041_e82_400bps_sup_v5.0.0,\nr1041_e82_400bps_sup_variant_g615, r1041_e82_400bps_sup_variant_v4.1.0,\nr1041_e82_400bps_sup_variant_v4.2.0, r1041_e82_400bps_sup_variant_v4.3.0,\nr1041_e82_400bps_sup_variant_v5.0.0, r104_e81_fast_g5015, r104_e81_fast_variant_g5015,\nr104_e81_hac_g5015, r104_e81_hac_variant_g5015, r104_e81_sup_g5015, r104_e81_sup_g610,\nr104_e81_sup_variant_g610, r10_min_high_g303, r10_min_high_g340, r941_e81_fast_g514,\nr941_e81_fast_variant_g514, r941_e81_hac_g514, r941_e81_hac_variant_g514,\nr941_e81_sup_g514, r941_e81_sup_variant_g514, r941_min_fast_g303, r941_min_fast_g507,\nr941_min_fast_snp_g507, r941_min_fast_variant_g507, r941_min_hac_g507,\nr941_min_hac_snp_g507, r941_min_hac_variant_g507, r941_min_high_g303, r941_min_high_g330,\nr941_min_high_g340_rle, r941_min_high_g344, r941_min_high_g351, r941_min_high_g360,\nr941_min_sup_g507, r941_min_sup_snp_g507, r941_min_sup_variant_g507, r941_prom_fast_g303,\nr941_prom_fast_g507, r941_prom_fast_snp_g507, r941_prom_fast_variant_g507,\nr941_prom_hac_g507, r941_prom_hac_snp_g507, r941_prom_hac_variant_g507,\nr941_prom_high_g303, r941_prom_high_g330, r941_prom_high_g344, r941_prom_high_g360,\nr941_prom_high_g4011, r941_prom_snp_g303, r941_prom_snp_g322, r941_prom_snp_g360,\nr941_prom_sup_g507, r941_prom_sup_snp_g507, r941_prom_sup_variant_g507,\nr941_prom_variant_g303, r941_prom_variant_g322, r941_prom_variant_g360,\nr941_sup_plant_g610, r941_sup_plant_variant_g610\nGeneral statistics about the assembly are generated with the consensus_qc task (task_assembly_metrics.wdl).
Artic Consensus Technical Details
Links Task task_artic_consensus.wdl Software Source Code Artic on GitHub Software Documentation Artic pipelineirma: Assembly and Characterization for flu in TheiaCoV_Illumina_PE & TheiaCoV_ONT Cleaned reads are assembled using irma which does not use a reference due to the rapid evolution and high variability of influenza. Assemblies produced by irma will be orderd from largest to smallest assembled flu segment. irma also performs typing and subtyping as part of the assembly process.
General statistics about the assembly are generated with the consensus_qc task (task_assembly_metrics.wdl).
IRMA Technical Details
Links Task task_irma.wdl Software Documentation IRMA website Original Publication(s) Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler"},{"location":"workflows/genomic_characterization/theiacov/#org-specific-tasks","title":"Organism-specific characterization tasks","text":"The following tasks only run for the appropriate organism designation. The following table illustrates which characterization tools are run for the indicated organism.
SARS-CoV-2 MPXV HIV WNV Influenza RSV-A RSV-B Pangolin \u2705 \u274c \u274c \u274c \u274c \u274c \u274c Nextclade \u2705 \u2705 \u274c \u274c \u2705 \u2705 \u2705 VADR \u2705 \u2705 \u274c \u2705 \u2705 \u2705 \u2705 Quasitools HyDRA \u274c \u274c \u2705 \u274c \u274c \u274c \u274c IRMA \u274c \u274c \u274c \u274c \u2705 \u274c \u274c Abricate \u274c \u274c \u274c \u274c \u2705 \u274c \u274c % Gene Coverage \u2705 \u2705 \u274c \u274c \u274c \u274c \u274c Antiviral Detection \u274c \u274c \u274c \u274c \u2705 \u274c \u274c GenoFLU \u274c \u274c \u274c \u274c \u2705 \u274c \u274cpangolin Pangolin designates SARS-CoV-2 lineage assignments.
Pangolin Technical Details
Links Task task_pangolin.wdl Software Source Code Pangolin on GitHub Software Documentation Pangolin website Original Publication(s) A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiologynextclade \"Nextclade is an open-source project for viral genome alignment, mutation calling, clade assignment, quality checks and phylogenetic placement.\"
Nextclade Technical Details
Links Task task_nextclade.wdl Software Source Code https://github.com/nextstrain/nextclade Software Documentation Nextclade Original Publication(s) Nextclade: clade assignment, mutation calling and quality control for viral genomes.vadr VADR annotates and validates completed assembly files.
VADR Technical Details
Links Task task_vadr.wdl Software Source Code https://github.com/ncbi/vadr Software Documentation https://github.com/ncbi/vadr/wiki Original Publication(s) For SARS-CoV-2: Faster SARS-CoV-2 sequence validation and annotation for GenBank using VADR For non-SARS_CoV-2: VADR: validation and annotation of virus sequence submissions to GenBankquasitools quasitools performs genome characterization for HIV.
Quasitools Technical Details
Links Task task_quasitools.wdl Software Source Code https://github.com/phac-nml/quasitools/ Software Documentation Quasitools HyDRAirma IRMA assigns types and subtype/lineages in addition to performing assembly of flu genomes. Please see the section above under \"Assembly tasks\" to find more information regarding this tool.
IRMA Technical Details
Links Task task_irma.wdl Software Documentation IRMA website Original Publication(s) Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assemblerabricate Abricate assigns types and subtype/lineages for flu samples
Abricate Technical Details
Links Task task_abricate.wdl (abricate_flu subtask) Software Source Code ABRicate on GitHub Software Documentation ABRicate on GitHubgene_coverage This task calculates the percent of the gene covered above a minimum depth. By default, it runs for SARS-CoV-2 and MPXV, but if a bed file is provided with regions of interest, this task will be run for other organisms as well.
Gene Coverage Technical Details
Links Task task_gene_coverage.wdlflu_antiviral_substitutions This sub-workflow determines which, if any, antiviral mutations are present in the sample.
The assembled HA, NA, PA, PB1 and PB2 segments are compared against a list of known amino-acid substitutions associated with resistance to the antivirals A_315675, compound_367, Favipiravir, Fludase, L_742_001, Laninamivir, Oseltamivir (tamiflu), Peramivir, Pimodivir, Xofluza, and Zanamivir. The list of known antiviral amino acid substitutions can be expanded via optional user input antiviral_aa_subs in the format \"NA:V95A,HA:I97V\", i.e. Protein:AAPositionAA.
Antiviral Substitutions Technical Details
Links Workflow wf_influenza_antiviral_substitutions.wdlgenoflu This sub-workflow determines the whole-genome genotype of an H5N1 flu sample.
GenoFLU Technical Details
Links Task task_genoflu.wdl Software Source Code GenoFLU on GitHub"},{"location":"workflows/genomic_characterization/theiacov/#outputs","title":"Outputs","text":"All TheiaCoV Workflows (not TheiaCoV_FASTA_Batch)
Variable Type Description Workflow abricate_flu_database String ABRicate database used for analysis FASTA, ONT, PE abricate_flu_results File File containing all results from ABRicate FASTA, ONT, PE abricate_flu_subtype String Flu subtype as determined by ABRicate FASTA, ONT, PE abricate_flu_type String Flu type as determined by ABRicate FASTA, ONT, PE abricate_flu_version String Version of ABRicate FASTA, ONT, PE aligned_bai File Index companion file to the bam file generated during the consensus assembly process CL, ONT, PE, SE aligned_bam File Primer-trimmed BAM file; generated during consensus assembly process CL, ONT, PE, SE artic_docker String Docker image utilized for read trimming and consensus genome assembly CL, ONT artic_version String Version of the Artic software utilized for read trimming and conesnsus genome assembly CL, ONT assembly_fasta File Consensus genome assembly; for lower quality flu samples, the output may state \"Assembly could not be generated\" when there is too little and/or too low quality data for IRMA to produce an assembly. Contigs will be ordered from largest to smallest when IRMA is used. CL, ONT, PE, SE assembly_length_unambiguous Int Number of unambiguous basecalls within the consensus assembly CL, FASTA, ONT, PE, SE assembly_mean_coverage Float Mean sequencing depth throughout the consensus assembly. Generated after performing primer trimming and calculated using the SAMtools coverage command CL, ONT, PE, SE assembly_method String Method employed to generate consensus assembly CL, FASTA, ONT, PE, SE auspice_json File Auspice-compatable JSON output generated from Nextclade analysis that includes the Nextclade default samples for clade-typing and the single sample placed on this tree CL, FASTA, ONT, PE, SE auspice_json_flu_ha File Auspice-compatable JSON output generated from Nextclade analysis on Influenza HA segment that includes the Nextclade default samples for clade-typing and the single sample placed on this tree ONT, PE auspice_json_flu_na File Auspice-compatable JSON output generated from Nextclade analysis on Influenza NA segment that includes the Nextclade default samples for clade-typing and the single sample placed on this tree ONT, PE bbduk_docker String Docker image used to run BBDuk PE, SE bwa_version String Version of BWA used to map read data to the reference genome PE, SE consensus_flagstat File Output from the SAMtools flagstat command to assess quality of the alignment file (BAM) CL, ONT, PE, SE consensus_n_variant_min_depth Int Minimum read depth to call variants for iVar consensus and iVar variants PE, SE consensus_stats File Output from the SAMtools stats command to assess quality of the alignment file (BAM) CL, ONT, PE, SE est_coverage_clean Float Estimated coverage of the clean reads ONT est_coverage_raw Float Estimated coverage of the raw reads ONT est_percent_gene_coverage_tsv File Percent coverage for each gene in the organism being analyzed (depending on the organism input) CL, ONT, PE, SE fastp_html_report File HTML report for fastp PE, SE fastp_version String Fastp version used PE, SE fastq_scan_clean1_json File JSON file output fromfastq-scan containing summary stats about clean forward read quality and length PE, SE, CL fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length PE fastq_scan_num_reads_clean_pairs String Number of paired reads after filtering as determined by fastq_scan PE fastq_scan_num_reads_clean1 Int Number of forward reads after filtering as determined by fastq_scan CL, PE, SE fastq_scan_num_reads_clean2 Int Number of reverse reads after filtering as determined by fastq_scan PE fastq_scan_num_reads_raw_pairs String Number of paired reads identified in the input fastq files as determined by fastq_scan PE fastq_scan_num_reads_raw1 Int Number of forward reads identified in the input fastq files as determined by fastq_scan CL, PE, SE fastq_scan_num_reads_raw2 Int Number of reverse reads identified in the input fastq files as determined by fastq_scan PE fastq_scan_r1_mean_q_clean Float Forward read mean quality value after quality trimming and adapter removal fastq_scan_r1_mean_q_raw Float Forward read mean quality value before quality trimming and adapter removal fastq_scan_r1_mean_readlength_clean Float Forward read mean read length value after quality trimming and adapter removal fastq_scan_r1_mean_readlength_raw Float Forward read mean read length value before quality trimming and adapter removal fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length PE, SE, CL fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length PE fastq_scan_version String Version of fastq_scan used for read QC analysis CL, PE, SE fastqc_clean1_html File Graphical visualization of clean forward read quality from fastqc to open in an internet browser PE, SE fastqc_clean2_html File Graphical visualization of clean reverse read quality from fastqc to open in an internet browser PE fastqc_docker String Docker container used for fastqc PE, SE fastqc_num_reads_clean_pairs String Number of read pairs after cleaning by fastqc PE fastqc_num_reads_clean1 Int Number of forward reads after cleaning by fastqc PE, SE fastqc_num_reads_clean2 Int Number of reverse reads after cleaning by fastqc PE fastqc_num_reads_raw_pairs Int Number of raw read pairs as computed by fastqc PE fastqc_num_reads_raw1 Int Number of raw forward/facing reads as computed by fastqc PE, SE fastqc_num_reads_raw2 Int Number of raw reverse-facing reads as computed by fastqc PE fastqc_raw1_html File Graphical visualization of raw forward read quality from fastqc to open in an internet browser PE, SE fastqc_raw2_html File Graphical visualization of raw reverse read quality from fastqc to open in an internet browser PE fastqc_version String Version of fastqc software used PE, SE flu_A_315675_resistance String resistance mutations to A_315675 ONT, PE flu_amantadine_resistance String resistance mutations to amantadine ONT, PE flu_compound_367_resistance String resistance mutations to compound_367 ONT, PE flu_favipiravir_resistance String resistance mutations to favipiravir ONT, PE flu_fludase_resistance String resistance mutations to fludase ONT, PE flu_L_742_001_resistance String resistance mutations to L_742_001 ONT, PE flu_laninamivir_resistance String resistance mutations to laninamivir ONT, PE flu_oseltamivir_resistance String resistance mutations to oseltamivir (Tamiflu\u00ae) ONT, PE flu_peramivir_resistance String resistance mutations to peramivir (Rapivab\u00ae) ONT, PE flu_pimodivir_resistance String resistance mutations to pimodivir ONT, PE flu_rimantadine_resistance String resistance mutations to rimantadine ONT, PE flu_xofluza_resistance String resistance mutations to xofluza (Baloxavir marboxil) ONT, PE flu_zanamivir_resistance String resistance mutations to zanamivir (Relenza\u00ae) ONT, PE genoflu_all_segments String The genotypes for each individual flu segment FASTA, ONT, PE genoflu_genotype String The genotype of the whole genome, based off of the individual segments types FASTA, ONT, PE genoflu_output_tsv File The output file from GenoFLU FASTA, ONT, PE genoflu_version String The version of GenoFLU used FASTA, ONT, PE irma_docker String Docker image used to run IRMA ONT, PE irma_ha_segment_fasta File HA (Haemagglutinin) assembly fasta file ONT, PE irma_mp_segment_fasta File MP (Matrix Protein) assembly fasta file ONT, PE irma_na_segment_fasta File NA (Neuraminidase) assembly fasta file ONT, PE irma_np_segment_fasta File NP (Nucleoprotein) assembly fasta file ONT, PE irma_ns_segment_fasta File NS (Nonstructural) assembly fasta file ONT, PE irma_pa_segment_fasta File PA (Polymerase acidic) assembly fasta file ONT, PE irma_pb1_segment_fasta File PB1 (Polymerase basic 1) assembly fasta file ONT, PE irma_pb2_segment_fasta File PB2 (Polymerase basic 2) assembly fasta file ONT, PE irma_subtype String Flu subtype as determined by IRMA ONT, PE irma_subtype_notes String Helpful note to user about Flu B subtypes. Output will be blank for Flu A samples. For Flu B samples it will state: \"IRMA does not differentiate Victoria and Yamagata Flu B lineages. See abricate_flu_subtype output column\" ONT, PE irma_type String Flu type as determined by IRMA ONT, PE irma_version String Version of IRMA used ONT, PE ivar_tsv File Variant descriptor file generated by iVar variants PE, SE ivar_variant_proportion_intermediate String The proportion of variants of intermediate frequency PE, SE ivar_variant_version String Version of iVar for running the iVar variants command PE, SE ivar_vcf File iVar tsv output converted to VCF format PE, SE ivar_version_consensus String Version of iVar for running the iVar consensus command PE, SE ivar_version_primtrim String Version of iVar for running the iVar trim command PE, SE kraken_human Float Percent of human read data detected using the Kraken2 software CL, ONT, PE, SE kraken_human_dehosted Float Percent of human read data detected using the Kraken2 software after host removal CL, ONT, PE kraken_report File Full Kraken report CL, ONT, PE, SE kraken_report_dehosted File Full Kraken report after host removal CL, ONT, PE kraken_sc2 String Percent of SARS-CoV-2 read data detected using the Kraken2 software CL, ONT, PE, SE kraken_sc2_dehosted String Percent of SARS-CoV-2 read data detected using the Kraken2 software after host removal CL, ONT, PE kraken_target_organism String Percent of target organism read data detected using the Kraken2 software CL, ONT, PE, SE kraken_target_organism_dehosted String Percent of target organism read data detected using the Kraken2 software after host removal CL, ONT, PE kraken_target_organism_name String The name of the target organism; e.g., \"Monkeypox\" or \"Human immunodeficiency virus\" CL, ONT, PE, SE kraken_version String Version of Kraken software used CL, ONT, PE, SE meanbaseq_trim Float Mean quality of the nucleotide basecalls aligned to the reference genome after primer trimming CL, ONT, PE, SE meanmapq_trim Float Mean quality of the mapped reads to the reference genome after primer trimming CL, ONT, PE, SE medaka_reference String Reference sequence used in medaka task CL, ONT medaka_vcf File A VCF file containing the identified variants ONT nanoplot_docker String Docker image used to run Nanoplot ONT nanoplot_html_clean File An HTML report describing the clean reads ONT nanoplot_html_raw File An HTML report describing the raw reads ONT nanoplot_num_reads_clean1 Float Number of clean reads ONT nanoplot_num_reads_raw1 Float Number of raw reads ONT nanoplot_r1_est_coverage_clean Float Estimated coverage on the clean reads by nanoplot ONT nanoplot_r1_est_coverage_raw Float Estimated coverage on the raw reads by nanoplot ONT nanoplot_r1_mean_q_clean Float Mean quality score of clean forward reads ONT nanoplot_r1_mean_q_raw Float Mean quality score of raw forward reads ONT nanoplot_r1_mean_readlength_clean Float Mean read length of clean forward reads ONT nanoplot_r1_mean_readlength_raw Float Mean read length of raw forward reads ONT nanoplot_r1_median_q_clean Float Median quality score of clean forward reads ONT nanoplot_r1_median_q_raw Float Median quality score of raw forward reads ONT nanoplot_r1_median_readlength_clean Float Median read length of clean forward reads ONT nanoplot_r1_median_readlength_raw Float Median read length of raw forward reads ONT nanoplot_r1_n50_clean Float N50 of clean forward reads ONT nanoplot_r1_n50_raw Float N50 of raw forward reads ONT nanoplot_r1_stdev_readlength_clean Float Standard deviation read length of clean forward reads ONT nanoplot_r1_stdev_readlength_raw Float Standard deviation read length of raw forward reads ONT nanoplot_tsv_clean File A TSV report describing the clean reads ONT nanoplot_tsv_raw File A TSV report describing the raw reads ONT nanoplot_version String Version of nanoplot tool used ONT nextclade_aa_dels String Amino-acid deletions as detected by NextClade. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_aa_dels_flu_ha String Amino-acid deletions as detected by NextClade. Specific to flu; it includes deletions for HA segment ONT, PE nextclade_aa_dels_flu_na String Amino-acid deletions as detected by NextClade. Specific to Flu; it includes deletions for NA segment ONT, PE nextclade_aa_subs String Amino-acid substitutions as detected by Nextclade. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_aa_subs_flu_ha String Amino-acid substitutions as detected by Nextclade. Specific to Flu; it includes substitutions for NA segment ONT, PE nextclade_aa_subs_flu_na String Amino-acid substitutions as detected by Nextclade. Specific to Flu; it includes substitutions for NA segment ONT, PE nextclade_clade String Nextclade clade designation, will be blank for Flu. CL, FASTA, ONT, PE, SE nextclade_clade_flu_ha String Nextclade clade designation, specific to Flu NA segment ONT, PE nextclade_clade_flu_na String Nextclade clade designation, specific to Flu HA segment ONT, PE nextclade_docker String Docker image used to run Nextclade CL, FASTA, ONT, PE, SE nextclade_ds_tag String Dataset tag used to run Nextclade. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_ds_tag_flu_ha String Dataset tag used to run Nextclade, specific to Flu HA segment ONT, PE nextclade_ds_tag_flu_na String Dataset tag used to run Nextclade, specific to Flu NA segment ONT, PE nextclade_json File Nextclade output in JSON file format. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_json_flu_ha File Nextclade output in JSON file format, specific to Flu HA segment ONT, PE nextclade_json_flu_na File Nextclade output in JSON file format, specific to Flu NA segment ONT, PE nextclade_lineage String Nextclade lineage designation CL, FASTA, ONT, PE, SE nextclade_qc String QC metric as determined by Nextclade. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_qc_flu_ha String QC metric as determined by Nextclade, specific to Flu HA segment ONT, PE nextclade_qc_flu_na String QC metric as determined by Nextclade, specific to Flu NA segment ONT, PE nextclade_tsv File Nextclade output in TSV file format. Will be blank for Flu CL, FASTA, ONT, PE, SE nextclade_tsv_flu_ha File Nextclade output in TSV file format, specific to Flu HA segment ONT, PE nextclade_tsv_flu_na File Nextclade output in TSV file format, specific to Flu NA segment ONT, PE nextclade_version String The version of Nextclade software used CL, FASTA, ONT, PE, SE number_Degenerate Int Number of degenerate basecalls within the consensus assembly CL, FASTA, ONT, PE, SE number_N Int Number of fully ambiguous basecalls within the consensus assembly CL, FASTA, ONT, PE, SE number_Total Int Total number of nucleotides within the consensus assembly CL, FASTA, ONT, PE, SE pango_lineage String Pango lineage as determined by Pangolin CL, FASTA, ONT, PE, SE pango_lineage_expanded String Pango lineage without use of aliases; e.g., \"BA.1\" \u2192 \"B.1.1.529.1\" CL, FASTA, ONT, PE, SE pango_lineage_report File Full Pango lineage report generated by Pangolin CL, FASTA, ONT, PE, SE pangolin_assignment_version String The version of the pangolin software (e.g. PANGO or PUSHER) used for lineage assignment CL, FASTA, ONT, PE, SE pangolin_conflicts String Number of lineage conflicts as determined by Pangolin CL, FASTA, ONT, PE, SE pangolin_docker String Docker image used to run Pangolin CL, FASTA, ONT, PE, SE pangolin_notes String Lineage notes as determined by Pangolin CL, FASTA, ONT, PE, SE pangolin_versions String All Pangolin software and database versions CL, FASTA, ONT, PE, SE percent_reference_coverage Float Percent coverage of the reference genome after performing primer trimming; calculated as assembly_length_unambiguous / length of the reference genome (SC2: 29903) x 100 CL, FASTA, ONT, PE, SE percentage_mapped_reads String Percentage of reads that successfully aligned to the reference genome. This value is calculated by number of mapped reads / total number of reads x 100. ONT, PE, SE primer_bed_name String Name of the primer bed files used for primer trimming CL, ONT, PE, SE primer_trimmed_read_percent Float Percentage of read data with primers trimmed as determined by iVar trim PE, SE qc_check String The results of the QC Check task CL, FASTA, ONT, PE, SE qc_standard File The file used in the QC Check task containing the QC thresholds. CL, FASTA, ONT, PE, SE quasitools_coverage_file File The coverage report created by Quasitools HyDRA ONT, PE quasitools_date String Date of Quasitools analysis ONT, PE quasitools_dr_report File Drug resistance report created by Quasitools HyDRA ONT, PE quasitools_hydra_vcf File The VCF created by Quasitools HyDRA ONT, PE quasitools_mutations_report File The mutation report created by Quasitools HyDRA ONT, PE quasitools_version String Version of Quasitools used ONT, PE read_screen_clean String A PASS or FAIL flag for input reads after cleaning ONT, PE, SE read_screen_raw String A PASS or FAIL flag for input reads ONT, PE, SE read1_aligned File Forward read file of only aligned reads CL, ONT, PE, SE read1_clean File Forward read file after quality trimming and adapter removal PE, SE read1_dehosted File Dehosted forward reads; suggested read file for SRA submission CL, ONT, PE read1_trimmed File Forward read file after quality trimming and adapter removal ONT read1_unaligned File Forward read file of unaligned reads PE, SE read2_aligned File Reverse read file of only aligned reads PE read2_clean File Reverse read file after quality trimming and adapter removal PE read2_dehosted File Dehosted reverse reads; suggested read file for SRA submission PE read2_unaligned File Reverse read file of unaligned reads PE samtools_version String The version of SAMtools used to sort and index the alignment file ONT, PE, SE samtools_version_consensus String The version of SAMtools used to create the pileup before running iVar consensus PE, SE samtools_version_primtrim String The version of SAMtools used to create the pileup before running iVar trim PE, SE samtools_version_stats String The version of SAMtools used to assess the quality of read mapping CL, PE, SE sc2_s_gene_mean_coverage Float Mean read depth for the S gene in SARS-CoV-2 CL, ONT, PE, SE sc2_s_gene_percent_coverage Float Percent coverage of the S gene in SARS-CoV-2 CL, ONT, PE, SE seq_platform String Description of the sequencing methodology used to generate the input read data CL, FASTA, ONT, PE, SE sorted_bam_unaligned File A BAM file that only contains reads that did not align to the reference PE, SE sorted_bam_unaligned_bai File Index companion file to a BAM file that only contains reads that did not align to the reference PE, SE theiacov_clearlabs_analysis_date String Date of analysis CL theiacov_clearlabs_version String Version of PHB used for running the workflow CL theiacov_fasta_analysis_date String Date of analysis FASTA theiacov_fasta_version String Version of PHB used for running the workflow FASTA theiacov_illumina_pe_analysis_date String Date of analysis PE theiacov_illumina_pe_version String Version of PHB used for running the workflow PE theiacov_illumina_se_analysis_date String Date of analysis SE theiacov_illumina_se_version String Version of PHB used for running the workflow SE theiacov_ont_analysis_date String Date of analysis ONT theiacov_ont_version String Version of PHB used for running the workflow ONT trimmomatic_docker String Docker container used with trimmomatic PE, SE trimmomatic_version String The version of Trimmomatic used PE, SE vadr_alerts_list File A file containing all of the fatal alerts as determined by VADR CL, FASTA, ONT, PE, SE vadr_all_outputs_tar_gz File A .tar.gz file (gzip-compressed tar archive file) containing all outputs from the VADR command v-annotate.pl. This file must be uncompressed & extracted to see the many files within. See https://github.com/ncbi/vadr/blob/master/documentation/formats.md#format-of-v-annotatepl-output-filesfor more complete description of all files present within the archive. Useful when deeply investigating a sample's genome & annotations. CL, FASTA, ONT, PE, SE vadr_classification_summary_file File Per-sequence tabular classification file. See https://github.com/ncbi/vadr/blob/master/documentation/formats.md#explanation-of-sqc-suffixed-output-files for more complete description. CL, FASTA, ONT, PE, SE vadr_docker String Docker image used to run VADR CL, FASTA, ONT, PE, SE vadr_fastas_zip_archive File Zip archive containing all fasta files created during VADR analysis CL, FASTA, ONT, PE, SE vadr_feature_tbl_fail File 5 column feature table output for failing sequences. See https://github.com/ncbi/vadr/blob/master/documentation/formats.md#format-of-v-annotatepl-output-files for more complete description. CL, FASTA, ONT, PE, SE vadr_feature_tbl_pass File 5 column feature table output for passing sequences. See https://github.com/ncbi/vadr/blob/master/documentation/formats.md#format-of-v-annotatepl-output-files for more complete description. CL, FASTA, ONT, PE, SE vadr_num_alerts String Number of fatal alerts as determined by VADR CL, FASTA, ONT, PE, SE variants_from_ref_vcf File Number of variants relative to the reference genome CL TheiaCoV_FASTA_Batch_PHB Outputs"},{"location":"workflows/genomic_characterization/theiacov/#theiacov-fasta-batch-outputs","title":"TheiaCoV_FASTA_Batch Outputs","text":"Overwrite Warning
TheiaCoV_FASTA_Batch_PHB workflow will output results to the set-level data table in addition to overwriting the Pangolin & Nextclade output columns in the sample-level data table. Users can view the set-level workflow output TSV file called \"Datatable\" to view exactly which columns were overwritten in the sample-level data table.
The TheiaEuk_Illumina_PE workflow is for the assembly, quality assessment, and characterization of fungal genomes. It is designed to accept Illumina paired-end sequencing data as the primary input. It is currently intended only for haploid fungal genomes like Candida auris. Analyzing diploid genomes using TheiaEuk should be attempted only with expert attention to the resulting genome quality.
All input reads are processed through \"core tasks\" in each workflow. The core tasks include raw read quality assessment, read cleaning (quality trimming and adapter removal), de novo assembly, assembly quality assessment, and species taxon identification. For some taxa identified, taxa-specific sub-workflows will be automatically activated, undertaking additional taxa-specific characterization steps, including clade-typing and/or antifungal resistance detection.
TheiaEuk Workflow Diagram
"},{"location":"workflows/genomic_characterization/theiaeuk/#inputs","title":"Inputs","text":"Input read data
The TheiaEuk_Illumina_PE workflow takes in Illumina paired-end read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip prior to Terra upload to minimize data upload time.
By default, the workflow anticipates\u00a02 x 150bp\u00a0reads (i.e. the input reads were generated using a 300-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate shorter read data, such as the 2 x 75bp reads generated using a 150-cycle sequencing kit.
All input reads are processed through \"core tasks\" in the TheiaEuk workflows. These undertake read trimming and assembly appropriate to the input data type, currently only Illumina paired-end data. TheiaEuk workflow subsequently launch default genome characterization modules for quality assessment, and additional taxa-specific characterization steps. When setting up the workflow, users may choose to use \"optional tasks\" or alternatives to tasks run in the workflow by default.
"},{"location":"workflows/genomic_characterization/theiaeuk/#core-tasks","title":"Core tasks","text":"These tasks are performed regardless of organism. They perform read trimming and various quality control steps.
versioning: Version capture for TheiaEuk The versioning task captures the workflow version from the GitHub (code repository) version.
Version Capture Technical details
Links Task task_versioning.wdlscreen: Total Raw Read Quantification and Genome Size Estimation (optional, on by default) The screen task ensures the quantity of sequence data is sufficient to undertake genomic analysis. It uses fastq-scan and bash commands for quantification of reads and base pairs, and mash sketching to estimate the genome size and its coverage. At each step, the results are assessed relative to pass/fail criteria and thresholds that may be defined by optional user inputs. Samples that do not meet these criteria will not be processed further by the workflow:
min_reads.min_proportion basepairs are in either the reads1 or read2 files.min_basepairs basepairsmin_genome_size or bigger than max_genome_size.min_coverage.Read screening is undertaken on both the raw and cleaned reads. The task may be skipped by setting the skip_screen variable to true.
Default values vary between the PE and SE workflow. The rationale for these default values can be found below.
Variable Rationaleskip_screen Prevent the read screen from running min_reads Minimum number of base pairs for 20x coverage of Hansenula polymorpha divided by 300 (longest Illumina read length) min_basepairs Greater than 10x coverage of Hansenula polymorpha min_genome_size Based on the Hansenula polymorpha genome - the smallest fungal genome as of 2015-04-02 (8.97 Mbp) max_genome_size Based on the Cenococcum geophilum genome, the biggest pathogenic fungal genome, (177.57 Mbp) min_coverage A bare-minimum coverage for genome characterization. Higher coverage would be required for high-quality phylogenetics. min_proportion Greater than 50% reads are in the read1 file; others are in the read2 file Screen Technical Details
There is a single WDL task for read screening. The screen task is run twice, once for raw reads and once for clean reads.
Rasusa: Read subsampling (optional, on by default) The Rasusa task performs subsampling of the raw reads. By default, this task will subsample reads to a depth of 150X using the estimated genome length produced during the preceding raw read screen. The user can prevent the task from being launched by setting the call_rasusavariable to false.
The user can also provide an estimated genome length for the task to use for subsampling using the genome_size variable. In addition, the read depth can be modified using the subsample_coverage variable.
Rasusa Technical Details
Links Task task_rasusa.wdl Software Source Code Rasusa on GitHub Software Documentation Rasusa on GitHub Original Publication(s) Rasusa: Randomly subsample sequencing reads to a specified coverageread_QC_trim: Read Quality Trimming, Adapter Removal, Quantification, and Identification read_QC_trim is a sub-workflow within TheiaEuk that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below.
Read quality trimming
Either trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.
If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:
Parameter Explanation -g enables polyG tail trimming -5 20 enables read end-trimming -3 20 enables read end-trimming --detect_adapter_for_pe enables adapter-trimming only for paired-end readsAdapter removal
The BBDuk task removes adapters from sequence reads. To do this:
Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.
Read Quantification
There are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. In TheiaProk_Illumina_PE, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.
Read Identification (optional)
The MIDAS task is for the identification of reads to detect contamination with non-target taxa. This task is optional and turned off by default. It can be used by setting the call_midas input variable to true.
The MIDAS tool was originally designed for metagenomic sequencing data but has been co-opted for use with bacterial isolate WGS methods. It can be used to detect contamination present in raw sequencing data by estimating bacterial species abundance in bacterial isolate WGS data. If a secondary genus is detected above a relative frequency of 0.01 (1%), then the sample should fail QC and be investigated further for potential contamination.
This task is similar to those used in commercial software, BioNumerics, for estimating secondary species abundance.
How are the MIDAS output columns determined?Example MIDAS report in the ****midas_report column:
MIDAS report column descriptions:
The value in the midas_primary_genus column is derived by ordering the rows in order of \"relative_abundance\" and identifying the genus of top species in the \"species_id\" column (Salmonella). The value in the midas_secondary_genus column is derived from the genus of the second-most prevalent genus in the \"species_id\" column (Citrobacter). The midas_secondary_genus_abundance column is the \"relative_abundance\" of the second-most prevalent genus (0.009477003). The midas_secondary_genus_coverage is the \"coverage\" of the second-most prevalent genus (0.995216227).
read_QC_trim Technical Details
Links Sub-workflow wf_read_QC_trim_pe.wdl Tasks task_fastp.wdltask_trimmomatic.wdltask_bbduk.wdltask_fastq_scan.wdltask_midas.wdltask_kraken2.wdl Software Source Code fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2 Software Documentation fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2 Original Publication(s) Trimmomatic: a flexible trimmer for Illumina sequence datafastp: an ultra-fast all-in-one FASTQ preprocessorAn integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeographyImproved metagenomic analysis with Kraken 2"},{"location":"workflows/genomic_characterization/theiaeuk/#assembly-tasks","title":"Assembly tasks","text":"These tasks assemble the reads into a de novo assembly and assess the quality of the assembly.
shovill: De novo Assembly De Novo assembly will be undertaken only for samples that have sufficient read quantity and quality, as determined by the screen task assessment of clean reads.
In TheiaEuk, assembly is performed using the Shovill pipeline. This undertakes the assembly with one of four assemblers (SKESA (default), SPAdes, Velvet, Megahit), but also performs a number of pre- and post-processing steps to improve the resulting genome assembly. Shovill uses an estimated genome size (see here). If this is not provided by the user as an optional input, Shovill will estimate the genome size using mash. Adaptor trimming can be undertaken with Shovill by setting the trim option to \"true\", but this is set to \"false\" by default as alternative adapter trimming is undertaken in the TheiaEuk workflow.
De novo assembly is the process or product of attempting to reconstruct a genome from scratch (without prior knowledge of the genome) using sequence reads. Assembly of fungal genomes from short-reads will produce multiple contigs per chromosome rather than a single contiguous sequence for each chromosome.
Shovill Technical Details
Links TheiaEuk WDL Task task_shovill.wdl Software Source Code Shovill on GitHub Software Documentation Shovill on GitHubQUAST: Assembly Quality Assessment QUAST (QUality ASsessment Tool) evaluates genome assemblies by computing several metrics that describe the assembly quality, including the total number of bases in the assembly, the length of the largest contig in the assembly, and the assembly percentage GC content.
QUAST Technical Details
Links Task task_quast.wdl Software Source Code QUAST on GitHub Software Documentation https://quast.sourceforge.net/docs/manual.html Orginal publication QUAST: quality assessment tool for genome assembliesCG-Pipeline: Assessment of Read Quality, and Estimation of Genome Coverage Thecg_pipeline task generates metrics about read quality and estimates the coverage of the genome using the \"run_assembly_readMetrics.pl\" script from CG-Pipeline. The genome coverage estimates are calculated using both using raw and cleaned reads, using either a user-provided genome_size or the estimated genome length generated by QUAST.
CG-Pipeline Technical Details
The cg_pipeline task is run twice in TheiaEuk, once with raw reads, and once with clean reads.
These tasks are performed regardless of the organism and provide quality control and taxonomic assignment.
GAMBIT: Taxon Assignment GAMBIT determines the taxon of the genome assembly using a k-mer based approach to match the assembly sequence to the closest complete genome in a database, thereby predicting its identity. Sometimes, GAMBIT can confidently designate the organism to the species level. Other times, it is more conservative and assigns it to a higher taxonomic rank.
For additional details regarding the GAMBIT tool and a list of available GAMBIT databases for analysis, please consult the GAMBIT tool documentation.
GAMBIT Technical Details
Links Task task_gambit.wdl Software Source Code GAMBIT on GitHub Software Documentation GAMBIT ReadTheDocs Original Publication(s) GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identificationBUSCO: Assembly Quality Assessment BUSCO (Benchmarking Universal Single-Copy Orthologue) attempts to quantify the completeness and contamination of an assembly to generate quality assessment metrics. It uses taxa-specific databases containing genes that are all expected to occur in the given taxa, each in a single copy. BUSCO examines the presence or absence of these genes, whether they are fragmented, and whether they are duplicated (suggestive that additional copies came from contaminants).
BUSCO notation
Here is an example of BUSCO notation: C:99.1%[S:98.9%,D:0.2%],F:0.0%,M:0.9%,n:440. There are several abbreviations used in this output:
A high equity assembly will use the appropriate database for the taxa, have high complete (C) and single-copy (S) percentages, and low duplicated (D), fragmented (F) and missing (M) percentages.
BUSCO Technical Details
Links Task task_busco.wdl Software Source Code BUSCO on GitLab Software Documentation https://busco.ezlab.org/ Orginal publication BUSCO: assessing genome assembly and annotation completeness with single-copy orthologsQC_check: Check QC Metrics Against User-Defined Thresholds (optional) The qc_check task compares generated QC metrics against user-defined thresholds for each metric. This task will run if the user provides a qc_check_table .tsv file. If all QC metrics meet the threshold, the qc_check output variable will read QC_PASS. Otherwise, the output will read QC_NA if the task could not proceed or QC_ALERT followed by a string indicating what metric failed.
The qc_check task applies quality thresholds according to the sample taxa. The sample taxa is taken from the gambit_predicted_taxon value inferred by the GAMBIT module OR can be manually provided by the user using the expected_taxon workflow input.
TheiaEuk_Illumina_PE_PHB: theiaeuk_qc_check_template.tsv
Example Purposes Only
QC threshold values shown are for example purposes only and should not be presumed to be sufficient for every dataset.
QC_Check Technical Details
Links Task task_qc_check_phb.wdl"},{"location":"workflows/genomic_characterization/theiaeuk/#organism-specific-characterization","title":"Organism-specific characterization","text":"The TheiaEuk workflow automatically activates taxa-specific tasks after identification of the relevant taxa using GAMBIT. Many of these taxa-specific tasks do not require any additional inputs from the user.
Two tools are deployed when Candida auris is identified.
Cladetyping: clade determinationGAMBIT is used to determine the clade of the specimen by comparing the sequence to five clade-specific reference files. The output of the clade typing task will be used to specify the reference genome for the antifungal resistance detection tool.
Default reference genomes used for clade typing and antimicrobial resistance gene detection of C. auris Clade Genome Accession Assembly Name Strain NCBI Submitter Included mutations in AMR genes (not comprehensive) Candida auris Clade I GCA_002759435.2 Cand_auris_B8441_V2 B8441 Centers for Disease Control and Prevention Candida auris Clade II GCA_003013715.2 ASM301371v2 B11220 Centers for Disease Control and Prevention Candida auris Clade III GCA_002775015.1 Cand_auris_B11221_V1 B11221 Centers for Disease Control and Prevention ERG11 V125A/F126L Candida auris Clade IV GCA_003014415.1 Cand_auris_B11243 B11243 Centers for Disease Control and Prevention ERG11 Y132F Candida auris Clade V GCA_016809505.1 ASM1680950v1 IFRC2087 Centers for Disease Control and PreventionCladetyping Technical Details
Links Task task_cauris_cladetyping.wdl Software Source Code GAMBIT on GitHub Software Documentation GAMBIT Overview Original Publication(s) GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification TheiaEuk: a species-agnostic bioinformatics workflow for fungal genomic characterization Snippy Variants: antifungal resistance detectionTo detect mutations that may confer antifungal resistance, Snippy is used to find all variants relative to the clade-specific reference, then these variants are queried for product names associated with resistance.
The genes in which there are known resistance-conferring mutations for this pathogen are:
We query Snippy results to see if any mutations were identified in those genes. By default, we automatically check for the following loci (which can be overwritten by the user). You will find the mutations next to the locus tag in the theiaeuk_snippy_variants_hits column corresponding gene name (see below):
For example, one sample may have the following output for the theiaeuk_snippy_variants_hits column:
lanosterol.14-alpha.demethylase: lanosterol 14-alpha demethylase (missense_variant c.428A>G p.Lys143Arg; C:266 T:0),B9J08_000401: hypothetical protein (stop_gained c.424C>T p.Gln142*; A:70 G:0)\nBased on this, we can tell that ERG11 has a missense variant at position 143 (Lysine to Arginine) and B9J08_000401 (which is FLO8) has a stop-gained variant at position 142 (Glutamine to Stop).
Known resistance-conferring mutations for Candida aurisMutations in these genes that are known to confer resistance are shown below
Organism Found in Gene name Gene locus AA mutation Drug Reference Candida auris Human ERG11 Y132F Fluconazole Simultaneous Emergence of Multidrug-Resistant Candida auris on 3 Continents Confirmed by Whole-Genome Sequencing and Epidemiological Analyses Candida auris Human ERG11 K143R Fluconazole Simultaneous Emergence of Multidrug-Resistant Candida auris on 3 Continents Confirmed by Whole-Genome Sequencing and Epidemiological Analyses Candida auris Human ERG11 F126T Fluconazole Simultaneous Emergence of Multidrug-Resistant Candida auris on 3 Continents Confirmed by Whole-Genome Sequencing and Epidemiological Analyses Candida auris Human FKS1 S639P Micafungin Activity of CD101, a long-acting echinocandin, against clinical isolates of Candida auris Candida auris Human FKS1 S639P Caspofungin Activity of CD101, a long-acting echinocandin, against clinical isolates of Candida auris Candida auris Human FKS1 S639P Anidulafungin Activity of CD101, a long-acting echinocandin, against clinical isolates of Candida auris Candida auris Human FKS1 S639F Micafungin A multicentre study of antifungal susceptibility patterns among 350 Candida auris isolates (2009\u201317) in India: role of the ERG11 and FKS1 genes in azole and echinocandin resistance Candida auris Human FKS1 S639F Caspofungin A multicentre study of antifungal susceptibility patterns among 350 Candida auris isolates (2009\u201317) in India: role of the ERG11 and FKS1 genes in azole and echinocandin resistance Candida auris Human FKS1 S639F Anidulafungin A multicentre study of antifungal susceptibility patterns among 350 Candida auris isolates (2009\u201317) in India: role of the ERG11 and FKS1 genes in azole and echinocandin resistance Candida auris Human FUR1 CAMJ_004922 F211I 5-flucytosine Genomic epidemiology of the UK outbreak of the emerging human fungal pathogen Candida aurisSnippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub Candida albicansWhen this species is detected by the taxon ID tool, an antifungal resistance detection task is deployed.
Snippy Variants: antifungal resistance detectionTo detect mutations that may confer antifungal resistance, Snippy is used to find all variants relative to the clade-specific reference, and these variants are queried for product names associated with resistance.
The genes in which there are known resistance-conferring mutations for this pathogen are:
We query Snippy results to see if any mutations were identified in those genes. By default, we automatically check for the following loci (which can be overwritten by the user). You will find the mutations next to the locus tag in the theiaeuk_snippy_variants_hits column corresponding gene name (see below):
Snippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub Aspergillus fumigatusWhen this species is detected by the taxon ID tool an antifungal resistance detection task is deployed.
Snippy Variants: antifungal resistance detectionTo detect mutations that may confer antifungal resistance, Snippy is used to find all variants relative to the clade-specific reference, and these variants are queried for product names associated with resistance.
The genes in which there are known resistance-conferring mutations for this pathogen are:
We query Snippy results to see if any mutations were identified in those genes. By default, we automatically check for the following loci (which can be overwritten by the user). You will find the mutations next to the locus tag in the theiaeuk_snippy_variants_hits column corresponding gene name (see below):
Snippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub Cryptococcus neoformansWhen this species is detected by the taxon ID tool an antifungal resistance detection task is deployed.
Snippy Variants: antifungal resistance detectionTo detect mutations that may confer antifungal resistance, Snippy is used to find all variants relative to the clade-specific reference, and these variants are queried for product names associated with resistance.
The genes in which there are known resistance-conferring mutations for this pathogen are:
We query Snippy results to see if any mutations were identified in those genes. By default, we automatically check for the following loci (which can be overwritten by the user). You will find the mutations next to the locus tag in the theiaeuk_snippy_variants_hits column corresponding gene name (see below):
Snippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/genomic_characterization/theiaeuk/#outputs","title":"Outputs","text":"Variable Type Description cg_pipeline_docker String Docker file used for running CG-Pipeline on cleaned reads cg_pipeline_report File TSV file of read metrics from raw reads, including average read length, number of reads, and estimated genome coverage est_coverage_clean Float Estimated coverage calculated from clean reads and genome length est_coverage_raw Float Estimated coverage calculated from raw reads and genome length fastq_scan_clean1_json File JSON file output fromfastq-scan containing summary stats about clean forward read quality and length fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length r1_mean_q_clean Float Mean quality score of clean forward reads r1_mean_q_raw Float Mean quality score of raw forward reads r2_mean_q_clean Float Mean quality score of clean reverse reads r2_mean_q_raw Float Mean quality score of raw reverse reads fastq_scan_version String Version of fastq-scan software used gambit_closest_genomes File CSV file listing genomes in the GAMBIT database that are most similar to the query assembly gambit_db_version String Version of GAMBIT used gambit_docker String GAMBIT docker file used gambit_predicted_taxon String Taxon predicted by GAMBIT gambit_predicted_taxon_rank String Taxon rank of GAMBIT taxon prediction gambit_report File GAMBIT report in a machine-readable format gambit_version String Version of GAMBIT software used assembly_length Int Length of assembly (total contig length) as determined by QUAST n50_value Int N50 of assembly calculated by QUAST number_contigs Int Total number of contigs in assembly quast_report File TSV report from QUAST quast_version String Software version of QUAST used rasusa_version String Version of rasusa used read1_subsampled File Subsampled read1 file read2_subsampled File Subsampled read2 file bbduk_docker String BBDuk docker image used fastp_version String Version of fastp software used read1_clean File Clean forward reads file read2_clean File Clean reverse reads file num_reads_clean_pairs String Number of read pairs after cleaning num_reads_clean1 Int Number of forward reads after cleaning num_reads_clean2 Int Number of reverse reads after cleaning num_reads_raw_pairs String Number of input read pairs num_reads_raw1 Int Number of input forward reads num_reads_raw2 Int Number of input reverse reads trimmomatic_version String Version of trimmomatic used clean_read_screen String PASS or FAIL result from clean read screening; FAIL accompanied by the reason for failure raw_read_screen String PASS or FAIL result from raw read screening; FAIL accompanied by thereason for failure assembly_fasta File https://github.com/tseemann/shovill#contigsfa contigs_fastg File Assembly graph if megahit used for genome assembly contigs_gfa File Assembly graph if spades used for genome assembly contigs_lastgraph File Assembly graph if velvet used for genome assembly shovill_pe_version String Shovill version used theiaeuk_snippy_variants_bam File BAM file produced by the snippy module theiaeuk_snippy_variants_gene_query_results File File containing all lines from variants file matching gene query terms theiaeuk_snippy_variants_hits String String of all variant file entries matching gene query term theiaeuk_snippy_variants_outdir_tarball File Tar compressed file containing full snippy output directory theiaeuk_snippy_variants_query String The gene query term(s) used to search variant theiaeuk_snippy_variants_query_check String Were the gene query terms present in the refence annotated genome file theiaeuk_snippy_variants_reference_genome File The reference genome used in the alignment and variant calling theiaeuk_snippy_variants_results File The variants file produced by snippy theiaeuk_snippy_variants_summary File A file summarizing the variants detected by snippy theiaeuk_snippy_variants_version String The version of the snippy_variants module being used seq_platform String Sequencing platform inout by the user theiaeuk_illumina_pe_analysis_date String Date of TheiaProk workflow execution theiaeuk_illumina_pe_version String TheiaProk workflow version used"},{"location":"workflows/genomic_characterization/theiameta/","title":"TheiaMeta","text":""},{"location":"workflows/genomic_characterization/theiameta/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Any Taxa PHB v2.3.0 Yes Sample-level"},{"location":"workflows/genomic_characterization/theiameta/#theiameta-workflows","title":"TheiaMeta Workflows","text":"Genomic characterization of pathogens is an increasing priority for public health laboratories globally. The workflows in the TheiaMeta Genomic Characterization Series make the analysis of pathogens from metagenomic samples easy by taking raw next-generation sequencing (NGS) data and generating metagenome-assembled genomes (MAGs), either using a reference-genome or not.
TheiaMeta can use one of two distinct methods for generating and processing the final assembly:
TheiaMeta Workflow Diagram
"},{"location":"workflows/genomic_characterization/theiameta/#inputs","title":"Inputs","text":"The\u00a0TheiaMeta_Illumina_PE workflow\u00a0processes Illumina paired-end (PE) reads generated for metagenomic characterization (typically by shotgun). By default, this workflow will assume that input reads were generated using a 300-cycle sequencing kit (i.e. 2 x 150 bp reads). Modifications to the optional parameter for trim_minlen may be required to accommodate shorter read data, such as 2 x 75bp reads generated using a 150-cycle sequencing kit.
versioning: Version Capture for TheiaMeta The versioning task captures the workflow version from the GitHub (code repository) version.
Version Capture Technical details
Links Task task_versioning.wdl"},{"location":"workflows/genomic_characterization/theiameta/#read-cleaning-and-qc","title":"Read Cleaning and QC","text":"HRRT: Human Host Sequence Removal All reads of human origin are removed, including their mates, by using NCBI's human read removal tool (HRRT).
HRRT is based on the SRA Taxonomy Analysis Tool and employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records with any k-mers found in non-Eukaryota RefSeq records subtracted from the database.
NCBI-Scrub Technical Details
Links Task task_ncbi_scrub.wdl Software Source Code NCBI Scrub on GitHub Software Documentation https://github.com/ncbi/sra-human-scrubber/blob/master/README.mdread_QC_trim: Read Quality Trimming, Adapter Removal, Quantification, and Identification read_QC_trim is a sub-workflow within TheiaMeta that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below.
Read quality trimming
Either trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.
If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:
Parameter Explanation -g enables polyG tail trimming -5 20 enables read end-trimming -3 20 enables read end-trimming --detect_adapter_for_pe enables adapter-trimming only for paired-end readsAdapter removal
The BBDuk task removes adapters from sequence reads. To do this:
Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.
Read Quantification
There are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. In TheiaProk_Illumina_PE, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.
Read Identification (optional)
The MIDAS task is for the identification of reads to detect contamination with non-target taxa. This task is optional and turned off by default. It can be used by setting the call_midas input variable to true.
The MIDAS reference database, located at gs://theiagen-large-public-files-rp/terra/theiaprok-files/midas/midas_db_v1.2.tar.gz, is provided as the default. It is possible to provide a custom database. More information is available here.
Example MIDAS report in the ****midas_report column:
MIDAS report column descriptions:
read_QC_trim Technical Details
Links Sub-workflow wf_read_QC_trim_pe.wdlwf_read_QC_trim_se.wdl Tasks task_fastp.wdltask_trimmomatic.wdltask_bbduk.wdltask_fastq_scan.wdltask_midas.wdltask_kraken2.wdl Software Source Code fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2 Software Documentation fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2 Original Publication(s) Trimmomatic: a flexible trimmer for Illumina sequence datafastp: an ultra-fast all-in-one FASTQ preprocessorAn integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeographyImproved metagenomic analysis with Kraken 2kraken: Taxonomic Classification Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
Kraken2 is run on the set of raw reads, provided as input, as well as the set of clean reads that are resulted from the read_QC_trim workflow
Database-dependent
The Kraken2 software is database-dependent and taxonomic assignments are highly sensitive to the database used. An appropriate database should contain the expected organism(s) (e.g. Escherichia coli) and other taxa that may be present in the reads (e.g. Citrobacter freundii, a common contaminant).
Kraken2 Technical Details
Links Task task_kraken2.wdl Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/wiki Original Publication(s) Improved metagenomic analysis with Kraken 2"},{"location":"workflows/genomic_characterization/theiameta/#assembly","title":"Assembly","text":"metaspades: De Novo Metagenomic Assembly While metagenomics has emerged as a technology of choice for analyzing bacterial populations, the assembly of metagenomic data remains challenging. A dedicated metagenomic assembly algorithm is necessary to circumvent the challenge of interpreting variation. metaSPAdes addresses various challenges of metagenomic assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes.
metaspades is a de novo assembler that first constructs a de Bruijn graph of all the reads using the SPAdes algorithm. Through various graph simplification procedures, paths in the assembly graph are reconstructed that correspond to long genomic fragments within the metagenome. For more details, please see the original publication.
MetaSPAdes Technical Details
Links Task task_metaspades.wdl Software Source Code SPAdes on GitHub Software Documentation SPAdes Manual Original Publication(s) metaSPAdes: a new versatile metagenomic assemblerminimap2: Assembly Alignment and Contig Filtering If a reference genome is provided through the reference optional input, the assembly produced with metaspades will be mapped to the reference genome with minimap2. The contigs which align to the reference are retrieved and returned in the assembly_fasta output.
minimap2 is a popular aligner that is used for correcting the assembly produced by metaSPAdes. This is done by aligning the reads back to the generated assembly or a reference genome.
In minimap2, \"modes\" are a group of preset options. Two different modes are used in this task depending on whether a reference genome is provided.
If a reference genome is not provided, the only mode used in this task is sr which is intended for \"short single-end reads without splicing\". The sr mode indicates the following parameters should be used: -k21 -w11 --sr --frag=yes -A2 -B8 -O12,32 -E2,1 -b0 -r100 -p.5 -N20 -f1000,5000 -n2 -m20 -s40 -g100 -2K50m --heap-sort=yes --secondary=no. The output file is in SAM format.
If a reference genome is provided, then after the draft assembly polishing with pilon, this task runs again with the mode set to asm20 which is intended for \"long assembly to reference mapping\". The asm20 mode indicates the following parameters should be used: -k19 -w10 -U50,500 --rmq -r100k -g10k -A1 -B4 -O6,26 -E2,1 -s200 -z200 -N50. The output file is in PAF format.
For more information, please see the minimap2 manpage
minimap2 Technical Details
Links Task task_minimap2.wdl Software Source Code minimap2 on GitHub Software Documentation minimap2 Original Publication(s) Minimap2: pairwise alignment for nucleotide sequencessamtools: SAM File Conversion This task converts the output SAM file from minimap2 and converts it to a BAM file. It then sorts the BAM based on the read names, and then generates an index file.
samtools Technical Details
Links Task task_samtools.wdl Software Source Code samtools on GitHub Software Documentation samtools Original Publication(s) The Sequence Alignment/Map format and SAMtoolsTwelve Years of SAMtools and BCFtoolspilon: Assembly Polishing pilon is a tool that uses read alignment to correct errors in an assembly. It is used to polish the assembly produced by metaSPAdes. The input to Pilon is the sorted BAM file produced by samtools, and the original draft assembly produced by metaspades.
pilon Technical Details
Links Task task_pilon.wdl Software Source Code Pilon on GitHub Software Documentation Pilon Wiki Original Publication(s) Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement"},{"location":"workflows/genomic_characterization/theiameta/#assembly-qc","title":"Assembly QC","text":"quast: Assembly Quality Assessment QUAST stands for QUality ASsessment Tool. It evaluates genome/metagenome assemblies by computing various metrics without a reference being necessary. It includes useful metrics such as number of contigs, length of the largest contig and N50.
QUAST Technical Details
Links Task task_quast.wdl Software Source Code QUAST on GitHub Software Documentation https://quast.sourceforge.net/ Original Publication(s) QUAST: quality assessment tool for genome assemblies"},{"location":"workflows/genomic_characterization/theiameta/#binning","title":"Binning","text":"semibin2: Metagenomic binning (if a reference is NOT provided) If no reference genome is provided through the reference optional input, the assembly produced with metaspades will be binned with semibin2, a a command tool for metagenomic binning with deep learning.
fastq-scan containing summary stats about clean forward read quality and length fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length fastq_scan_num_reads_clean_pairs String Number of read pairs after cleaning as calculated by fastq_scan fastq_scan_num_reads_clean1 Int Number of forward reads after cleaning as calculated by fastq_scan fastq_scan_num_reads_clean2 Int Number of reverse reads after cleaning as calculated by fastq_scan fastq_scan_num_reads_raw_pairs String Number of input read pairs as calculated by fastq_scan fastq_scan_num_reads_raw1 Int Number of input forward reads as calculated by fastq_scan fastq_scan_num_reads_raw2 Int Number of input reserve reads as calculated by fastq_scan fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length fastq_scan_version String fastq_scan version fastqc_clean1_html File Graphical visualization of clean forward read quality from fastqc to open in an internet browser fastqc_clean2_html File Graphical visualization of clean reverse read quality from fastqc to open in an internet browser fastqc_docker String Docker container used for fastqc fastqc_num_reads_clean_pairs String Number of read pairs after cleaning by fastqc fastqc_num_reads_clean1 Int Number of forward reads after cleaning by fastqc fastqc_num_reads_clean2 Int Number of reverse reads after cleaning by fastqc fastqc_num_reads_raw_pairs String Number of input read pairs by fastqc fastqc_num_reads_raw1 Int Number of input forward reads by fastqc fastqc_num_reads_raw2 Int Number of input reverse reads by fastqc fastqc_raw1_html File Graphical visualization of raw forward read quality from fastqc to open in an internet browser fastqc_raw2_html File Graphical visualization of raw reverse read qualityfrom fastqc to open in an internet browser fastqc_version String Version of fastqc software used kraken2_docker String Docker image of kraken2 kraken2_percent_human_clean Float Percentage of human-classified reads in the sample's clean reads kraken2_percent_human_raw Float Percentage of human-classified reads in the sample's raw reads kraken2_report_clean File Full Kraken report for the sample's clean reads kraken2_report_raw File Full Kraken report for the sample's raw reads kraken2_version String Version of kraken krona_docker String Docker image of Krona krona_html_clean File The KronaPlot after reads are cleaned krona_html_raw File The KronaPlot before reads are cleaned krona_version String Version of Krona largest_contig Int Largest contig size metaspades_docker String Docker image of metaspades metaspades_version String Version of metaspades midas_primary_genus String Primary genus detected by MIDAS midas_report File MIDAS report file tsv file minimap2_docker String Docker image of minimap2 minimap2_version String Version of minimap2 ncbi_scrub_docker String Docker image for NCBI's HRRT percent_coverage Float Percentage coverage of the reference genome provided percentage_mapped_reads Float Percentage of mapped reads to the assembly pilon_docker String Docker image for pilon pilon_version String Version of pilon quast_docker String Docker image of QUAST quast_version String Version of QUAST read1_clean File Clean forward reads file read1_dehosted File Dehosted forward reads file read1_mapped File Mapped forward reads to the assembly read1_unmapped File Unmapped forwards reads to the assembly read2_clean File Clean reverse reads file read2_dehosted File Dehosted reverse reads file read2_mapped File Mapped reverse reads to the assembly read2_unmapped File Unmapped reverse reads to the assembly samtools_docker String Docker image of samtools samtools_version String Version of samtools semibin_bins Array[File] Array of binned metagenomic assembled genome files semibin_docker String Docker image of semibin semibin_version String Semibin version used theiameta_illumina_pe_analysis_date String Date of analysis theiameta_illumina_pe_version String Version of workflow trimmomatic_docker String Docker image of trimmomatic trimmomatic_version String Version of trimmomatic used"},{"location":"workflows/genomic_characterization/theiameta/#references","title":"References","text":"Human read removal tool (HRRT): https://github.com/ncbi/sra-human-scrubber
Trimmomatic: Anthony M. Bolger\u00a0and others, Trimmomatic: a flexible trimmer for Illumina sequence data,\u00a0Bioinformatics, Volume 30, Issue 15, August 2014, Pages 2114\u20132120,\u00a0https://doi.org/10.1093/bioinformatics/btu170
Fastq-Scan: https://github.com/rpetit3/fastq-scan
metaSPAdes: Sergey Nurk and others, metaSPAdes: a new versatile metagenomic assembler,\u00a0Genome Res. 2017 May; 27(5): 824\u2013834.,\u00a0https://doi.org/10.1101%2Fgr.213959.116
Pilon: Bruce J. Walker and others. Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement. Plos One. November 19, 2014. https://doi.org/10.1371/journal.pone.0112963
Minimap2: Heng Li, Minimap2: pairwise alignment for nucleotide sequences,\u00a0Bioinformatics, Volume 34, Issue 18, September 2018, Pages 3094\u20133100,\u00a0https://doi.org/10.1093/bioinformatics/bty191
QUAST: Alexey Gurevich\u00a0and others, QUAST: quality assessment tool for genome assemblies,\u00a0Bioinformatics, Volume 29, Issue 8, April 2013, Pages 1072\u20131075,\u00a0https://doi.org/10.1093/bioinformatics/btt086
Samtools: Li, Heng, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, Richard Durbin, and 1000 Genome Project Data Processing Subgroup. 2009. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25(16): 2078-2079.
Bcftools: Petr Danecek, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, Thomas Keane, Shane A McCarthy, Robert M Davies, Heng Li. Twelve years of SAMtools and BCFtools. GigaScience, Volume 10, Issue 2, February 2021, giab008, https://doi.org/10.1093/gigascience/giab008
Semibin2: Shaojun Pan, Xing-Ming Zhao, Luis Pedro Coelho, SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing,\u00a0Bioinformatics, Volume 39, Issue Supplement_1, June 2023, Pages i21\u2013i29,\u00a0https://doi.org/10.1093/bioinformatics/btad209
"},{"location":"workflows/genomic_characterization/theiaprok/","title":"TheiaProk Workflow Series","text":""},{"location":"workflows/genomic_characterization/theiaprok/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Bacteria PHB v2.3.0 Yes, some optional features incompatible Sample-level"},{"location":"workflows/genomic_characterization/theiaprok/#theiaprok-workflows","title":"TheiaProk Workflows","text":"The TheiaProk workflows are for the assembly, quality assessment, and characterization of bacterial genomes. There are currently four TheiaProk workflows designed to accommodate different kinds of input data:
TheiaProk Workflow Diagram
All input reads are processed through \"core tasks\" in the TheiaProk Illumina and ONT workflows. These undertake read trimming and assembly appropriate to the input data type. TheiaProk workflows subsequently launch default genome characterization modules for quality assessment, species identification, antimicrobial resistance gene detection, sequence typing, and more. For some taxa identified, \"taxa-specific sub-workflows\" will be automatically activated, undertaking additional taxa-specific characterization steps. When setting up each workflow, users may choose to use \"optional tasks\" as additions or alternatives to tasks run in the workflow by default.
"},{"location":"workflows/genomic_characterization/theiaprok/#inputs","title":"Inputs","text":"TheiaProk_Illumina_PE Input Read DataThe TheiaProk_Illumina_PE workflow takes in Illumina paired-end read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip before Terra uploads to minimize data upload time.
By default, the workflow anticipates\u00a02 x 150bp\u00a0reads (i.e. the input reads were generated using a 300-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate shorter read data, such as the 2 x 75bp reads generated using a 150-cycle sequencing kit.
TheiaProk_Illumina_SE takes in Illumina single-end reads. Read file names should end with .fastq or .fq, with the optional addition of .gz. Theiagen highly recommends zipping files with gzip before uploading to Terra to minimize data upload time & save on storage costs.
By default, the workflow anticipates 1 x 35 bp reads (i.e. the input reads were generated using a 70-cycle sequencing kit). Modifications to the optional parameter for trim_minlen may be required to accommodate longer read data.
The TheiaProk_ONT workflow takes in base-called ONT read data. Read file names should end with .fastq or .fq, with the optional addition of .gz. When possible, Theiagen recommends zipping files with gzip before uploading to Terra to minimize data upload time.
The ONT sequencing kit and base-calling approach can produce substantial variability in the amount and quality of read data. Genome assemblies produced by the TheiaProk_ONT workflow must be quality assessed before reporting results.
TheiaProk_FASTA Input Assembly DataThe TheiaProk_FASTA workflow takes in assembly files in FASTA format.
Terra Task name Variable Type Description Default value Terra Status Workflow *workflow name samplename String Name of sample to be analyzed Required FASTA, ONT, PE, SE theiaprok_fasta assembly_fasta File Assembly file in fasta format Required FASTA theiaprok_illumina_pe read1 File Illumina forward read file in FASTQ file format (compression optional) Required PE theiaprok_illumina_pe read2 File Illumina reverse read file in FASTQ file format (compression optional) Required PE theiaprok_illumina_se read1 File Illumina forward read file in FASTQ file format (compression optional) Required SE theiaprok_ont read1 File Base-called ONT read file in FASTQ file format (compression optional) Required ONT *workflow name abricate_db String Database to use with the Abricate tool. Options: NCBI, CARD, ARG-ANNOT, Resfinder, MEGARES, EcOH, PlasmidFinder, Ecoli_VF and VFDB vfdb Optional FASTA, ONT, PE, SE *workflow name call_abricate Boolean Set to true to enable the Abricate task FALSE Optional FASTA, ONT, PE, SE *workflow name call_ani Boolean Set to true to enable the ANI task FALSE Optional FASTA, ONT, PE, SE *workflow name call_kmerfinder Boolean Set to true to enable the kmerfinder task FALSE Optional FASTA, ONT, PE, SE *workflow name call_plasmidfinder Boolean Set to true to enable the plasmidfinder task TRUE Optional FASTA, ONT, PE, SE *workflow name call_resfinder Boolean Set to true to enable the ResFinder task FALSE Optional FASTA, ONT, PE, SE *workflow name city String Will be used in the \"city\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name collection_date String Will be used in the \"collection_date\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name county String Will be used in the \"county\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name expected_taxon String If provided, this input will override the taxonomic assignment made by GAMBIT and launch the relevant taxon-specific submodules. It will also modify the organism flag used by AMRFinderPlus. Example format: \"Salmonella enterica\" Optional FASTA, ONT, PE, SE *workflow name genome_annotation String If set to \"bakta\", TheiaProk will use Bakta rather than Prokka to annotate the genome prokka Optional FASTA, ONT, PE, SE *workflow name genome_length Int User-specified expected genome length to be used in genome statistics calculations Optional ONT, PE, SE *workflow name max_genome_length Int Maximum genome length able to pass read screening. For TheiaProk_ONT, screening using max_genome_length is skipped by default. 18040666 Optional ONT, PE, SE *workflow name min_basepairs Int Minimum number of base pairs able to pass read screening 2241820 Optional ONT, PE, SE *workflow name min_coverage Int Minimum genome coverage able to pass read screening. Screening using min_coverage is skipped by default. 5 Optional ONT *workflow name min_coverage Int Minimum genome coverage able to pass read screening 10 Optional PE, SE *workflow name min_genome_length Int Minimum genome length able to pass read screening. For TheiaProk_ONT, screening using min_genome_length is skipped by default. 100000 Optional ONT, PE, SE *workflow name min_proportion Int Minimum proportion of total reads in each read file to pass read screening 40 Optional PE *workflow name min_reads Int Minimum number of reads to pass read screening 5000 Optional ONT *workflow name min_reads Int Minimum number of reads to pass read screening 7472 Optional PE, SE *workflow name originating_lab String Will be used in the \"originating_lab\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name perform_characterization Boolean Set to \"false\" if you want to only generate an assembly and relevant QC metrics and skip all characterization tasks TRUE Optional FASTA, ONT, PE, SE *workflow name qc_check_table File TSV value with taxons for rows and QC values for columns; internal cells represent user-determined QC thresholds; if provided, turns on the QC Check task.Click on the variable name for an example QC Check table Optional FASTA, ONT, PE, SE *workflow name read1_lane2 File If provided, the Concatenate_Illumina_Lanes subworkflow will concatenate all files from the same lane before doing any subsequent analysis Optional PE, SE *workflow name read1_lane3 File If provided, the Concatenate_Illumina_Lanes subworkflow will concatenate all files from the same lane before doing any subsequent analysis Optional PE, SE *workflow name read1_lane4 File If provided, the Concatenate_Illumina_Lanes subworkflow will concatenate all files from the same lane before doing any subsequent analysis Optional PE, SE *workflow name read2_lane2 File If provided, the Concatenate_Illumina_Lanes subworkflow will concatenate all files from the same lane before doing any subsequent analysis Optional PE, SE *workflow name read2_lane3 File If provided, the Concatenate_Illumina_Lanes subworkflow will concatenate all files from the same lane before doing any subsequent analysis Optional PE, SE *workflow name read2_lane4 File If provided, the Concatenate_Illumina_Lanes subworkflow will concatenate all files from the same lane before doing any subsequent analysis Optional PE, SE *workflow name run_id String Will be used in the \"run_id\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name seq_method String Will be used in the \"seq_id\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE *workflow name skip_mash Boolean If true, skips estimation of genome size and coverage in read screening steps. As a result, providing true also prevents screening using these parameters. TRUE Optional ONT, SE *workflow name skip_screen Boolean Option to skip the read screening prior to analysis FALSE Optional ONT, PE, SE *workflow name taxon_tables File File indicating data table names to copy samples of a particular taxon to Optional FASTA, ONT, PE, SE *workflow name terra_project String The name of the Terra Project where you want the taxon tables written to Optional FASTA, ONT, PE, SE *workflow name terra_workspace String The name of the Terra Workspace where you want the taxon tables written to Optional FASTA, ONT, PE, SE *workflow name trim_min_length Int Specifies minimum length of each read after trimming to be kept 25 Optional SE *workflow name trim_min_length Int Specifies minimum length of each read after trimming to be kept 75 Optional PE *workflow name trim_quality_min_score Int Specifies the minimum average quality of bases in a sliding window to be kept 20 Optional PE *workflow name trim_quality_trim_score Int Specifies the average quality of bases in a sliding window to be kept 30 Optional SE *workflow name trim_window_size Int Specifies window size for trimming (the number of bases to average the quality across) 4 Optional SE *workflow name trim_window_size Int Specifies window size for trimming (the number of bases to average the quality across) 4 Optional PE *workflow name zip String Will be used in the \"zip\" column in any taxon-specific tables created in the Export Taxon Tables task Optional FASTA, ONT, PE, SE abricate cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE abricate disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE abricate docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/abricate:1.0.1-abaum-plasmid Optional FASTA, ONT, PE, SE abricate memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE abricate mincov Int Minimum DNA %coverage for the Abricate task 80 Optional FASTA, ONT, PE, SE abricate minid Int Minimum DNA %identity for the Abricate task 80 Optional FASTA, ONT, PE, SE amrfinderplus_task cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE amrfinderplus_task detailed_drug_class Boolean If set to true, amrfinderplus_amr_classes and amrfinderplus_amr_subclasses outputs will be created FALSE Optional FASTA, ONT, PE, SE amrfinderplus_task disk_size Boolean Amount of storage (in GB) to allocate to the AMRFinderPlus task 50 Optional FASTA, ONT, PE, SE amrfinderplus_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ncbi-amrfinderplus:3.12.8-2024-07-22.1 Optional FASTA, ONT, PE, SE amrfinderplus_task hide_point_mutations Boolean If set to true, point mutations are not reported FALSE Optional FASTA, ONT, PE, SE amrfinderplus_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE amrfinderplus_task mincov Float Minimum proportion of reference gene covered for a BLAST-based hit (Methods BLAST or PARTIAL).\" Attribute should be a float ranging from 0-1, such as 0.6 (equal to 60% coverage) 0.5 Optional FASTA, ONT, PE, SE amrfinderplus_task minid Float \"Minimum identity for a blast-based hit hit (Methods BLAST or PARTIAL). -1 means use a curated threshold if it exists and 0.9 otherwise. Setting this value to something other than -1 will override any curated similarity cutoffs.\" Attribute should be a float ranging from 0-1, such as 0.95 (equal to 95% identity) 0.9 Optional FASTA, ONT, PE, SE amrfinderplus_task separate_betalactam_genes Boolean Report beta-Lactam AMR genes separated out by all beta-lactam and the respective beta-lactam subclasses FALSE Optional FASTA, ONT, PE, SE ani ani_threshold Float ANI value threshold must be surpassed in order to output the ani_top_species_match. If a genome does not surpass this threshold (and the percent_bases_aligned_threshold) then the ani_top_species_match output String will show a warning instead of a genus & species. 80 Optional FASTA, ONT, PE, SE ani cpu Int Number of CPUs to allocate to the task 4 Optional FASTA, ONT, PE, SE ani disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE ani docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/mummer:4.0.0-rgdv2 Optional FASTA, ONT, PE, SE ani mash_filter Float Mash distance threshold over which ANI is not calculated 0.9 Optional FASTA, ONT, PE, SE ani memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE ani percent_bases_aligned_threshold Float Threshold regarding the proportion of bases aligned between the query genome and reference genome. If a genome does not surpass this threshold (and the ani_threshold) then the ani_top_species_match output String will show a warning instead of a genus & species. 70 Optional FASTA, ONT, PE, SE ani ref_genome File If not set, uses all 43 genomes in RGDv2 Optional FASTA, ONT, PE, SE bakta bakta_db File Database of reference annotations (seehttps://github.com/oschwengers/bakta#database) gs://theiagen-public-files-rp/terra/theiaprok-files/bakta_db_2022-08-29.tar.gz Optional FASTA, ONT, PE, SE bakta bakta_opts String Parameters to pass to bakta from https://github.com/oschwengers/bakta#usage Optional FASTA, ONT, PE, SE bakta compliant Boolean If true, forces Genbank/ENA/DDJB compliance FALSE Optional FASTA, ONT, PE, SE bakta cpu Int Number of CPUs to allocate to the task 8 Optional FASTA, ONT, PE, SE bakta disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE bakta docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/bakta:1.5.1--pyhdfd78af_0 Optional FASTA, ONT, PE, SE bakta memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional FASTA, ONT, PE, SE bakta prodigal_tf File Prodigal training file to use for CDS prediction by bakta Optional FASTA, ONT, PE, SE bakta proteins Boolean FALSE Optional FASTA, ONT, PE, SE busco cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE busco disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE busco docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/ezlabgva/busco:v5.7.1_cv1 Optional FASTA, ONT, PE, SE busco eukaryote Boolean Assesses eukaryotic organisms, rather than prokaryotic organisms FALSE Optional FASTA, ONT, PE, SE busco memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE cg_pipeline_clean cg_pipe_opts String Options to pass to CG-Pipeline for clean read assessment --fast Optional PE, SE cg_pipeline_clean cpu Int Number of CPUs to allocate to the task 4 Optional PE, SE cg_pipeline_clean disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE, SE cg_pipeline_clean docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/lyveset:1.1.4f Optional PE, SE cg_pipeline_clean memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE cg_pipeline_clean read2 File Internal component, do not modify Do not modify, Optional SE cg_pipeline_raw cg_pipe_opts String Options to pass to CG-Pipeline for raw read assessment --fast Optional PE, SE cg_pipeline_raw cpu Int Number of CPUs to allocate to the task 4 Optional PE, SE cg_pipeline_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE, SE cg_pipeline_raw docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/lyveset:1.1.4f Optional PE, SE cg_pipeline_raw memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE cg_pipeline_raw read2 File Internal component, do not modify Do not modify, Optional SE clean_check_reads cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE, SE clean_check_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE, SE clean_check_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/bactopia/gather_samples:2.0.2 Optional ONT, PE, SE clean_check_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE, SE clean_check_reads organism String Internal component, do not modify Do not modify, Optional ONT, PE, SE clean_check_reads workflow_series String Internal component, do not modify Do not modify, Optional ONT, PE, SE dragonflye assembler String The assembler to use in dragonflye. Three options: raven, miniasm, flye flye Optional ONT dragonflye assembler_options String Enables extra assembler options in quote Optional ONT dragonflye cpu Int Number of CPUs to allocate to the task 4 Optional ONT dragonflye disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT dragonflye docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/dragonflye:1.0.14--hdfd78af_0 Optional ONT dragonflye illumina_polishing_rounds Int Number of polishing rounds to conduct with Illumina data 1 Optional ONT dragonflye illumina_read1 File If Illumina reads are provided, Dragonflye will perform Illumina polishing Optional ONT dragonflye illumina_read2 File If Illumina reads are provided, Dragonflye will perform Illumina polishing Optional ONT dragonflye medaka_model String The model of medaka to use for assembly r941_min_hac_g507 Optional ONT dragonflye memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional ONT dragonflye polishing_rounds Int The number of polishing rounds to conduct (without Illumina) 1 Optional ONT dragonflye use_pilon_illumina_polisher Boolean Set to true to use Pilon to polish Illumina reads FALSE Optional ONT dragonflye use_racon Boolean Set to true to use Racon to polish instead of Medaka FALSE Optional ONT export_taxon_tables asembly_fasta File Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables bbduk_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT export_taxon_tables cg_pipeline_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT export_taxon_tables cg_pipeline_report_clean File Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables cg_pipeline_report_raw File Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables combined_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables combined_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables combined_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables combined_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables contigs_gfa File Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables cpu Int Number of CPUs to allocate to the task 1 Optional FASTA, ONT, PE, SE export_taxon_tables disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE export_taxon_tables docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16 Optional FASTA, ONT, PE, SE export_taxon_tables dragonflye_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables emmtypingtool_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT, SE export_taxon_tables emmtypingtool_emm_type String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables emmtypingtool_results_xml File Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables emmtypingtool_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables est_coverage_clean Float Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables est_coverage_raw Float Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables fastp_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables fastq_scan_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables hicap_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT export_taxon_tables hicap_genes String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables hicap_results_tsv File Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables hicap_serotype String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables hicap_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables kmc_est_genome_length String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables kmc_kmer_stats File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables kmc_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables kraken2_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT, PE export_taxon_tables kraken2_report String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables kraken2_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE export_taxon_tables midas_docker String The Docker container to use for the task Do not modify, Optional FASTA, ONT export_taxon_tables midas_primary_genus String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables midas_report File Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables midas_secondary_genus String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables midas_secondary_genus_abundance Float Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables midas_secondary_genus_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables nanoplot_docker String The Docker container to use for the task Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_html_clean File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_html_raw File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_num_reads_clean1 Int Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_num_reads_raw1 Int Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_est_coverage_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_est_coverage_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_median_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_median_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_median_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_median_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_n50_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_n50_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_stdev_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_r1_stdev_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_tsv_clean File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_tsv_raw File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoplot_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables nanoq_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables num_reads_clean_pairs String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables num_reads_clean1 Int Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables num_reads_clean2 Int Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables num_reads_raw_pairs String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables num_reads_raw1 Int Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables num_reads_raw2 Int Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables r1_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables r1_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables r1_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables r1_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables r2_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables r2_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables rasusa_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables read1 File Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables read1_clean File Internal component, do not modify Do not modify, Optional FASTA export_taxon_tables read2 File Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables read2_clean File Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables seroba_ariba_identity String Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables seroba_ariba_serotype String Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables seroba_details File Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables seroba_docker String The Docker container to use for the task Do not modify, Optional ONT, SE export_taxon_tables seroba_serotype String Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables seroba_version String Internal component, do not modify Do not modify, Optional ONT, SE export_taxon_tables shigeifinder_cluster_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_docker_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_H_antigen_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_ipaH_presence_absence_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_notes_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_num_virulence_plasmid_genes String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_O_antigen_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_report_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_serotype_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shigeifinder_version_reads String Internal component, do not modify Do not modify, Optional ONT export_taxon_tables shovill_pe_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables shovill_se_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables srst2_vibrio_biotype String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_ctxA String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_detailed_tsv String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_ompW String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_serogroup String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_toxR String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables srst2_vibrio_version String Internal component, do not modify Do not modify, Optional FASTA, ONT export_taxon_tables theiaprok_fasta_analysis_date String Internal component, do not modify Do not modify, Optional ONT, PE, SE export_taxon_tables theiaprok_fasta_version String Internal component, do not modify Do not modify, Optional ONT, PE, SE export_taxon_tables theiaprok_illumina_pe_analysis_date String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables theiaprok_illumina_pe_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, SE export_taxon_tables theiaprok_illumina_se_analysis_date String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables theiaprok_illumina_se_version String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE export_taxon_tables theiaprok_ont_analysis_date String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables theiaprok_ont_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables tiptoft_plasmid_replicon_fastq File Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables tiptoft_plasmid_replicon_genes String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables tiptoft_version String Internal component, do not modify Do not modify, Optional FASTA, PE, SE export_taxon_tables trimmomatic_version String Internal component, do not modify Do not modify, Optional FASTA, ONT gambit cpu Int Number of CPUs to allocate to the task 8 Optional FASTA, ONT, PE, SE gambit disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE gambit docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/gambit:1.0.0 Optional FASTA, ONT, PE, SE gambit gambit_db_genomes File User-provided database of assembled query genomes; requires complementary signatures file. If not provided, uses default database, \"/gambit-db\" gs://gambit-databases-rp/2.0.0/gambit-metadata-2.0.0-20240628.gdb Optional FASTA, ONT, PE, SE gambit gambit_db_signatures File User-provided signatures file; requires complementary genomes file. If not specified, the file from the docker container will be used. gs://gambit-databases-rp/2.0.0/gambit-signatures-2.0.0-20240628.gs Optional FASTA, ONT, PE, SE gambit memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional FASTA, ONT, PE, SE kmerfinder cpu Int Number of CPUs to allocate to the task 4 Optional FASTA, ONT, PE, SE kmerfinder disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE kmerfinder docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/kmerfinder:3.0.2--hdfd78af_0 Optional FASTA, ONT, PE, SE kmerfinder kmerfinder_args String Kmerfinder additional arguments Optional FASTA, ONT, PE, SE kmerfinder kmerfinder_db String Bacterial database for KmerFinder gs://theiagen-public-files-rp/terra/theiaprok-files/kmerfinder_bacteria_20230911.tar.gz Optional FASTA, ONT, PE, SE kmerfinder memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional FASTA, ONT, PE, SE merlin_magic abricate_abaum_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/abricate:1.0.1-abaum-plasmid Optional FASTA, ONT, PE, SE merlin_magic abricate_abaum_mincov Int Minimum DNA percent coverage Optional FASTA, ONT, PE, SE merlin_magic abricate_abaum_minid Int Minimum DNA percent identity; set to 95 because there is a strict threshold of 95% identity for typing purposes 95 Optional FASTA, ONT, PE, SE merlin_magic abricate_vibrio_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/abricate:1.0.1-abaum-plasmid Optional FASTA, ONT, PE, SE merlin_magic abricate_vibrio_mincov Int Minimum DNA percent coverage 80 Optional FASTA, ONT, PE, SE merlin_magic abricate_vibrio_minid Int Minimum DNA percent identity 80 Optional FASTA, ONT, PE, SE merlin_magic agrvate_agr_typing_only Boolean Set to true to skip agr operon extraction and frameshift detection False Optional FASTA, ONT, PE, SE merlin_magic agrvate_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/agrvate:1.0.2--hdfd78af_0 Optional FASTA, ONT, PE, SE merlin_magic assembly_only Boolean Internal component, do not modify Do not modify, Optional ONT, PE, SE merlin_magic call_poppunk Boolean If \"true\", runs PopPUNK for GPSC cluster designation for S. pneumoniae TRUE Optional FASTA, ONT, PE, SE merlin_magic call_shigeifinder_reads_input Boolean If set to \"true\", the ShigEiFinder task will run again but using read files as input instead of the assembly file. Input is shown but not used for TheiaProk_FASTA. FALSE Optional FASTA, ONT, PE, SE merlin_magic call_stxtyper Boolean If set to \"true\", the StxTyper task will run on all samples regardless of thegambit_predicted_taxon output. Useful if you suspect a non-E.coli or non-Shigella sample contains stx genes. FALSE Optional FASTA, ONT, PE, SE merlin_magic call_tbp_parser Boolean If set to \"true\", activates the tbp_parser module and results in more outputs, including\u00a0tbp_parser_looker_report_csv, tbp_parser_laboratorian_report_csv, tbp_parser_lims_report_csv, tbp_parser_coverage_report, and tbp_parser_genome_percent_coverage FALSE Optional FASTA, ONT, PE, SE merlin_magic cauris_cladetyper_docker_image String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_kmer_size Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade1 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade1_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade2 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade2_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade3 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade3_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade4 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade4_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade5 File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic cladetyper_ref_clade5_annotated File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic clockwork_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/cdcgov/varpipe_wgs_with_refs:2bc7234074bd53d9e92a1048b0485763cd9bbf6f4d12d5a1cc82bfec8ca7d75e Optional FASTA, ONT, PE, SE merlin_magic ectyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/ectyper:1.0.0--pyhdfd78af_1 Optional FASTA, ONT, PE, SE merlin_magic ectyper_hpcov Int Minumum percent coverage required for an H antigen allele match 50 Optional FASTA, ONT, PE, SE merlin_magic ectyper_hpid Int Percent identity required for an H antigen allele match 95 Optional FASTA, ONT, PE, SE merlin_magic ectyper_opcov Int Minumum percent coverage required for an O antigen allele match 90 Optional FASTA, ONT, PE, SE merlin_magic ectyper_opid Int Percent identity required for an O antigen allele match 90 Optional FASTA, ONT, PE, SE merlin_magic ectyper_print_alleles Boolean Set to true to print the allele sequences as the final column False Optional FASTA, ONT, PE, SE merlin_magic ectyper_verify Boolean Set to true to enable E. coli species verification False Optional FASTA, ONT, PE, SE merlin_magic emmtypingtool_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/emmtypingtool:0.0.1 Optional FASTA, ONT, PE, SE merlin_magic genotyphi_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.11.0 Optional FASTA, ONT, PE, SE merlin_magic hicap_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/hicap:1.0.3--py_0 Optional FASTA, ONT, PE, SE merlin_magic kaptive_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/kaptive:2.0.3 Optional FASTA, ONT, PE, SE merlin_magic kaptive_low_gene_id Float Percent identity threshold for what counts as a low identity match in the gene BLAST search 95 Optional FASTA, ONT, PE, SE merlin_magic kaptive_min_coverage Float Minimum required percent identity for the gene BLAST search via tBLASTn 80 Optional FASTA, ONT, PE, SE merlin_magic kaptive_min_identity Float Minimum required percent coverage for the gene BLAST search via tBLASTn 90 Optional FASTA, ONT, PE, SE merlin_magic kaptive_start_end_margin Int Determines flexibility in identifying the start and end of a locus - if this value is 10, a locus match that is missing the first 8 base pairs will still count as capturing the start of the locus 10 Optional FASTA, ONT, PE, SE merlin_magic kleborate_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/kleborate:2.2.0 Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_coverage Float Minimum alignment percent coverage for main results 80 Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_identity Float Minimum alignment percent identity for main results 90 Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_kaptive_confidence String {None,Low,Good,High,Very_high,Perfect} Minimum Kaptive confidence to call K/O loci - confidence levels below this will be reported as unknown Good Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_spurious_coverage Float Minimum alignment percent coverage for spurious results 40 Optional FASTA, ONT, PE, SE merlin_magic kleborate_min_spurious_identity Float Minimum alignment percent identity for spurious results 80 Optional FASTA, ONT, PE, SE merlin_magic kleborate_skip_kaptive Boolean Equivalent to --kaptive_k --kaptive_ False Optional FASTA, ONT, PE, SE merlin_magic kleborate_skip_resistance Boolean Set to true to turn on resistance genes screening (default: no resistance gene screening) False Optional FASTA, ONT, PE, SE merlin_magic legsta_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/legsta:0.5.1--hdfd78af_2 Optional FASTA, ONT, PE, SE merlin_magic lissero_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/lissero:0.4.9--py_0 Optional FASTA, ONT, PE, SE merlin_magic lissero_min_cov Float Minimum coverage of the gene to accept a match 95 Optional FASTA, ONT, PE, SE merlin_magic lissero_min_id Float Minimum percent identity to accept a match 95 Optional FASTA, ONT, PE, SE merlin_magic meningotype_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/meningotype:0.8.5--pyhdfd78af_0 Optional FASTA, ONT, PE, SE merlin_magic ngmaster_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ngmaster:1.0.0 Optional FASTA, ONT, PE, SE merlin_magic ont_data Boolean Internal component, do not modify Do not modify, Optional FASTA, PE, SE merlin_magic paired_end Boolean Internal component, do not modify Do not modify, Optional ONT, PE merlin_magic pasty_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pasty:1.0.3 Optional FASTA, ONT, PE, SE merlin_magic pasty_min_coverage Int Minimum coverage of a O-antigen to be considered for serogrouping by pasty 95 Optional FASTA, ONT, PE, SE merlin_magic pasty_min_pident Int Minimum percent identity for a blast hit to be considered for serogrouping 95 Optional FASTA, ONT, PE, SE merlin_magic pbptyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pbptyper:1.0.4 Optional FASTA, ONT, PE, SE merlin_magic pbptyper_min_coverage Int Minimum percent coverage to count a hit 90 Optional FASTA, ONT, PE, SE merlin_magic pbptyper_min_pident Int Minimum percent identity to count a hit 90 Optional FASTA, ONT, PE, SE merlin_magic poppunk_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/poppunk:2.4.0 Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_clusters_csv File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_clusters.csv Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_dists_npy File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.dists.npy Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_dists_pkl File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.dists.pkl Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_external_clusters_csv File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_external_clusters.csv Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_fit_npz File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_fit.npz Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_fit_pkl File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_fit.pkl Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_graph_gt File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_graph.gt Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_h5 File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.h5 Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_qcreport_txt File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_qcreport.txt Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.refs Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs_dists_npy File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.refs.dists.npy Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs_dists_pkl File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.refs.dists.pkl Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs_graph_gt File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6refs_graph.gt Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_refs_h5 File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6.refs.h5 Optional FASTA, ONT, PE, SE merlin_magic poppunk_gps_unword_clusters_csv File Poppunk database file *Provide an empty or local file if running TheiaProk on the command-line gs://theiagen-public-files-rp/terra/theiaprok-files/GPS_v6/GPS_v6_unword_clusters.csv Optional FASTA, ONT, PE, SE merlin_magic read1 File Internal component, do not modify Do not modify, Optional FASTA merlin_magic read2 File Internal component, do not modify Do not modify, Optional FASTA, ONT, SE merlin_magic seqsero2_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/seqsero2:1.2.1 Optional FASTA, ONT, PE, SE merlin_magic seroba_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/seroba:1.0.2 Optional FASTA, ONT, PE, SE merlin_magic serotypefinder_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/serotypefinder:2.0.1 Optional FASTA, ONT, PE, SE merlin_magic shigatyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/shigatyper:2.0.5 Optional FASTA, ONT, PE, SE merlin_magic shigeifinder_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/shigeifinder:1.3.5 Optional FASTA, ONT, PE, SE merlin_magic sistr_cpu Int The number of CPU cores to allocate for the task 8 Optional FASTA, ONT, PE, SE merlin_magic sistr_disk_size Int The disk size (in GB) to allocate for the task 100 Optional FASTA, ONT, PE, SE merlin_magic sistr_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/sistr_cmd:1.1.1--pyh864c0ab_2 Optional FASTA, ONT, PE, SE merlin_magic sistr_memory Int The amount of memory (in GB) to allocate for the task. 32 Optional FASTA, ONT, PE, SE merlin_magic sistr_use_full_cgmlst_db Boolean Set to true to use the full set of cgMLST alleles which can include highly similar alleles. By default the smaller \"centroid\" alleles or representative alleles are used for each marker False Optional FASTA, ONT, PE, SE merlin_magic snippy_base_quality Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_gene_query_docker_image String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_map_qual Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_maxsoft Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_min_coverage Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_min_frac Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_min_quality Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_query_gene String Internal component, do not modify Do not modify, Optional FASTA, PE, SE merlin_magic snippy_reference_afumigatus File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_reference_calbicans File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_reference_cryptoneo File *Provide an empty file if running TheiaProk on the command-line Do not modify, Optional FASTA, ONT, PE, SE merlin_magic snippy_variants_docker_image String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic sonneityping_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional FASTA, ONT, PE, SE merlin_magic sonneityping_mykrobe_opts String Additional options for mykrobe in sonneityping Optional FASTA, ONT, PE, SE merlin_magic spatyper_do_enrich Boolean Set to true to enable PCR product enrichment False Optional FASTA, ONT, PE, SE merlin_magic spatyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/spatyper:0.3.3--pyhdfd78af_3 Optional FASTA, ONT, PE, SE merlin_magic srst2_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/srst2:0.2.0-vcholerae Optional FASTA, ONT, PE, SE merlin_magic srst2_gene_max_mismatch Int Maximum number of mismatches for SRST2 to call a gene as present 2000 Optional FASTA, ONT, PE, SE merlin_magic srst2_max_divergence Int Maximum divergence, in percentage, for SRST2 to call a gene as present 20 Optional FASTA, ONT, PE, SE merlin_magic srst2_min_cov Int Minimum breadth of coverage for SRST2 to call a gene as present 80 Optional FASTA, ONT, PE, SE merlin_magic srst2_min_depth Int Minimum depth of coverage for SRST2 to call a gene as present 5 Optional FASTA, ONT, PE, SE merlin_magic srst2_min_edge_depth Int Minimum edge depth for SRST2 to call a gene as present 2 Optional FASTA, ONT, PE, SE merlin_magic stxtyper_cpu Int The number of CPU cores to allocate for the task. 1 Optional FASTA, ONT, PE, SE merlin_magic stxtyper_disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional FASTA, ONT, PE, SE merlin_magic stxtyper_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/stxtyper:1.0.24 Optional FASTA, ONT, PE, SE merlin_magic stxtyper_enable_debug Boolean When enabled, additional messages are printed and files in $TMPDIR are not removed after running FALSE Optional FASTA, ONT, PE, SE merlin_magic stxtyper_memory Int Amount of memory (in GB) to allocate to the task 4 Optional FASTA, ONT, PE, SE merlin_magic staphopia_sccmec_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/staphopia-sccmec:1.0.0--hdfd78af_0 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_add_cs_lims Boolean Set to true add cycloserine results to the LIMS report FALSE Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_coverage_regions_bed File A bed file that lists the regions to be considered for QC Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_coverage_threshold Int The minimum coverage for a region to pass QC in tbp_parser 100 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_debug Boolean Activate the debug mode on tbp_parser; increases logging outputs TRUE Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/tbp-parser:2.1.1 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_etha237_frequency Float Minimum frequency for a mutation in ethA at protein position 237 to pass QC in tbp-parser 0.1 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_expert_rule_regions_bed File A file that contains the regions where R mutations and expert rules are applied Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_min_depth Int Minimum depth for a variant to pass QC in tbp_parser 10 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_min_frequency Int The minimum frequency for a mutation to pass QC 0.1 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_min_read_support Int The minimum read support for a mutation to pass QC 10 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_operator String Fills the \"operator\" field in the tbp_parser output files Operator not provided Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_output_seq_method_type String Fills out the \"seq_method\" field in the tbp_parser output files Sequencing method not provided Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_rpob449_frequency Float Minimum frequency for a mutation at protein position 449 to pass QC in tbp-parser 0.1 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_rrl_frequency Float Minimum frequency for a mutation in rrl to pass QC in tbp-parser 0.1 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_rrl_read_support Int Minimum read support for a mutation in rrl to pass QC in tbp-parser 10 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_rrs_frequency Float Minimum frequency for a mutation in rrs to pass QC in tbp-parser 0.1 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_rrs_read_support Int Minimum read support for a mutation in rrs to pass QC in tbp-parser 10 Optional FASTA, ONT, PE, SE merlin_magic tbp_parser_tngs_data Boolean Set to true to enable tNGS-specific parameters and runs in tbp-parser FALSE Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_custom_db File TBProfiler uses by default the TBDB database; if you have a custom database you wish to use, you must provide a custom database in this field and set tbprofiler_run_custom_db to true Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/tbprofiler:4.4.2 Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_mapper String The mapping tool used in TBProfiler to align the reads to the reference genome; see TBProfiler\u2019s original documentation for available options. bwa Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_min_af Float The minimum allele frequency to call a variant 0.1 Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_min_depth Int The minimum depth for a variant to be called. 10 Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_run_cdph_db Boolean TBProfiler uses by default the TBDB database; set this value to \"true\" to use the WHO v2 database with customizations for CDPH FALSE Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_run_custom_db Boolean TBProfiler uses by default the TBDB database; if you have a custom database you wish to use, you must set this value to true and provide a custom database in the tbprofiler_custom_db field FALSE Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_variant_caller String Select a different variant caller for TBProfiler to use by writing it in this block; see TBProfiler\u2019s original documentation for available options. GATK Optional FASTA, ONT, PE, SE merlin_magic tbprofiler_variant_calling_params String Enter additional variant calling parameters in this free text input to customize how the variant caller works in TBProfiler Optional FASTA, ONT, PE, SE merlin_magic theiaeuk Boolean Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE merlin_magic virulencefinder_coverage_threshold Float The threshold for minimum coverage Optional FASTA, ONT, PE, SE merlin_magic virulencefinder_database String The specific database to use virulence_ecoli Optional FASTA, ONT, PE, SE merlin_magic virulencefinder_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/virulencefinder:2.0.4 Optional FASTA, ONT, PE, SE merlin_magic virulencefinder_identity_threshold Float The threshold for minimum blast identity Optional FASTA, ONT, PE, SE nanoplot_clean cpu Int Number of CPUs to allocate to the task 4 Optional ONT nanoplot_clean disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT nanoplot_clean docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0 Optional ONT nanoplot_clean max_length Int Maximum read length for nanoplot 100000 Optional ONT nanoplot_clean memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT nanoplot_raw cpu Int Number of CPUs to allocate to the task 4 Optional ONT nanoplot_raw disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT nanoplot_raw docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/nanoplot:1.40.0 Optional ONT nanoplot_raw max_length Int Maximum read length for nanoplot 100000 Optional ONT nanoplot_raw memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional ONT plasmidfinder cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE plasmidfinder database String User-specified database Optional FASTA, ONT, PE, SE plasmidfinder database_path String Path to user-specified database Optional FASTA, ONT, PE, SE plasmidfinder disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional FASTA, ONT, PE, SE plasmidfinder docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/plasmidfinder:2.1.6 Optional FASTA, ONT, PE, SE plasmidfinder memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE plasmidfinder method_path String Path to files for a user-specified method to use (blast or kma) Optional FASTA, ONT, PE, SE plasmidfinder min_cov Float Threshold for minimum coverage, default threshold from PlasmidFinder CLI tool is used (0.60) 0.6 Optional FASTA, ONT, PE, SE plasmidfinder threshold Float Threshold for mininum blast identity, default threshold from PlasmidFinder CLI tool is used (0.90). This default differs from the default of the PlasmidFinder webtool (0.95) 0.9 Optional FASTA, ONT, PE, SE prokka compliant Boolean Forces Genbank/ENA/DDJB compliant headers in Prokka output files TRUE Optional FASTA, ONT, PE, SE prokka cpu Int Number of CPUs to allocate to the task 8 Optional FASTA, ONT, PE, SE prokka disk_size String Amount of storage (in GB) to allocate to the PlasmidFinder task 100 Optional FASTA, ONT, PE, SE prokka docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/prokka:1.14.5 Optional FASTA, ONT, PE, SE prokka memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional FASTA, ONT, PE, SE prokka prodigal_tf File https://github.com/tseemann/prokka#option---prodigaltf Optional FASTA, ONT, PE, SE prokka prokka_arguments String Any additional https://github.com/tseemann/prokka#command-line-options Optional FASTA, ONT, PE, SE prokka proteins Boolean FASTA file of trusted proteins for Prokka to first use for annotations FALSE Optional FASTA, ONT, PE, SE qc_check_task assembly_length_unambiguous Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task assembly_mean_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task combined_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task combined_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task combined_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task combined_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task cpu Int Number of CPUs to allocate to the task 4 Optional FASTA, ONT, PE, SE qc_check_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE qc_check_task docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16\" Optional FASTA, ONT, PE, SE qc_check_task est_coverage_clean Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task est_coverage_raw Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task kraken_human Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_human_dehosted Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_sc2 Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_sc2_dehosted Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_target_organism Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task kraken_target_organism_dehosted Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task meanbaseq_trim String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE qc_check_task midas_secondary_genus_abundance Int Internal component, do not modify Do not modify, Optional FASTA, ONT qc_check_task midas_secondary_genus_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT qc_check_task num_reads_clean1 Int Internal component, do not modify Do not modify, Optional FASTA qc_check_task num_reads_clean2 Int Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task num_reads_raw1 Int Internal component, do not modify Do not modify, Optional FASTA qc_check_task num_reads_raw2 Int Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task number_Degenerate Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task number_N Int Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task percent_reference_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task r1_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task r1_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task r1_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task r1_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA qc_check_task r2_mean_q_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task r2_mean_q_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task r2_mean_readlength_clean Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task r2_mean_readlength_raw Float Internal component, do not modify Do not modify, Optional FASTA, ONT, SE qc_check_task sc2_s_gene_mean_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task sc2_s_gene_percent_coverage Float Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE qc_check_task vadr_num_alerts String Internal component, do not modify Do not modify, Optional FASTA, ONT, PE, SE quast cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE quast disk_size String Amount of storage (in GB) to allocate to the Quast task 100 Optional FASTA, ONT, PE, SE quast docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/quast:5.0.2 Optional FASTA, ONT, PE, SE quast memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional FASTA, ONT, PE, SE quast min_contig_length Int Lower threshold for a contig length in bp. Shorter contigs won\u2019t be taken into account 500 Optional FASTA, ONT, PE, SE raw_check_reads cpu Int Number of CPUs to allocate to the task 2 Optional ONT, PE, SE raw_check_reads disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT, PE, SE raw_check_reads docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/bactopia/gather_samples:2.0.2 Optional ONT, PE, SE raw_check_reads memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ONT, PE, SE raw_check_reads organism String Internal component, do not modify Do not modify, Optional ONT, PE, SE raw_check_reads workflow_series String Internal component, do not modify Do not modify, Optional ONT, PE, SE read_QC_trim adapters File A file containing the sequence of the adapters used during library preparation, used in the BBDuk task Optional PE, SE read_QC_trim bbduk_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE read_QC_trim call_kraken Boolean Set to true to launch Kraken2; if true, you must provide a kraken_db FALSE Optional ONT, PE, SE read_QC_trim call_midas Boolean Set to true to launch Midas TRUE Optional PE, SE read_QC_trim downsampling_coverage Float The depth to downsample to with Rasusa 150 Optional ONT read_QC_trim fastp_args String Additional arguments to pass to fastp -g -5 20 -3 20 Optional SE read_QC_trim fastp_args String Additional arguments to pass to fastp \"--detect_adapter_for_pe -g -5 20 -3 20 Optional PE read_QC_trim kraken_cpu Int Number of CPUs to allocate to the task 4 Optional ONT, PE, SE read_QC_trim kraken_db File Kraken2 database file; must be provided in call_kraken is true Optional ONT, PE, SE read_QC_trim kraken_disk_size Int GB of storage to request for VM used to run the kraken2 task. Increase this when using large (>30GB kraken2 databases such as the \"k2_standard\" database) 100 Optional ONT, PE, SE read_QC_trim kraken_memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional ONT, PE, SE read_QC_trim max_length Int Internal component, do not modify Do not modify, Optional ONT read_QC_trim midas_db File Midas database file gs://theiagen-large-public-files-rp/terra/theiaprok-files/midas/midas_db_v1.2.tar.gz Optional PE, SE read_QC_trim min_length Int Internal component, do not modify Do not modify, Optional ONT read_QC_trim phix File A file containing the phix used during Illumina sequencing; used in the BBDuk task Optional PE, SE read_QC_trim read_processing String Read trimming software to use, either \"trimmomatic\" or \"fastp\" trimmomatic Optional PE, SE read_QC_trim read_qc String Allows the user to decide between fastq_scan (default) and fastqc for the evaluation of read quality. fastq_scan Optional PE, SE read_QC_trim run_prefix String Internal component, do not modify Do not modify, Optional ONT read_QC_trim target_organism String This string is searched for in the kraken2 outputs to extract the read percentage Optional ONT, PE, SE read_QC_trim trimmomatic_args String Additional arguments to pass to trimmomatic. \"-phred33\" specifies the Phred Q score encoding which is almost always phred33 with modern sequence data. -phred33 Optional PE, SE resfinder_task acquired Boolean Set to true to tell ResFinder to identify acquired resistance genes TRUE Optional FASTA, ONT, PE, SE resfinder_task call_pointfinder Boolean Set to true to enable detection of point mutations. FALSE Optional FASTA, ONT, PE, SE resfinder_task cpu Int Number of CPUs to allocate to the task 2 Optional FASTA, ONT, PE, SE resfinder_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional FASTA, ONT, PE, SE resfinder_task docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/resfinder:4.1.11 Optional FASTA, ONT, PE, SE resfinder_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional FASTA, ONT, PE, SE resfinder_task min_cov Float Minimum coverage breadth of a gene for it to be identified 0.5 Optional FASTA, ONT, PE, SE resfinder_task min_id Float Minimum identity for ResFinder to identify a gene 0.9 Optional FASTA, ONT, PE, SE shovill_pe assembler String Assembler to use (spades, skesa, velvet or megahit), see https://github.com/tseemann/shovill#--assembler skesa Optional PE shovill_pe assembler_options String Assembler-specific options that you might choose, see https://github.com/tseemann/shovill#--opts Optional PE shovill_pe cpu Int Number of CPUs to allocate to the task 4 Optional PE shovill_pe depth Int User specified depth of coverage for downsampling (see https://github.com/tseemann/shovill#--depth and https://github.com/tseemann/shovill#main-steps) 150 Optional PE shovill_pe disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE shovill_pe docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/shovill:1.1.0 Optional PE shovill_pe kmers String User-specified Kmer length to override choice made by Shovill, see https://github.com/tseemann/shovill#--kmers Auto Optional PE shovill_pe memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional PE shovill_pe min_contig_length Int Minimum contig length to keep in final assembly 200 Optional PE shovill_pe min_coverage Float Minimum contig coverage to keep in final assembly 2 Optional PE shovill_pe nocorr Boolean Disable correction of minor assembly errors by Shovill (see https://github.com/tseemann/shovill#main-steps) FALSE Optional PE shovill_pe noreadcorr Boolean Disable correction of sequencing errors in reads by Shovill (seehttps://github.com/tseemann/shovill#main-steps) FALSE Optional PE shovill_pe nostitch Boolean Disable read stitching by Shovill (see https://github.com/tseemann/shovill#main-steps) FALSE Optional PE shovill_pe trim Boolean Enable adaptor trimming (see https://github.com/tseemann/shovill#main-steps) FALSE Optional PE shovill_se assembler String Assembler to use (spades, skesa, velvet or megahit), see https://github.com/tseemann/shovill#--assembler skesa Optional SE shovill_se assembler_options String Assembler-specific options that you might choose, see https://github.com/tseemann/shovill#--opts Optional SE shovill_se cpu Int Number of CPUs to allocate to the task 4 Optional SE shovill_se depth Int User specified depth of coverage for downsampling (see https://github.com/tseemann/shovill#--depth and https://github.com/tseemann/shovill#main-steps) 150 Optional SE shovill_se disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional SE shovill_se docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/shovill:1.1.0 Optional SE shovill_se kmers String User-specified Kmer length to override choice made by Shovill, see https://github.com/tseemann/shovill#--kmers auto Optional SE shovill_se memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional SE shovill_se min_contig_length Int Minimum contig length to keep in final assembly 200 Optional SE shovill_se min_coverage Float Minimum contig coverage to keep in final assembly 2 Optional SE shovill_se nocorr Boolean Disable correction of minor assembly errors by Shovill (see https://github.com/tseemann/shovill#main-steps) FALSE Optional SE shovill_se noreadcorr Boolean Disable correction of sequencing errors in reads by Shovill (seehttps://github.com/tseemann/shovill#main-steps) FALSE Optional SE shovill_se trim Boolean Enable adaptor trimming (see https://github.com/tseemann/shovill#main-steps) FALSE Optional SE ts_mlst cpu Int Number of CPUs to allocate to the task 1 Optional FASTA, ONT, PE, SE ts_mlst disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional FASTA, ONT, PE, SE ts_mlst docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mlst:2.23.0-2024-08-01 Optional FASTA, ONT, PE, SE ts_mlst memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional FASTA, ONT, PE, SE ts_mlst mincov Float Minimum % breadth of coverage to report an MLST allele 10 Optional FASTA, ONT, PE, SE ts_mlst minid Float Minimum % identity to known MLST gene to report an MLST allele 95 Optional FASTA, ONT, PE, SE ts_mlst minscore Float Minimum https://github.com/tseemann/mlst#scoring-system to assign an MLST profile 50 Optional FASTA, ONT, PE, SE ts_mlst nopath Boolean true = use mlst --nopath. If set to false, filename paths are not stripped from FILE column in output TSV TRUE Optional FASTA, ONT, PE, SE ts_mlst scheme String Don\u2019t autodetect the MLST scheme; force this scheme on all inputs (see https://www.notion.so/TheiaProk-Workflow-Series-68c34aca2a0240ef94fef0acd33651b9?pvs=21 for accepted strings) None Optional FASTA, ONT, PE, SE version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional FASTA, ONT, PE, SE version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) FASTA, ONT, PE, SE Skip Characterization
Ever wanted to skip characterization? Now you can! Set the optional input perform_characterization to false to only generate an assembly and run assembly QC.
versioning: Version Capture for TheiaProk The versioning task captures the workflow version from the GitHub (code repository) version.
Version Capture Technical details
Links Task task_versioning.wdlconcatenate_illumina_lanes: Concatenate Multi-Lane Illumina FASTQs for Illumina only The concatenate_illumina_lanes task concatenates Illumina FASTQ files from multiple lanes into a single file. This task only runs if the read1_lane2 input file has been provided. All read1 lanes are concatenated together and are used in subsequent tasks, as are the read2 lanes. These concatenated files are also provided as output.
Concatenate Illumina Lanes Technical Details
The concatenate_illumina_lanes task is run before any downstream steps take place.
screen: Total Raw Read Quantification and Genome Size Estimation The screen task ensures the quantity of sequence data is sufficient to undertake genomic analysis. It uses fastq-scan and bash commands for quantification of reads and base pairs, and mash sketching to estimate the genome size and its coverage. At each step, the results are assessed relative to pass/fail criteria and thresholds that may be defined by optional user inputs. Samples that do not meet these criteria will not be processed further by the workflow:
min_reads.min_proportion basepairs are in either the reads1 or read2 files.min_basepairs basepairsmin_genome_size or bigger than max_genome_size.min_coverage.Read screening is undertaken on both the raw and cleaned reads. The task may be skipped by setting the skip_screen variable to true.
Default values vary between the PE and SE workflow. The rationale for these default values can be found below. If two default values are shown, the first is for Illumina workflows and the second is for ONT.
Variable Default Value Rationaleskip_screen false Set to false to avoid waste of compute resources processing insufficient data min_reads 7472 or 5000 Calculated from the minimum number of base pairs required for 20x coverage of Nasuia deltocephalinicola genome, the smallest known bacterial genome as of 2019-08-07 (112,091 bp), divided by 300 (the longest Illumina read length) or 5000 (estimate of ONT read length) min_basepairs 2241820 Should be greater than 20x coverage of Nasuia deltocephalinicola, the smallest known bacterial genome (112,091 bp) min_genome_length 100000 Based on the Nasuia deltocephalinicola genome - the smallest known bacterial genome (112,091 bp) max_genome_length 18040666 Based on the Minicystis rosea genome, the biggest known bacterial genome (16,040,666 bp), plus an additional 2 Mbp to cater for potential extra genomic material min_coverage 10 or 5 A bare-minimum average per base coverage across the genome required for genome characterization. Note, a higher per base coverage coverage would be required for high-quality phylogenetics. min_proportion 40 Neither read1 nor read2 files should have less than 40% of the total number of reads. For paired-end data only Screen Technical Details
There is a single WDL task for read screening that contains two separate sub-tasks, one used for PE data and the other for SE data. The screen task is run twice, once for raw reads and once for clean reads.
read_QC_trim: Read Quality Trimming, Adapter Removal, Quantification, and Identification read_QC_trim is a sub-workflow within TheiaMeta that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below.
Read quality trimming
Either trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.
If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:
Parameter Explanation -g enables polyG tail trimming -5 20 enables read end-trimming -3 20 enables read end-trimming --detect_adapter_for_pe enables adapter-trimming only for paired-end readsAdapter removal
The BBDuk task removes adapters from sequence reads. To do this:
Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.
Read Quantification
There are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. In TheiaProk_Illumina_PE, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.
Read Identification (optional)
The MIDAS task is for the identification of reads to detect contamination with non-target taxa. This task is optional and turned off by default. It can be used by setting the call_midas input variable to true.
The MIDAS tool was originally designed for metagenomic sequencing data but has been co-opted for use with bacterial isolate WGS methods. It can be used to detect contamination present in raw sequencing data by estimating bacterial species abundance in bacterial isolate WGS data. If a secondary genus is detected above a relative frequency of 0.01 (1%), then the sample should fail QC and be investigated further for potential contamination.
This task is similar to those used in commercial software, BioNumerics, for estimating secondary species abundance.
How are the MIDAS output columns determined?Example MIDAS report in the midas_report column:
MIDAS report column descriptions:
The value in the midas_primary_genus column is derived by ordering the rows in order of \"relative_abundance\" and identifying the genus of top species in the \"species_id\" column (Salmonella). The value in the midas_secondary_genus column is derived from the genus of the second-most prevalent genus in the \"species_id\" column (Citrobacter). The midas_secondary_genus_abundance column is the \"relative_abundance\" of the second-most prevalent genus (0.009477003). The midas_secondary_genus_coverage is the \"coverage\" of the second-most prevalent genus (0.995216227).
MIDAS Reference Database Overview
The MIDAS reference database is a comprehensive tool for identifying bacterial species in metagenomic and bacterial isolate WGS data. It includes several layers of genomic data, helping detect species abundance and potential contaminants.
Key Components of the MIDAS Database
MIDAS clusters bacterial genomes based on 96.5% sequence identity, forming over 5,950 species groups from 31,007 genomes. These groups align with the gold-standard species definition (95% ANI), ensuring highly accurate species identification.
Genomic Data Structure:
Pan-genome: The database includes clusters of non-redundant genes, with options for multi-level clustering (e.g., 99%, 95%, 90% identity), enabling MIDAS to identify gene content within strains at various clustering thresholds.
Taxonomic Annotation:
Using the Default MIDAS Database
TheiaProk uses the pre-loaded MIDAS database in Terra (see input table for current version) by default for bacterial species detection in metagenomic data, requiring no additional setup.
How to Set Up the Default MIDAS Database
Users can also build their own custom MIDAS database if they want to include specific genomes or configurations. This custom database can replace the default MIDAS database used in Terra. To build a custom MIDAS database, follow the MIDAS GitHub guide on building a custom database. Once the database is built, users can upload it to a Google Cloud Storage bucket or Terra workkspace and provide the link to the database in the midas_db input variable.
Alternatively to MIDAS, the Kraken2 task can also be turned on through setting the call_kraken input variable as true for the identification of reads to detect contamination with non-target taxa.
Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate) whole genome sequence data. A database must be provided if this optional module is activated, through the kraken_db optional input. A list of suggested databases can be found on Kraken2 standalone documentation.
read_QC_trim Technical Details
Links Sub-workflow wf_read_QC_trim_pe.wdlwf_read_QC_trim_se.wdl Tasks task_fastp.wdltask_trimmomatic.wdltask_bbduk.wdltask_fastq_scan.wdltask_midas.wdltask_kraken2.wdl Software Source Code fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2 Software Documentation fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2 Original Publication(s) Trimmomatic: a flexible trimmer for Illumina sequence datafastp: an ultra-fast all-in-one FASTQ preprocessorAn integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeographyImproved metagenomic analysis with Kraken 2CG-Pipeline: Assessment of Read Quality, and Estimation of Genome Coverage Thecg_pipeline task generates metrics about read quality and estimates the coverage of the genome using the \"run_assembly_readMetrics.pl\" script from CG-Pipeline. The genome coverage estimates are calculated using both using raw and cleaned reads, using either a user-provided genome_size or the estimated genome length generated by QUAST.
CG-Pipeline Technical Details
The cg_pipeline task is run twice in TheiaProk, once with raw reads, and once with clean reads.
shovill: De novo Assembly De Novo assembly will be undertaken only for samples that have sufficient read quantity and quality, as determined by the screen task assessment of clean reads.
In TheiaProk, assembly is performed using the Shovill pipeline. This undertakes the assembly with one of four assemblers (SKESA (default), SPAdes, Velvet, Megahit), but also performs a number of pre- and post-processing steps to improve the resulting genome assembly. Shovill uses an estimated genome size (see here). If this is not provided by the user as an optional input, Shovill will estimate the genome size using mash. Adaptor trimming can be undertaken with Shovill by setting the trim option to \"true\", but this is set to \"false\" by default as alternative adapter trimming is undertaken in the TheiaEuk workflow.
De novo assembly is the process or product of attempting to reconstruct a genome from scratch (without prior knowledge of the genome) using sequence reads. Assembly of fungal genomes from short-reads will produce multiple contigs per chromosome rather than a single contiguous sequence for each chromosome.
Shovill Technical Details
Links TheiaEuk WDL Task task_shovill.wdl Software Source Code Shovill on GitHub Software Documentation Shovill on GitHub"},{"location":"workflows/genomic_characterization/theiaprok/#ont-data-core-tasks","title":"ONT Data Core Tasks","text":"read_QC_trim_ont: Read Quality Trimming, Quantification, and Identification read_QC_trim_ont is a sub-workflow within TheiaProk_ONT that filters low-quality reads and trims low-quality regions of reads. It uses several tasks, described below.
Estimated genome length:
By default, an estimated genome length is set to 5 Mb, which is around 0.7 Mb higher than the average bacterial genome length, according to the information collated here. This estimate can be overwritten by the user, and is used by Rasusa and dragonflye.
Plotting and quantifying long-read sequencing data: nanoplot
Nanoplot is used for the determination of mean quality scores, read lengths, and number of reads. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads.
Read subsampling: Samples are automatically randomly subsampled to 150X coverage using RASUSA.
Plasmid prediction: tiptoft is used to predict plasmid sequences directly from uncorrected long-read data. Plasmids are identified using replicon sequences used for typing from PlasmidFinder.
Read filtering: Reads are filtered by length and quality using nanoq. By default, sequences with less than 500 basepairs and quality score lower than 10 are filtered out to improve assembly accuracy.
read_QC_trim_ont Technical Details
TheiaProk_ONT calls a sub-workflow listed below, which then calls the individual tasks:
Workflow TheiaProk_ONT Sub-workflow wf_read_QC_trim_ont.wdl Tasks task_nanoplot.wdl task_fastq_scan.wdl task_rasusa.wdl task_nanoq.wdl task_tiptoft.wdl Software Source Code fastq-scan, NanoPlot, RASUSA, tiptoft, nanoq Original Publication(s) NanoPlot paperRASUSA paperNanoq PaperTiptoft paperdragonflye: De novo Assembly dragonflye Technical Details
Links Task task_dragonflye.wdl Software Source Code dragonflye on GitHub Software Documentation dragonflye on GitHub"},{"location":"workflows/genomic_characterization/theiaprok/#post-assembly-tasks-performed-for-all-taxa","title":"Post-Assembly Tasks (performed for all taxa)","text":"quast: Assembly Quality Assessment QUAST stands for QUality ASsessment Tool. It evaluates genome/metagenome assemblies by computing various metrics without a reference being necessary. It includes useful metrics such as number of contigs, length of the largest contig and N50.
QUAST Technical Details
Links Task task_quast.wdl Software Source Code QUAST on GitHub Software Documentation https://quast.sourceforge.net/ Original Publication(s) QUAST: quality assessment tool for genome assembliesBUSCO: Assembly Quality Assessment BUSCO (Benchmarking Universal Single-Copy Orthologue) attempts to quantify the completeness and contamination of an assembly to generate quality assessment metrics. It uses taxa-specific databases containing genes that are all expected to occur in the given taxa, each in a single copy. BUSCO examines the presence or absence of these genes, whether they are fragmented, and whether they are duplicated (suggestive that additional copies came from contaminants).
BUSCO notation
Here is an example of BUSCO notation: C:99.1%[S:98.9%,D:0.2%],F:0.0%,M:0.9%,n:440. There are several abbreviations used in this output:
A high equity assembly will use the appropriate database for the taxa, have high complete (C) and single-copy (S) percentages, and low duplicated (D), fragmented (F) and missing (M) percentages.
BUSCO Technical Details
Links Task task_busco.wdl Software Source Code BUSCO on GitLab Software Documentation https://busco.ezlab.org/ Orginal publication BUSCO: assessing genome assembly and annotation completeness with single-copy orthologsMUMmer_ANI: Average Nucleotide Identity (optional) Average Nucleotide Identity (ANI) is a useful approach for taxonomic identification. The higher the percentage ANI of a query sequence to a given reference genome, the more likely the sequence is the same taxa as the reference.
ANI is calculated in TheiaProk using a perl script written by Lee Katz (ani-m.pl). This uses MUMmer to rapidly align entire query assemblies to one or more reference genomes. By default, TheiaProk uses a set of 43 reference genomes in RGDv2, a database containing genomes of enteric pathogens commonly sequenced by CDC EDLB & PulseNet participating laboratories. The user may also provide their own reference genome. After genome alignment with MUMmer, ani-m.pl calculates the average nucleotide identity and percent bases aligned between 2 genomes (query and reference genomes)
The default database of reference genomes used is called \"Reference Genome Database version 2\" AKA \"RGDv2\". This database is composed of 43 enteric bacteria representing 32 species and is intended for identification of enteric pathogens and common contaminants. It contains six Campylobacter spp., three Escherichia/Shigella spp., one Grimontia hollisae, six Listeria spp., one Photobacterium damselae, two Salmonella spp., and thirteen Vibrio spp.
2 Thresholds are utilized to prevent false positive hits. The ani_top_species_match will only report a genus & species match if both thresholds are surpassed. Both of these thresholds are set to match those used in BioNumerics for PulseNet organisms.
ani_threshold default value of 80.0percent_bases_aligned_threshold default value of 70.0For more information on RGDv2 database of reference genomes, please see the publication here.
MUMmer_ANI Technical Details
Links Task task_mummer_ani.wdl Software Source Code ani-m, MUMmer Software Documentation ani-m, MUMmer Original Publication(s) MUMmer4: A fast and versatile genome alignment system Publication about RGDv2 database https://www.frontiersin.org/articles/10.3389/fmicb.2023.1225207/fullGAMBIT: Taxon Assignment GAMBIT determines the taxon of the genome assembly using a k-mer based approach to match the assembly sequence to the closest complete genome in a database, thereby predicting its identity. Sometimes, GAMBIT can confidently designate the organism to the species level. Other times, it is more conservative and assigns it to a higher taxonomic rank.
For additional details regarding the GAMBIT tool and a list of available GAMBIT databases for analysis, please consult the GAMBIT tool documentation.
GAMBIT Technical Details
Links Task task_gambit.wdl Software Source Code GAMBIT on GitHub Software Documentation GAMBIT ReadTheDocs Original Publication(s) GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identificationKmerFinder: Taxon Assignment (optional) The KmerFinder method predicts prokaryotic species based on the number of overlapping (co-occurring)\u00a0k-mers, i.e., 16-mers, between the query genome and genomes in a reference database.
KmerFinder Technical Details
Links Task task_kmerfinder.wdl Software Source Code https://bitbucket.org/genomicepidemiology/kmerfinder Software Documentation https://cge.food.dtu.dk/services/KmerFinder/instructions.php Original Publication(s) Benchmarking of Methods for Genomic TaxonomyAMRFinderPlus: AMR Genotyping (default) NCBI's AMRFinderPlus is the default antimicrobial resistance (AMR) detection tool used in TheiaProk. ResFinder may be used alternatively and if so, AMRFinderPlus is not run.
AMRFinderPlus identifies acquired antimicrobial resistance (AMR) genes, virulence genes, and stress genes. Such AMR genes confer resistance to antibiotics, metals, biocides, heat, or acid. For some taxa (see here), AMRFinderPlus will provide taxa-specific results including filtering out genes that are almost ubiquitous in the taxa (intrinsic genes) and identifying resistance-associated point mutations. In TheiaProk, the taxon used by AMRFinderPlus is specified based on the gambit_predicted_taxon or a user-provided expected_taxon.
You can check if a gene or point mutation is in the AMRFinderPlus database here, find the sequences of reference genes here, and search the query Hidden Markov Models (HMMs) used by AMRFinderPlus to identify AMR genes and some stress and virulence proteins (here). The AMRFinderPlus database is updated frequently. You can ensure you are using the most up-to-date version by specifying the docker image as a workflow input. You might like to save this docker image as a workspace data element to make this easier.
AMRFinderPlus Technical Details
Links Task task_amrfinderplus.wdl Software Source Code amr on GitHub Software Documentation https://github.com/ncbi/amr/wiki Original Publication(s) AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulenceResFinder: AMR Genotyping & Shigella XDR phenotype prediction (alternative) The ResFinder task is an alternative to using AMRFinderPlus for detection and identification of AMR genes and resistance-associated mutations.
This task runs the Centre for Genomic Epidemiology (CGE) ResFinder tool to identify acquired antimicrobial resistance. It can also run the CGE PointFinder tool if the call_pointfinder variable is set with to true. The databases underlying the task are different to those used by AMRFinderPlus.
The default thresholds for calling AMR genes are 90% identity and 50% coverage of the reference genes (expressed as a fraction in workflow inputs: 0.9 & 0.5). These are the same thresholds utilized in BioNumerics for calling AMR genes.
Organisms currently support by PointFinder for mutational-based predicted resistance:
XDR Shigella prediction
The ResFinder Task also has the ability to predict whether or not a sample meets the CDC's definition for extensively drug-resistant (XDR) Shigella.
CDC defines XDR Shigella bacteria as strains that are resistant to all commonly recommended empiric and alternative antibiotics \u2014 azithromycin, ciprofloxacin, ceftriaxone, trimethoprim-sulfamethoxazole (TMP-SMX), and ampicillin. Link to CDC HAN where this definition is found.
A sample is required to meet all 7 criteria in order to be predicted as XDR Shigella
Shigella OR the user must input the word Shigella somewhere within the input String variable called expected_taxon. This requirement serves as the identification of a sample to be of the Shigella genus.There are 3 potential outputs for the resfinder_predicted_xdr_shigella output string:
Not Shigella based on gambit_predicted_taxon or user inputNot XDR Shigella\u00a0for samples identified as Shigella by GAMBIT or user input BUT does ResFinder did not predict resistance to all 6 drugs in XDR definitionXDR Shigella\u00a0meaning the sample was identified as Shigella and ResFinder/PointFinder did predict resistance to ceftriazone, azithromycin, ciprofloxacin, trimethoprim, sulfamethoxazole, and ampicillin.ResFinder Technical Details
Links Task task_resfinder.wdl Software Source Code https://bitbucket.org/genomicepidemiology/resfinder/src/master/ Software Documentation https://bitbucket.org/genomicepidemiology/resfinder/src/master/ ResFinder database https://bitbucket.org/genomicepidemiology/resfinder_db/src/master/ PointFinder database https://bitbucket.org/genomicepidemiology/pointfinder_db/src/master/ Web-server https://cge.food.dtu.dk/services/ResFinder/ Original Publication(s) ResFinder 4.0 for predictions of phenotypes from genotypesTS_MLST: MLST Profiling Multilocus sequence typing (MLST) is a typing method reflecting population structure. It was developed as a portable, unambiguous method for global epidemiology using PCR, but can be applied to whole-genome sequences in silico. MLST is commonly used for pathogen surveillance, ruling out transmission, and grouping related genomes for comparative analysis.
MLST schemes are taxa-specific. Each scheme uses fragments of typically 7 housekeeping genes (\"loci\") and has a database associating an arbitrary number with each distinct allele of each locus. Each unique combination of alleles (\"allelic profile\") is assigned a numbered sequence type (ST). Significant diversification of genomes is captured by changes to the MLST loci via mutational events creating new alleles and STs, or recombinational events replacing the allele and changing the ST. Relationships between STs are based on the number of alleles they share. Clonal complexes share a scheme-specific number of alleles (usually for five of the seven loci).
MLST Limitations
Some taxa have multiple MLST schemes, and some MLST schemes are insufficiently robust.
TheiaProk uses the MLST tool developed by Torsten Seeman to assess MLST using traditional PubMLST typing schemes.
Interpretation of MLST resultsEach MLST results file returns the ST and allele results for one sample. If the alleles and ST are correctly assigned, only a single integer value will be present for each. If an ST cannot be assigned, multiple integers or additional characters will be shown, representing the issues with assignment as described here.
Identifying novel alleles and STsThe MLST schemes used in TheiaProk are curated on the PubMLST website.If you identify novel alleles or allelic profiles in your data using TheiaProk's MLST task, you can get these assigned via PubMLST:
As default, the MLST tool automatically detects the genome's taxa to select the MLST scheme.
Some taxa have multiple MLST schemes, e.g. the Escherichia and Leptospira genera, Acinetobacter baumannii, Clostridium difficile and Streptococcus thermophilus. Only one scheme will be used by default.
Users may specify the scheme as an optional workflow input using the scheme variable of the \"ts_mlst\" task. Available schemes are listed here and the scheme name should be provided in quotation marks (\"\u2026.\").
If results from multiple MLST schemes are required for the same sample, TheiaProk can be run multiple times specifying non-default schemes. After the first run, output attributes for the workflow (i.e. output column names) must be amended to prevent results from being overwritten. Despite re-running the whole workflow, unmodified tasks will return cached outputs, preventing redundant computation.
TS_MLST Technical Details
Links Task task_ts_mlst.wdl Software Source Code mlst Software Documentation mlstProkka: Assembly Annotation (default) Assembly annotation is available via Prokka as default, or alternatively via Bakta. When Prokka annotation is used, Bakta is not.
Prokka is a prokaryotic genome annotation tool used to identify and describe features of interest within the genome sequence. Prokka annotates there genome by querying databases described here.
Prokka Technical Details
Links Task task_prokka.wdl Software Source Code prokka Software Documentation prokka Original Publication(s) Prokka: rapid prokaryotic genome annotationBakta: Assembly Annotation (alternative) Assembly annotation is available via Bakta as an alternative to Prokka. When Bakta annotation is used, Prokka is not.
Bakta is intended for annotation of Bacteria and plasmids only, and is best described here!
Bakta Technical Details
Links Task task_bakta.wdl Software Source Code bakta Software Documentation https://github.com/oschwengers/bakta Original Publication(s) Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identificationPlasmidFinder: Plasmid Identification PlasmidFinder detects plasmids in totally- or partially-sequenced genomes, and identifies the closest plasmid type in the database for typing purposes.
Plasmids are double-stranded circular or linear DNA molecules that are capable of replication independently of the chromosome and may be transferred between different species and clones. Many plasmids contain resistance or virulence genes, though some do not clearly confer an advantage to their host bacterium.
PlasmidFinder Technical Details
Links Task task_plasmidfinder.wdl Software Source Code https://bitbucket.org/genomicepidemiology/plasmidfinder/src/master/ Software Documentation https://bitbucket.org/genomicepidemiology/plasmidfinder/src/master/ Original Publication(s) In Silico Detection and Typing of Plasmids using PlasmidFinder and Plasmid Multilocus Sequence TypingQC_check: Check QC Metrics Against User-Defined Thresholds (optional) The qc_check task compares generated QC metrics against user-defined thresholds for each metric. This task will run if the user provides a qc_check_table .tsv file. If all QC metrics meet the threshold, the qc_check output variable will read QC_PASS. Otherwise, the output will read QC_NA if the task could not proceed or QC_ALERT followed by a string indicating what metric failed.
The qc_check task applies quality thresholds according to the sample taxa. The sample taxa is taken from the gambit_predicted_taxon value inferred by the GAMBIT module OR can be manually provided by the user using the expected_taxon workflow input.
Example Purposes Only
QC threshold values shown are for example purposes only and should not be presumed to be sufficient for every dataset.
QC_Check Technical Details
Links Task task_qc_check_phb.wdlTaxon Tables: Copy outputs to new data tables based on taxonomic assignment (optional) The taxon_tables module, if enabled, will copy sample data to a different data table based on the taxonomic assignment. For example, if an E. coli sample is analyzed, the module will copy the sample data to a new table for E. coli samples or add the sample data to an existing table.
To implement the taxon_tables module, provide a file indicating data table names to copy samples of each taxa to in the taxon_tables input variable. No other input variables are needed.
Formatting the taxon_tables file
The taxon_tables file must be uploaded a Google storage bucket that is accessible by Terra and should be in the format below. Briefly, the bacterial genera or species should be listed in the leftmost column with the name of the data table to copy samples of that taxon to in the rightmost column.
There are no output columns for the taxon table task. The only output of the task is that additional data tables will appear for in the Terra workspace for samples matching a taxa in the taxon_tables file.
Abricate: Mass screening of contigs for antimicrobial and virulence genes (optional) The abricate module, if enabled, will run abricate with the database defined in abricate_db to perform mass screening of contigs for antimicrobial resistance or virulence genes. It comes bundled with multiple databases: NCBI, CARD, ARG-ANNOT, Resfinder, MEGARES, EcOH, PlasmidFinder, Ecoli_VF and VFDB. It only detects acquired resistance genes,\u00a0NOT\u00a0point mutations
The TheiaProk workflows automatically activate taxa-specific sub-workflows after the identification of relevant taxa using GAMBIT. Alternatively, the user can provide the expected taxa in the expected_taxon workflow input to override the taxonomic assignment made by GAMBIT. Modules are launched for all TheiaProk workflows unless otherwise indicated.
A number of approaches are available in TheiaProk for A. baumannii characterization.
Kaptive: Capsule and lipooligosaccharide outer core typing The cell-surface capsular polysaccharide (CPS) of Acinetobacter baumannii can be used as an epidemiological marker. CPS varies in its composition and structure and is a key determinant in virulence and a target for non-antibiotic therapeutics. Specificity for non-antibiotic therapeutics (e.g. phage therapy) bear particular significance given the extent of antibiotic resistance found in this ESKAPE pathogen.
Biosynthesis and export of CPS is encoded by genes clustering at the K locus (KL). Additional genes associated with CPS biosynthesis and export are sometimes found in other chromosomal locations. The full combination of these genes is summarized as a \"K type\", described as a \"predicted serotype associated with the best match locus\". You can read more about this here.
Previously, serotyping of A. baumannii focused on a major immunogenic polysaccharide which was considered the O antigen for the species. This serotyping approach appears to no longer be used and the serotyping scheme has not been updated in over 20 years. Nonetheless, the O-antigen polysaccharide is attached to lipooligosaccharide, and the outer core (OC) of this lipooligosaccharide varies. Biosynthesis of the outer core lipooligosaccharide is encoded by a cluster of genes at the outer core (OC) locus.
Variation in the KL and OCL can be characterized with the Kaptive tool and its associated databases of numbered A. baumannii K and OC locus variants. Kaptive takes in a genome assembly file (fasta), and assigns the K and OC locus to their numbered variants, provides K type and a description of genes in the K or OC loci and elsewhere in the chromosome, alongside metrics for quality of locus match. A description of how Kaptive works, explanations of the full output reports which are provided in the Terra data table by TheiaProk and resources for interpreting outputs are available on the Kaptive Wiki page.
Kaptive Technical Details
Links Task task_kaptive.wdl Software Source Code Kaptive on GitHub Software Documentation https://github.com/katholt/Kaptive/wiki Orginal publications Identification of Acinetobacter baumannii loci for capsular polysaccharide (KL) and lipooligosaccharide outer core (OCL) synthesis in genome assemblies using curated reference databases compatible with KaptiveAn update to the database for Acinetobacter baumannii capsular polysaccharide locus typing extends the extensive and diverse repertoire of genes found at and outside the K locusAcinetobacterPlasmidTyping: Acinetobacter plasmid detection Acinetobacter plasmids are not included in the PlasmidFinder database. Instead, the AcinetobacterPlasmidTyping database contains variants of the plasmid rep gene for A. baumannii plasmid identification. When matched with >/= 95 % identity, this represents a typing scheme for Acinetobacter baumannii plasmids. In TheiaProk, we use the tool abricate to query our assemblies against this database.
The bioinformatics software for querying sample assemblies against the AcinetobacterPlasmidTyping database is Abricate. The WDL task simply runs abricate, and the Acinetobacter Plasmid database and default setting of 95% minimum identity are set in the merlin magic sub-workflow.
AcinetobacterPlasmidTyping Technical Details
Links Task task_abricate.wdl Database and documentation https://github.com/MehradHamidian/AcinetobacterPlasmidTyping Software Source Code and documentation abricate on GitHub Original Publication(s) Detection and Typing of Plasmids in\u00a0Acinetobacter baumannii\u00a0Using\u00a0rep\u00a0Genes Encoding Replication Initiation Proteins Acinetobacter MLSTTwo MLST schemes are available for Acinetobacter. The Pasteur scheme is run by default, given significant problems with the Oxford scheme have been described. Should users with to alternatively or additionally use the Oxford MLST scheme, see the section above on MLST. The Oxford scheme is activated in TheiaProk with the MLST scheme input as \"abaumannii\".
The blaOXA-51-like genes, also known as oxaAB, are considered intrinsic to Acinetobacter baumannii but are not found in other Acinetobacter species. Identification of a blaOXA-51-like gene is therefore considered to confirm the species' identity as A. baumannii.
NCBI's AMRFinderPlus, which is implemented as a core module in TheiaProk, detects the blaOXA-51-like genes. This may be used to confirm the species, in addition to the GAMBIT taxon identification. The blaOXA-51-like genes act as carbapenemases when an ISAba1 is found 7 bp upstream of the gene. Detection of this IS is not currently undertaken in TheiaProk.
"},{"location":"workflows/genomic_characterization/theiaprok/#escherichia-or-shigella","title":"Escherichia or Shigella spp.","text":"The Escherichia and Shigella genera are difficult to differentiate as they do not comply with genomic definitions of genera and species. Consequently, when either Escherichia or Shigella are identified by GAMBIT, all tools intended for these taxa are used.
SerotypeFinder and ECTyper are intended for analysis of E. coli. Both tools are used as there are occasional discrepancies between the serotypes predicted. This primarily arises due to differences in the databases used by each tool.
SerotypeFinder: Serotyping SerotypeFinder, from the Centre for Genomic Epidemiology (CGE), identifies the serotype of total or partially-sequenced isolates of E. coli.
SerotypeFinder Technical Details
Links Task task_serotypefinder.wdl Software Source Code https://bitbucket.org/genomicepidemiology/serotypefinder/src/master/ Software Documentation https://bitbucket.org/genomicepidemiology/serotypefinder/src/master/ Original Publication(s) Rapid and Easy In Silico Serotyping of Escherichia coli Isolates by Use of Whole-Genome Sequencing DataECTyper: Serotyping ECTyper is a serotyping module for E. coli. In TheiaProk, we are using assembly files as input.
ECTyper Technical Details
Links Task task_ectyper.wdl Software Source Code ECTyper on GitHub Software Documentation ECTyper on GitHub Orginal publication ECTyper: in silico Escherichia coli serotype and species prediction from raw and assembled whole-genome sequence dataVirulenceFinder identifies virulence genes in total or partial sequenced isolates of bacteria. Currently, only E. coli is supported in TheiaProk workflows.
VirulenceFinder: Virulence gene identification VirulenceFinder in TheiaProk is only run on assembly files due to issues regarding discordant results when using read files on the web application versus the command-line.
VirulenceFinder Technical Details
Links Task task_virulencefinder.wdl Software Source Code VirulenceFinder Software Documentation VirulenceFinder Original Publication(s) Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic Escherichia coShigaTyper and ShigEiFinder are intended for differentiation and serotype prediction for any Shigella species and Enteroinvasive Escherichia coli (EIEC). You can read about differences between these here and here. ShigEiFinder can be run using either the assembly (default) or reads. These tasks will report if the samples are neither Shigella nor EIEC.
ShigaTyper: Shigella/EIEC differentiation and serotyping for Illumina and ONT only ShigaTyper predicts Shigella spp. serotypes from Illumina or ONT read data. If the genome is not Shigella or EIEC, the results from this tool will state this. In the notes it provides, it also reports on the presence of ipaB which is suggestive of the presence of the \"virulent invasion plasmid\".
ShigaTyper Technical Details
Links Task task_shigatyper.wdl Software Source Code ShigaTyper on GitHub Software Documentation https://github.com/CFSAN-Biostatistics/shigatyper Origin publication In Silico Serotyping Based on Whole-Genome Sequencing Improves the Accuracy of Shigella IdentificationShigEiFinder: Shigella/EIEC differentiation and serotyping using the assembly file as input ShigEiFinder differentiates\u00a0Shigella and enteroinvasive E. coli (EIEC) using cluster-specific genes, identifies some serotypes based on the presence of O-antigen and H-antigen genes, and predicts the number of virulence plasmids. The shigeifinder task operates on assembly files.
ShigEiFinder Technical Details
Links Task task_shigeifinder.wdl Software Source Code ShigEiFinder on GitHub Software Documentation ShigEiFinder on GitHub Origin publication Cluster-specific gene markers enhance Shigella and enteroinvasive Escherichia coli in silico serotypingShigEiFinder_reads: Shigella/EIEC differentiation and serotyping using Illumina read files as input (optional) for Illumina data only ShigEiFinder differentiates\u00a0Shigella and enteroinvasive E. coli (EIEC) using cluster-specific genes, identifies some serotypes based on the presence of O-antigen and H-antigen genes, and predicts the number of virulence plasmids. The shigeifinder_reads task performs on read files.
ShigEiFinder_reads Technical Details
Links Task task_shigeifinder.wdl Software Source Code ShigEiFinder on GitHub Software Documentation ShigEiFinder on GitHub Origin publication Cluster-specific gene markers enhance Shigella and enteroinvasive Escherichia coli in silico serotypingSonneiTyper is run only when GAMBIT predicts the S. sonnei species. This is the most common Shigella species in the United States.
SonneiTyper: Shigella sonnei identification, genotyping, and resistance mutation identification for Illumina and ONT data only SonneiTyper identifies Shigella sonnei, and uses single-nucleotide variants for genotyping and prediction of quinolone resistance in gyrA (S83L, D87G, D87Y) and parC (S80I). Outputs are provided in this format.
SonneiTyper is a wrapper script around another tool, Mykrobe, that analyses the S. sonnei genomes.
SonneiTyper Technical Details
Links Task task_sonneityping.wdl Software Source Code Mykrobe, sonneityping Software Documentation https://github.com/Mykrobe-tools/mykrobe/wiki, sonneityping Original Publication(s) Global population structure and genotyping framework for genomic surveillance of the major dysentery pathogen,\u00a0Shigella sonneiShigella XDR prediction. Please see the documentation section above for ResFinder for details regarding this taxa-specific analysis.
StxTyper: Identification and typing of Shiga toxin (Stx) genes using the assembly file as input StxTyper screens bacterial genome assemblies for shiga toxin genes and subtypes them into known subtypes and also looks for novel subtypes in cases where the detected sequences diverge from the reference sequences.
Shiga toxin is the main virulence factor of Shiga-toxin-producing E. coli (STEC), though these genes are also found in Shigella species as well as some other genera more rarely, such as Klebsiella. Please see this review paper that describes shiga toxins in great detail.
Running StxTyper via the TheiaProk workflows
The TheiaProk workflow will automatically run stxtyper on all E. coli and Shigella spp. samples, but the user can opt-in to running the tool on any sample by setting the optional input variable call_stxtyper to true when configuring the workflow.
Generally, stxtyper looks for stxA and stxB subunits that compose a complete operon. The A subunit is longer (in amino acid length) than the B subunit. Stxtyper attempts to detect these, compare them to a database of known sequences, and type them based on amino acid composition. There typing algorithm and rules defining how to type these genes & operons will be described more completely in a publication that will be available in the future.
The stxtyper_report output TSV is provided in this output format.
Eventually this tool will be incorporated into AMRFinderPlus and will run behind-the-scenes when the user (or in this case, the TheiaProk workflow) provides the amrfinder --organism Escherichia option.
StxTyper Technical Details
Links Task task_stxtyper.wdl Software Source Code ncbi/stxtyper GitHub repository Software Documentation ncbi/stxtyper GitHub repository Original Publication(s) No publication currently available, as this is a new tool. One will be available in the future."},{"location":"workflows/genomic_characterization/theiaprok/#haemophilus-influenzae","title":"Haemophilus influenzae","text":"hicap: Sequence typing Identification of\u00a0cap\u00a0locus serotype in\u00a0Haemophilus influenzae\u00a0assemblies with hicap.
The\u00a0cap\u00a0locus of\u00a0H. influenzae\u00a0is categorised into 6 different groups based on serology (a-f). There are three functionally distinct regions of the\u00a0cap\u00a0locus, designated\u00a0region I,\u00a0region II, and\u00a0region III. Genes within\u00a0region I\u00a0(bexABCD) and\u00a0region III\u00a0(hcsAB) are associated with transport and post-translation modification. The\u00a0region II\u00a0genes encode serotype-specific proteins, with each serotype (a-f) having a distinct set of genes.\u00a0cap\u00a0loci are often subject to structural changes (e.g. duplication, deletion) making the process of\u00a0in silico\u00a0typing and characterisation of loci difficult.
hicap\u00a0automates the identification of the\u00a0cap\u00a0locus, describes the structural layout, and performs\u00a0in silico\u00a0serotyping.
hicap Technical Details
Links Task task_hicap.wdl Software Source Code hicap on GitHub Software Documentation hicap on GitHub Original Publication(s) hicap: In Silico Serotyping of the Haemophilus influenzae Capsule Locus"},{"location":"workflows/genomic_characterization/theiaprok/#klebsiella","title":"Klebsiella spp.","text":"Kleborate: Species identification, MLST, serotyping, AMR and virulence characterization Kleborate is a tool to identify the Klebsiella species, MLST sequence type, serotype, virulence factors (ICEKp and plasmid associated), and AMR genes and mutations. Serotyping is based on the capsular (K antigen) and lipopolysaccharide (LPS) (O antigen) genes. The resistance genes identified by Kleborate are described here.
Kleborate Technical Details
Links Task task_kleborate.wdl Software Source Code kleborate on GitHub Software Documentation https://github.com/katholt/Kleborate/wiki Orginal publication A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complexIdentification of Klebsiella capsule synthesis loci from whole genome data"},{"location":"workflows/genomic_characterization/theiaprok/#legionella-pneumophila","title":"Legionella pneumophila","text":"Legsta: Sequence-based typing Legsta performs a sequence-based typing of Legionella pneumophila, with the intention of being used for outbreak investigations.
Legsta Technical Details
Links Task task_legsta.wdl Software Source Code Legsta Software Documentation Legsta"},{"location":"workflows/genomic_characterization/theiaprok/#listeria-monocytogenes","title":"Listeria monocytogenes","text":"LisSero: Serogroup prediction LisSero performs serogroup prediction (1/2a, 1/2b, 1/2c, or 4b) for Listeria monocytogenes based on the presence or absence of five genes, lmo1118, lmo0737, ORF2110, ORF2819, and prs. These do not predict somatic (O) or flagellar (H) biosynthesis.
LisSero Technical Details
Links Task task_lissero.wdl Software Source Code LisSero Software Documentation LisSero"},{"location":"workflows/genomic_characterization/theiaprok/#mycobacterium-tuberculosis","title":"Mycobacterium tuberculosis","text":"TBProfiler: Lineage and drug susceptibility prediction for Illumina and ONT only TBProfiler identifies Mycobacterium tuberculosis complex species, lineages, sub-lineages and drug resistance-associated mutations.
TBProfiler Technical Details
Links Task task_tbprofiler.wdl Software Source Code TBProfiler on GitHub Software Documentation https://jodyphelan.gitbook.io/tb-profiler/ Original Publication(s) Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugstbp-parser: Interpretation and Parsing of TBProfiler JSON outputs; requires TBProfiler and tbprofiler_additonal_outputs = true tbp-parser adds useful drug resistance interpretation by applying expert rules and organizing the outputs from TBProfiler. Please note that this tool has not been tested on ONT data and although it is available, result accuracy should be considered carefully. To understand this module and its functions, please examine the README found with the source code here.
tbp-parser Technical Details
Links Task task_tbp_parser.wdl Software Source Code tbp-parser Software Documentation tbp-parserClockwork: Decontamination of input read files for Illumina PE only Clockwork decontaminates paired-end data by removing all reads that do not match the H37Rv genome or are unmapped.
Clockwork Technical Details
Links Task task_clockwork.wdl Software Source Code clockwork Software Documentation https://github.com/iqbal-lab-org/clockwork/wiki"},{"location":"workflows/genomic_characterization/theiaprok/#neisseria","title":"Neisseria spp.","text":"ngmaster: Neisseria gonorrhoeae sequence typing NG-MAST is currently the most widely used method for epidemiological surveillance of\u00a0Neisseria gonorrhoea. This tool is targeted at clinical and research microbiology laboratories that have performed WGS of\u00a0N. gonorrhoeae isolates and wish to understand the molecular context of their data in comparison to previously published epidemiological studies. As WGS becomes more routinely performed,\u00a0NGMASTER \u00a0has been developed to completely replace PCR-based NG-MAST, reducing time and labour costs.
The NG-STAR offers a standardized method of classifying seven well-characterized genes associated antimicrobial resistance in N. gonorrhoeae (penA, mtrR, porB, ponA, gyrA, parC and 23S rRNA) to three classes of antibiotics (cephalosporins, macrolides and fluoroquinolones).
ngmaster combines two tools: NG-MAST (in silico multi-antigen sequencing typing) and NG-STAR (sequencing typing for antimicrobial resistance).
ngmaster Technical Details
Links Task task_ngmaster.wdl Software Source Code ngmaster Software Documentation ngmaster Original Publication(s) NGMASTER: in silico multi-antigen sequence typing for Neisseria gonorrhoeaemeningotype: Neisseria meningitidis serotyping This tool performs serotyping, MLST, finetyping (of porA, fetA, and porB), and Bexsero Antigen Sequencing Typing (BAST).
meningotype Technical Details
Links Task task_meningotype.wdl Software Source Code meningotype Software Documentation meningotype"},{"location":"workflows/genomic_characterization/theiaprok/#pseudomonas-aeruginosa","title":"Pseudomonas aeruginosa","text":"pasty: Serotyping pasty is a tool for in silico serogrouping of Pseudomonas aeruginosa isolates. pasty was developed by Robert Petit, based on the PAst tool from the Centre for Genomic Epidemiology.
pasty Technical Details
Links Task task_pasty.wdl Software Source Code pasty Software Documentation pasty Original Publication(s) Application of Whole-Genome Sequencing Data for O-Specific Antigen Analysis and In Silico Serotyping of Pseudomonas aeruginosa Isolates."},{"location":"workflows/genomic_characterization/theiaprok/#salmonella","title":"Salmonella spp.","text":"Both SISTR and SeqSero2 are used for serotyping all Salmonella spp. Occasionally, the predicted serotypes may differ between SISTR and SeqSero2. When this occurs, differences are typically small and analogous, and are likely as a result of differing source databases. More information about Salmonella serovar nomenclature can be found here. For Salmonella Typhi, genotyphi is additionally run for further typing.
SISTR: Salmonella serovar prediction SISTR performs Salmonella spp. serotype prediction using antigen gene and cgMLST gene alleles. In TheiaProk. SISTR is run on genome assemblies, and uses the default database setting (smaller \"centroid\" alleles or representative alleles instead of the full set of cgMLST alleles). It also runs a QC mode to determine the level of confidence in the serovar prediction (see here).
SISTR Technical Details
Links Task task_sistr.wdl Software Source Code SISTR Software Documentation SISTR Original Publication(s) The Salmonella In Silico Typing Resource (SISTR): an open web-accessible tool for rapidly typing and subtyping draft Salmonella genome assemblies.SeqSero2: Serotyping SeqSero2 is a tool for Salmonella serotype prediction. In the TheiaProk Illumina and ONT workflows, SeqSero2 takes in raw sequencing reads and performs targeted assembly of serotype determinant alleles, which can be used to predict serotypes including contamination between serotypes. Optionally, SeqSero2 can take the genome assembly as input.
SeqSero2 Technical Details
Links Task task_seqsero2.wdl Software Source Code SeqSero2 Software Documentation SeqSero2 Original Publication(s) Salmonella serotype determination utilizing high-throughput genome sequencing data.SeqSero2: rapid and improved Salmonella serotype determination using whole genome sequencing data.genotyphi: Salmonella Typhi lineage, clade, subclade and plasmid typing, AMR prediction for Illumina and ONT only genotyphi is activated upon identification of the \"Typhi\" serotype by SISTR or SeqSero2. genotyphi divides the Salmonella enterica serovar Typhi population into detailed lineages, clades, and subclades. It also detects mutations in the quinolone-resistance determining regions, acquired antimicrobial resistance genes, plasmid replicons, and subtypes of the IncHI1 plasmid which is associated with multidrug resistance.
TheiaProk uses the Mykrobe implementation of genotyphi that takes raw sequencing reads as input.
genotyphi Technical Details
Links Task task_genotyphi.wdl Software Source Code genotyphi Software Documentation https://github.com/katholt/genotyphi/blob/main/README.md#mykrobe-implementation Orginal publication An extended genotyping framework for Salmonella enterica serovar Typhi, the cause of human typhoidFive Years of GenoTyphi: Updates to the Global Salmonella Typhi Genotyping Framework"},{"location":"workflows/genomic_characterization/theiaprok/#staphyloccocus-aureus","title":"Staphyloccocus aureus","text":"spatyper: Sequence typing Given a fasta file or multiple fasta files, this script identifies the repeats and the order and generates a spa type. The repeat sequences and repeat orders found on\u00a0http://spaserver2.ridom.de/ are used to identify the spa type of each enriched sequence. Ridom spa type and the genomics repeat sequence are then reported back to the user.
spatyper Technical Details
Links Task task_spatyper.wdl Software Source Code spatyper Software Documentation spatyperstaphopia-sccmec: Sequence typing This tool assigns a SCCmec type by BLAST the SCCmec primers against an assembly. staphopia-sccmecreports\u00a0True for exact primer matches and\u00a0False for at least 1 base pair difference. The Hamming Distance is also reported.
staphopia-sccmec Technical Details
Links Task task_staphopiasccmec.wdl Software Source Code staphopia-sccmec Software Documentation staphopia-sccmec Original Publication(s) Staphylococcus aureus viewed from the perspective of 40,000+ genomesagrvate: Sequence typing This tool identifies the agr locus type and reports possible variants in the agr operon. AgrVATE accepts a\u00a0S. aureus genome assembly as input and performs a kmer search using an Agr-group specific kmer database to assign the Agr-group. The\u00a0agr operon is then extracted using\u00a0in-silico PCR and variants are called using an Agr-group specific reference operon.
agrvate Technical Details
Links Task task_agrvate.wdl Software Source Code agrVATE Software Documentation agrVATE Original Publication(s) Species-Wide Phylogenomics of the Staphylococcus aureus Agr Operon Revealed Convergent Evolution of Frameshift Mutations"},{"location":"workflows/genomic_characterization/theiaprok/#streptococcus-pneumoniae","title":"Streptococcus pneumoniae","text":"PopPUNK: Global Pneumococcal Sequence Cluster typing Global Pneumococcal Sequence Clusters (GPSC) define and name pneumococcal strains. GPSC designation is undertaken using the PopPUNK software and GPSC database as described in the file below, obtained from here.
:file: GPSC_README_PopPUNK2.txt
Interpreting GPSC results
*_external_clusters.csv novel clusters are assigned NA. For isolates that are assigned a novel cluster and pass QC, you can email\u00a0globalpneumoseq@gmail.com\u00a0to have these novel clusters added to the database.PopPUNK Technical Details
Links Task task_poppunk_streppneumo.wdl GPSC database https://www.pneumogen.net/gps/#/training#command-line Software Source Code PopPunk Software Documentation https://poppunk.readthedocs.io/en/latest/ Original Publication(s) Fast and flexible bacterial genomic epidemiology with PopPUNKSeroBA: Serotyping for Illumina_PE only Streptococcus pneumoniae serotyping is performed with SeroBA.
SeroBA Technical Details
Links Task task_seroba.wdl Software Source Code SeroBA Software Documentation https://sanger-pathogens.github.io/seroba/ Original Publication(s) SeroBA: rapid high-throughput serotyping of Streptococcus pneumoniae from whole genome sequence datapbptyper: Penicillin-binding protein genotyping The Penicillin-binding proteins (PBP) are responsible for the minimum inhibitory concentration phenotype for beta-lactam antibiotic. In Streptococcus pneumoniae, these PBP genes can be identified and typed with PBPTyper.
pbptyper Technical Details
Links Task task_pbptyper.wdl Software Source Code pbptyper Software Documentation pbptyper Original Publication(s) Penicillin-binding protein transpeptidase signatures for tracking and predicting \u03b2-lactam resistance levels in Streptococcus pneumoniae"},{"location":"workflows/genomic_characterization/theiaprok/#streptococcus-pyogenes","title":"Streptococcus pyogenes","text":"emm-typing-tool: Sequence typing for Illumina_PE only emm-typing of Streptococcus pyogenes raw reads. Assign emm type and subtype by querying the CDC M-type specific database.
emm-typing-tool Technical Details
Links Task task_emmtypingtool.wdl Software Source Code emm-typing-tool Software Documentation emm-typing-tool"},{"location":"workflows/genomic_characterization/theiaprok/#vibrio","title":"Vibrio spp.","text":"SRST2: Vibrio characterization for Illumina only The SRST2 Vibrio characterization task detects sequences for Vibrio spp. characterization using Illumina sequence reads and a database of target sequence that are traditionally used in PCR methods. The sequences included in the database are as follows:
SRST2 Technical Details
Links Task task_srst2_vibrio.wdl Software Source Code srst2 Software Documentation srst2 Database Description Docker containerAbricate: Vibrio characterization The Abricate Vibrio characterization task detects sequences for Vibrio spp. characterization using genome assemblies and the abricate \"vibrio\" database. The sequences included in the database are as follows:
Abricate Technical Details
Links Task task_abricate_vibrio.wdl Software Source Code abricate Software Documentation abricate Database Description Docker container"},{"location":"workflows/genomic_characterization/theiaprok/#outputs","title":"Outputs","text":"Variable Type Description Workflow abricate_abaum_database String Database of reference A. baumannii plasmid typing genes used for plasmid typing FASTA, ONT, PE, SE abricate_abaum_docker String Docker file used for running abricate FASTA, ONT, PE, SE abricate_abaum_plasmid_tsv File https://github.com/tseemann/abricate#output containing a row for each A. baumannii plasmid type gene found in the sample FASTA, ONT, PE, SE abricate_abaum_plasmid_type_genes String A. baumannii Plasmid typing genes found in the sample; from GENE column in https://github.com/tseemann/abricate#output FASTA, ONT, PE, SE abricate_abaum_version String Version of abricate used for A. baumannii plasmid typing FASTA, ONT, PE, SE abricate_database String Database of reference used with Abricate FASTA, ONT, PE, SE abricate_docker String Docker file used for running abricate FASTA, ONT, PE, SE abricate_genes String Genes found in the sample; from GENE column in https://github.com/tseemann/abricate#output FASTA, ONT, PE, SE abricate_results_tsv File https://github.com/tseemann/abricate#output containing a row for each gene found in the sample FASTA, ONT, PE, SE abricate_version String Version of abricate used for A. baumannii plasmid typing FASTA, ONT, PE, SE abricate_vibrio_biotype String Biotype classification according to tcpA gene sequence (Classical or ElTor) FASTA, ONT, PE, SE abricate_vibrio_ctxA String Presence or absence of the ctxA gene FASTA, ONT, PE, SE abricate_vibrio_detailed_tsv File Detailed ABRicate output file FASTA, ONT, PE, SE abricate_vibrio_ompW String Presence or absence of the ompW gene FASTA, ONT, PE, SE abricate_vibrio_serogroup String Serotype classification as O1 (wbeN gene), O139 (wbfR gene) or not detected. FASTA, ONT, PE, SE abricate_vibrio_toxR String Presence or absence of the toxR gene FASTA, ONT, PE, SE abricate_vibrio_version String The abricate version run FASTA, ONT, PE, SE agrvate_agr_canonical String Canonical or non-canonical agrD FASTA, ONT, PE, SE agrvate_agr_group String Agr group FASTA, ONT, PE, SE agrvate_agr_match_score String Match score for agr group FASTA, ONT, PE, SE agrvate_agr_multiple String If multiple agr groups were found FASTA, ONT, PE, SE agrvate_agr_num_frameshifts String Number of frameshifts found in CDS of extracted agr operon FASTA, ONT, PE, SE agrvate_docker String The docker used for AgrVATE FASTA, ONT, PE, SE agrvate_results File A gzipped tarball of all results FASTA, ONT, PE, SE agrvate_summary File The summary file produced FASTA, ONT, PE, SE agrvate_version String The version of AgrVATE used FASTA, ONT, PE, SE amrfinderplus_all_report File Output TSV file from AMRFinderPlus (described https://github.com/ncbi/amr/wiki/Running-AMRFinderPlus#fields) FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_betalactam_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to beta-lactams FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_carbapenem_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to carbapenem FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_cephalosporin_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to cephalosporin FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_cephalothin_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to cephalothin FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_genes String Beta-lactam AMR genes identified by AMRFinderPlus FASTA, ONT, PE, SE amrfinderplus_amr_betalactam_methicillin_genes String Beta-lactam AMR genes identified by AMRFinderPlus that are known to confer resistance to methicilin FASTA, ONT, PE, SE amrfinderplus_amr_classes String AMRFinderPlus predictions for classes of drugs that genes found in the reads are known to confer resistance to FASTA, ONT, PE, SE amrfinderplus_amr_core_genes String AMR genes identified by AMRFinderPlus where the scope is \"core\" FASTA, ONT, PE, SE amrfinderplus_amr_plus_genes String AMR genes identified by AMRFinderPlus where the scope is \"plus\" FASTA, ONT, PE, SE amrfinderplus_amr_report File TSV file detailing AMR genes only, from the amrfinderplus_all_report FASTA, ONT, PE, SE amrfinderplus_amr_subclasses String More specificity about the drugs that genes identified in the reads confer resistance to FASTA, ONT, PE, SE amrfinderplus_db_version String AMRFinderPlus database version used FASTA, ONT, PE, SE amrfinderplus_stress_genes String Stress genes identified by AMRFinderPlus FASTA, ONT, PE, SE amrfinderplus_stress_report File TSV file detailing stress genes only, from the amrfinderplus_all_report FASTA, ONT, PE, SE amrfinderplus_version String AMRFinderPlus version used FASTA, ONT, PE, SE amrfinderplus_virulence_genes String Virulence genes identified by AMRFinderPlus FASTA, ONT, PE, SE amrfinderplus_virulence_report File TSV file detailing virulence genes only, from the amrfinderplus_all_report FASTA, ONT, PE, SE ani_highest_percent Float Highest ANI between query and any given reference genome (top species match) FASTA, ONT, PE, SE ani_highest_percent_bases_aligned Float Percentage of bases aligned between query genome and top species match FASTA, ONT, PE, SE ani_mummer_docker String Docker image used to run the ANI_mummer task FASTA, ONT, PE, SE ani_mummer_version String Version of MUMmer used FASTA, ONT, PE, SE ani_output_tsv File Full output TSV from ani-m FASTA, ONT, PE, SE ani_top_species_match String Species of genome with highest ANI to query FASTA FASTA, ONT, PE, SE assembly_fasta File https://github.com/tseemann/shovill#contigsfa ONT, PE, SE assembly_length Int Length of assembly (total contig length) as determined by QUAST FASTA, ONT, PE, SE bakta_gbff File Genomic GenBank format annotation file FASTA, ONT, PE, SE bakta_gff3 File Generic Feature Format Version 3 file FASTA, ONT, PE, SE bakta_summary File Bakta summary output TXT file FASTA, ONT, PE, SE bakta_tsv File Annotations as simple human readable TSV FASTA, ONT, PE, SE bakta_version String Bakta version used FASTA, ONT, PE, SE bbduk_docker String BBDuk docker image used PE, SE busco_database String BUSCO database used FASTA, ONT, PE, SE busco_docker String BUSCO docker image used FASTA, ONT, PE, SE busco_report File A plain text summary of the results in BUSCO notation FASTA, ONT, PE, SE busco_results String BUSCO results (see https://www.notion.so/TheiaProk-Workflow-Series-68c34aca2a0240ef94fef0acd33651b9?pvs=21) FASTA, ONT, PE, SE busco_version String BUSCO software version used FASTA, ONT, PE, SE cg_pipeline_docker String Docker file used for running CG-Pipeline on cleaned reads PE, SE cg_pipeline_report_clean File TSV file of read metrics from clean reads, including average read length, number of reads, and estimated genome coverage PE, SE cg_pipeline_report_raw File TSV file of read metrics from raw reads, including average read length, number of reads, and estimated genome coverage PE, SE clockwork_decontaminated_read1 File Decontaminated forward reads by Clockwork PE clockwork_decontaminated_read2 File Decontaminated reverse reads by Clockwork PE combined_mean_q_clean Float Mean quality score for the combined clean reads PE combined_mean_q_raw Float Mean quality score for the combined raw reads PE combined_mean_readlength_clean Float Mean read length for the combined clean reads PE combined_mean_readlength_raw Float Mean read length for the combined raw reads PE contigs_fastg File Assembly graph if megahit used for genome assembly PE contigs_gfa File Assembly graph if spades used for genome assembly ONT, PE, SE contigs_lastgraph File Assembly graph if velvet used for genome assembly PE dragonflye_version String Version of dragonflye used for de novo assembly ONT ectyper_predicted_serotype String Serotype predicted by ECTyper FASTA, ONT, PE, SE ectyper_results File TSV file of evidence for ECTyper predicted serotype (see https://github.com/phac-nml/ecoli_serotyping#report-format) FASTA, ONT, PE, SE ectyper_version String Version of ECTyper used FASTA, ONT, PE, SE emmtypingtool_docker String Docker image for emm-typing-tool PE emmtypingtool_emm_type String emm-type predicted PE emmtypingtool_results_xml File XML file with emm-typing-tool resuls PE emmtypingtool_version String Version of emm-typing-tool used PE est_coverage_clean Float Estimated coverage calculated from clean reads and genome length ONT, PE, SE est_coverage_raw Float Estimated coverage calculated from raw reads and genome length ONT, PE, SE fastp_html_report File The HTML report made with fastp PE, SE fastp_version String Version of fastp software used PE, SE fastq_scan_clean1_json File JSON file output fromfastq-scan containing summary stats about clean forward read quality and length PE, SE fastq_scan_clean2_json File JSON file output from fastq-scan containing summary stats about clean reverse read quality and length PE fastq_scan_num_reads_clean_pairs String Number of read pairs after cleaning as calculated by fastq_scan PE fastq_scan_num_reads_clean1 Int Number of forward reads after cleaning as calculated by fastq_scan PE, SE fastq_scan_num_reads_clean2 Int Number of reverse reads after cleaning as calculated by fastq_scan PE fastq_scan_num_reads_raw_pairs String Number of input read pairs calculated by fastq_scan PE fastq_scan_num_reads_raw1 Int Number of input forward reads calculated by fastq_scan PE, SE fastq_scan_num_reads_raw2 Int Number of input reverse reads calculated by fastq_scan PE fastq_scan_raw1_json File JSON file output from fastq-scan containing summary stats about raw forward read quality and length PE, SE fastq_scan_raw2_json File JSON file output from fastq-scan containing summary stats about raw reverse read quality and length PE fastq_scan_version String Version of fastq-scan software used PE, SE fastqc_clean1_html File Graphical visualization of clean forward read quality from fastqc to open in an internet browser PE, SE fastqc_clean2_html File Graphical visualization of clean reverse read quality from fastqc to open in an internet browser PE fastqc_docker String Docker container used with fastqc PE, SE fastqc_num_reads_clean_pairs String Number of read pairs after cleaning by fastqc PE fastqc_num_reads_clean1 Int Number of forward reads after cleaning by fastqc PE, SE fastqc_num_reads_clean2 Int Number of reverse reads after cleaning by fastqc PE fastqc_num_reads_raw_pairs String Number of input read pairs by fastqc PE fastqc_num_reads_raw1 Int Number of input reverse reads by fastqc PE, SE fastqc_num_reads_raw2 Int Number of input reverse reads by fastqc PE fastqc_raw1_html File Graphical visualization of raw forward read quality from fastqc to open in an internet browser PE, SE fastqc_raw2_html File Graphical visualization of raw reverse read qualityfrom fastqc to open in an internet browser PE fastqc_version String Version of fastqc software used PE, SE gambit_closest_genomes File CSV file listing genomes in the GAMBIT database that are most similar to the query assembly FASTA, ONT, PE, SE gambit_db_version String Version of GAMBIT used FASTA, ONT, PE, SE gambit_docker String GAMBIT docker file used FASTA, ONT, PE, SE gambit_predicted_taxon String Taxon predicted by GAMBIT FASTA, ONT, PE, SE gambit_predicted_taxon_rank String Taxon rank of GAMBIT taxon prediction FASTA, ONT, PE, SE gambit_report File GAMBIT report in a machine-readable format FASTA, ONT, PE, SE gambit_version String Version of GAMBIT software used FASTA, ONT, PE, SE genotyphi_final_genotype String Final genotype call from GenoTyphi ONT, PE, SE genotyphi_genotype_confidence String Confidence in the final genotype call made by GenoTyphi ONT, PE, SE genotyphi_mykrobe_json File JSON file of GenoTyphi output, described https://github.com/katholt/genotyphi#explanation-of-columns-in-the-output ONT, PE, SE genotyphi_report_tsv File TSV file of GenoTyphi output, described https://github.com/katholt/genotyphi#explanation-of-columns-in-the-output ONT, PE, SE genotyphi_species String Species call from Mykrobe, used to run GenoTyphi ONT, PE, SE genotyphi_st_probes_percent_coverage Float Percentage coverage to the Typhi MLST probes ONT, PE, SE genotyphi_version String Version of GenoTyphi used ONT, PE, SE hicap_docker String Docker image used for hicap ONT, PE, SE hicap_genes String cap\u00a0genes identified. genes on different contigs delimited by;. truncation shown by trailing\u00a0* ONT, PE, SE hicap_results_tsv File TSV file of hicap output ONT, PE, SE hicap_serotype String hicap serotype ONT, PE, SE hicap_version String hicap version used ONT, PE, SE kaptive_k_locus String Best matching K locus identified by Kaptive FASTA, ONT, PE, SE kaptive_k_type String Best matching K type identified by Kaptive FASTA, ONT, PE, SE kaptive_kl_confidence String Kaptive\u2019s confidence in the KL match (see https://github.com/katholt/Kaptive/wiki/Interpreting-the-results) FASTA, ONT, PE, SE kaptive_oc_locus String Best matching K locus identified by Kaptive FASTA, ONT, PE, SE kaptive_ocl_confidence String Kaptive\u2019s confidence in the OCL match (see https://github.com/katholt/Kaptive/wiki/Interpreting-the-results) FASTA, ONT, PE, SE kaptive_output_file_k File TSV https://github.com/katholt/Kaptive/wiki/How-to-run#output-filesfrom the K locus from Kaptive FASTA, ONT, PE, SE kaptive_output_file_oc File TSV https://github.com/katholt/Kaptive/wiki/How-to-run#output-filesfrom the OC locus from Kaptive FASTA, ONT, PE, SE kaptive_version String Version of Kaptive used FASTA, ONT, PE, SE kleborate_docker String Kleborate docker image used FASTA, ONT, PE, SE kleborate_genomic_resistance_mutations String Genomic resistance mutations identifies by Kleborate FASTA, ONT, PE, SE kleborate_key_resistance_genes String Key resistance genes identified by Kleborate FASTA, ONT, PE, SE kleborate_klocus String Best matching K locus identified by Kleborate via Kaptive FASTA, ONT, PE, SE kleborate_klocus_confidence String Kaptive\u2019s confidence in the KL match (see https://github.com/katholt/Kaptive/wiki/Interpreting-the-results) FASTA, ONT, PE, SE kleborate_ktype String Best matching K type identified by Kleborate via Kaptive FASTA, ONT, PE, SE kleborate_mlst_sequence_type String https://github.com/katholt/Kleborate/wiki/MLST#multi-locus-sequence-typing-mlst call by Kleborate FASTA, ONT, PE, SE kleborate_olocus String Best matching OC locus identified by Kleborate via Kaptive FASTA, ONT, PE, SE kleborate_olocus_confidence String Kaptive\u2019s confidence in the KL match (see https://github.com/katholt/Kaptive/wiki/Interpreting-the-results) FASTA, ONT, PE, SE kleborate_otype String Best matching OC type identified by Kleborate via Kaptive FASTA, ONT, PE, SE kleborate_output_file File https://github.com/katholt/Kleborate/wiki/Scores-and-counts FASTA, ONT, PE, SE kleborate_resistance_score String Resistance score as given by kleborate FASTA, ONT, PE, SE kleborate_version String Version of Kleborate used FASTA, ONT, PE, SE kleborate_virulence_score String Virulence score as given by kleborate FASTA, ONT, PE, SE kmerfinder_database String Database used to run KmerFinder FASTA, ONT, PE, SE kmerfinder_docker String Docker image used to run KmerFinder FASTA, ONT, PE, SE kmerfinder_query_coverage String KmerFinder\u2019s query coverage of the top hit result FASTA, ONT, PE, SE kmerfinder_results_tsv File Output TSV file created by KmerFinder FASTA, ONT, PE, SE kmerfinder_template_coverage String FASTA, ONT, PE, SE kmerfinder_top_hit String Top hit species of KmerFinder FASTA, ONT, PE, SE kraken2_database String Kraken2 database used for the taxonomic assignment ONT, PE, SE kraken2_docker String Docker container for Kraken2 ONT, PE, SE kraken2_report File Report, in text format, of Kraken2 results ONT, PE, SE kraken2_version String Kraken2 version ONT, PE, SE legsta_predicted_sbt String Sequence based type predicted by Legsta FASTA, ONT, PE, SE legsta_results File TSV file of legsta results (see https://github.com/tseemann/legsta#output) FASTA, ONT, PE, SE legsta_version String Version of legsta used FASTA, ONT, PE, SE lissero_results File TSV results file from LisSero (see https://github.com/MDU-PHL/LisSero#example-output) FASTA, ONT, PE, SE lissero_serotype String Serotype predicted by LisSero FASTA, ONT, PE, SE lissero_version String Version of LisSero used FASTA, ONT, PE, SE meningotype_BAST String BAST type FASTA, ONT, PE, SE meningotype_FetA String FetA type FASTA, ONT, PE, SE meningotype_fHbp String fHbp type FASTA, ONT, PE, SE meningotype_NadA String NBA type FASTA, ONT, PE, SE meningotype_NHBA String NHBA type FASTA, ONT, PE, SE meningotype_PorA String PorA type FASTA, ONT, PE, SE meningotype_PorB String PorB type FASTA, ONT, PE, SE meningotype_serogroup String Serogroup FASTA, ONT, PE, SE meningotype_tsv File Full result file FASTA, ONT, PE, SE meningotype_version String Version of meningotype used FASTA, ONT, PE, SE midas_docker String MIDAS docker image used PE, SE midas_primary_genus String Genus of most abundant species in reads PE, SE midas_report File TSV report of full MIDAS results PE, SE midas_secondary_genus String Genus of the next most abundant species after removing all species of the most abundant genus PE, SE midas_secondary_genus_abundance String Relative abundance of secondary genus PE, SE midas_secondary_genus_coverage String Absolute coverage of secondary genus PE, SE n50_value Int N50 of assembly calculated by QUAST FASTA, ONT, PE, SE nanoplot_docker String Docker image for nanoplot ONT nanoplot_html_clean File Clean read file ONT nanoplot_html_raw File Raw read file ONT nanoplot_num_reads_clean1 Int Number of clean reads ONT nanoplot_num_reads_raw1 Int Number of raw reads ONT nanoplot_r1_est_coverage_clean Float Estimated coverage on the clean reads by nanoplot ONT nanoplot_r1_est_coverage_raw Float Estimated coverage on the raw reads by nanoplot ONT nanoplot_r1_mean_q_clean Float Mean quality score of clean forward reads ONT nanoplot_r1_mean_q_raw Float Mean quality score of raw forward reads ONT nanoplot_r1_mean_readlength_clean Float Mean read length of clean forward reads ONT nanoplot_r1_mean_readlength_raw Float Mean read length of raw forward reads ONT nanoplot_r1_median_q_clean Float Median quality score of clean forward reads ONT nanoplot_r1_median_q_raw Float Median quality score of raw forward reads ONT nanoplot_r1_median_readlength_clean Float Median read length of clean forward reads ONT nanoplot_r1_median_readlength_raw Float Median read length of raw forward reads ONT nanoplot_r1_n50_clean Float N50 of clean forward reads ONT nanoplot_r1_n50_raw Float N50 of raw forward reads ONT nanoplot_r1_stdev_readlength_clean Float Standard deviation read length of clean forward reads ONT nanoplot_r1_stdev_readlength_raw Float Standard deviation read length of raw forward reads ONT nanoplot_tsv_clean File Output TSV file created by nanoplot ONT nanoplot_tsv_raw File Output TSV file created by nanoplot ONT nanoplot_version String Version of nanoplot used for analysis ONT nanoq_version String Version of nanoq used in analysis ONT ngmaster_ngmast_porB_allele String porB allele number FASTA, ONT, PE, SE ngmaster_ngmast_sequence_type String NG-MAST sequence type FASTA, ONT, PE, SE ngmaster_ngmast_tbpB_allele String tbpB allele number FASTA, ONT, PE, SE ngmaster_ngstar_23S_allele String 23S rRNA allele number FASTA, ONT, PE, SE ngmaster_ngstar_gyrA_allele String gyrA allele number FASTA, ONT, PE, SE ngmaster_ngstar_mtrR_allele String mtrR allele number FASTA, ONT, PE, SE ngmaster_ngstar_parC_allele String parC allele number FASTA, ONT, PE, SE ngmaster_ngstar_penA_allele String penA allele number FASTA, ONT, PE, SE ngmaster_ngstar_ponA_allele String ponA allele number FASTA, ONT, PE, SE ngmaster_ngstar_porB_allele String porB allele number FASTA, ONT, PE, SE ngmaster_ngstar_sequence_type String NG-STAR sequence type FASTA, ONT, PE, SE ngmaster_tsv File TSV file with NG-MAST/NG-STAR typing FASTA, ONT, PE, SE ngmaster_version String ngmaster version FASTA, ONT, PE, SE number_contigs Int Total number of contigs in assembly FASTA, ONT, PE, SE pasty_all_serogroups File TSV file with details of each serogroup from pasty (see https://github.com/rpetit3/pasty#example-prefixdetailstsv) FASTA, ONT, PE, SE pasty_blast_hits File TSV file of BLAST hits from pasty (see https://github.com/rpetit3/pasty#example-prefixblastntsv) FASTA, ONT, PE, SE pasty_comment String FASTA, ONT, PE, SE pasty_docker String pasty docker image used FASTA, ONT, PE, SE pasty_serogroup String Serogroup predicted by pasty FASTA, ONT, PE, SE pasty_serogroup_coverage Float The breadth of coverage of the O-antigen by pasty FASTA, ONT, PE, SE pasty_serogroup_fragments Int Number of BLAST hits included in the prediction (fewer is better) FASTA, ONT, PE, SE pasty_summary_tsv File TSV summary file of pasty outputs (see https://github.com/rpetit3/pasty#example-prefixtsv) FASTA, ONT, PE, SE pasty_version String Version of pasty used FASTA, ONT, PE, SE pbptyper_docker String pbptyper docker image used FASTA, ONT, PE, SE pbptyper_pbptype_predicted_tsv File TSV file of pbptyper results (see https://github.com/rpetit3/pbptyper#example-prefixtsv) FASTA, ONT, PE, SE pbptyper_predicted_1A_2B_2X String PBP type predicted by pbptyper FASTA, ONT, PE, SE pbptyper_version String Version of pbptyper used FASTA, ONT, PE, SE plasmidfinder_db_version String Version of PlasmidFnder used FASTA, ONT, PE, SE plasmidfinder_docker String PlasmidFinder docker image used FASTA, ONT, PE, SE plasmidfinder_plasmids String Names of plasmids identified by PlasmidFinder FASTA, ONT, PE, SE plasmidfinder_results File Output file from PlasmidFinder in TSV format FASTA, ONT, PE, SE plasmidfinder_seqs File Hit_in_genome_seq.fsa file produced by PlasmidFinder FASTA, ONT, PE, SE poppunk_docker String PopPUNK docker image with GPSC database used FASTA, ONT, PE, SE poppunk_gps_cluster String GPS cluster predicted by PopPUNK FASTA, ONT, PE, SE poppunk_GPS_db_version String Version of GPSC database used FASTA, ONT, PE, SE poppunk_gps_external_cluster_csv File GPSC v6 scheme designations FASTA, ONT, PE, SE poppunk_version String Version of PopPUNK used FASTA, ONT, PE, SE prokka_gbk File GenBank file produced from Prokka annotation of input FASTA FASTA, ONT, PE, SE prokka_gff File Prokka output GFF3 file containing sequence and annotation (you can view this in IGV) FASTA, ONT, PE, SE prokka_sqn File A Sequin file for GenBank submission FASTA, ONT, PE, SE qc_check String A string that indicates whether or not the sample passes a set of pre-determined and user-provided QC thresholds FASTA, ONT, PE, SE qc_standard File The user-provided file that contains the QC thresholds used for the QC check FASTA, ONT, PE, SE quast_gc_percent Float The GC percent of your sample FASTA, ONT, PE, SE quast_report File TSV report from QUAST FASTA, ONT, PE, SE quast_version String Software version of QUAST used FASTA, ONT, PE, SE r1_mean_q_clean Float Mean quality score of clean forward reads PE, SE r1_mean_q_raw Float Mean quality score of raw forward reads PE, SE r1_mean_readlength_clean Float Mean read length of clean forward reads PE, SE r1_mean_readlength_raw Float Mean read length of raw forward reads PE, SE r2_mean_q_clean Float Mean quality score of clean reverse reads PE r2_mean_q_raw Float Mean quality score of raw reverse reads PE r2_mean_readlength_clean Float Mean read length of clean reverse reads PE r2_mean_readlength_raw Float Mean read length of raw reverse reads PE rasusa_version String Version of RASUSA used for analysis ONT read_screen_clean String PASS or FAIL result from clean read screening; FAIL accompanied by the reason for failure ONT, PE, SE read_screen_raw String PASS or FAIL result from raw read screening; FAIL accompanied by thereason for failure ONT, PE, SE read1_clean File Clean forward reads file ONT, PE, SE read2_clean File Clean reverse reads file PE resfinder_db_version String Version of ResFinder database FASTA, ONT, PE, SE resfinder_docker String ResFinder docker image used FASTA, ONT, PE, SE resfinder_pheno_table File Table containing al AMR phenotypes FASTA, ONT, PE, SE resfinder_pheno_table_species File Table with species-specific AMR phenotypes FASTA, ONT, PE, SE resfinder_pointfinder_pheno_table File TSV showing presence(1)/absence(0) of predicted resistance against an antibiotic class FASTA, ONT, PE, SE resfinder_pointfinder_results File Predicted point mutations, grouped by the gene they occur in FASTA, ONT, PE, SE resfinder_predicted_pheno_resistance String Semicolon delimited list of antimicrobial drugs and associated genes and/or point mutations.\u00a0: , , ; : , ; FASTA, ONT, PE, SE resfinder_predicted_resistance_Amp String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Ampicillin based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Axo String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Ceftriaxone based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Azm String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Azithromycin based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Cip String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Ciprofloxacin based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Smx String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Sulfamethoxazole based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_resistance_Tmp String States either\u00a0Resistance\u00a0or\u00a0No Resistance predicted\u00a0to Trimothoprim based on resfinder phenotypic predictions FASTA, ONT, PE, SE resfinder_predicted_xdr_shigella String Final prediction of XDR Shigella status based on CDC definition. Explanation can be found in the description above this table. FASTA, ONT, PE, SE resfinder_results File Predicted resistance genes grouped by antibiotic class FASTA, ONT, PE, SE resfinder_seqs File FASTA of resistance gene sequences from user\u2019s input sequence FASTA, ONT, PE, SE seq_platform String Sequencing platform input by the user FASTA, ONT, PE, SE seqsero2_predicted_antigenic_profile String Antigenic profile predicted for Salmonella spp. by SeqSero2 ONT, PE, SE seqsero2_predicted_contamination String Indicates whether contamination between Salmonella with different serotypes was predicted by SeqSero2 ONT, PE, SE seqsero2_predicted_serotype String Serotype predicted by SeqSero2 ONT, PE, SE seqsero2_report File TSV report produced by SeqSero2 ONT, PE, SE seqsero2_version String Version of SeqSero2 used ONT, PE, SE seroba_ariba_identity String Percentage identity between the query sequence and ARIBA-predicted serotype PE seroba_ariba_serotype String Serotype predicted by ARIBA, via SeroBA PE seroba_details File Detailed TSV file from SeroBA PE seroba_docker String SeroBA docker image used PE seroba_serotype String Serotype predicted by SeroBA PE seroba_version String SeroBA version used PE serotypefinder_docker String SerotypeFinder docker image used FASTA, ONT, PE, SE serotypefinder_report File TSV report produced by SerotypeFinder FASTA, ONT, PE, SE serotypefinder_serotype String Serotype predicted by SerotypeFinder FASTA, ONT, PE, SE shigatyper_docker String ShigaTyper docker image used ONT, PE, SE shigatyper_hits_tsv File Detailed TSV report from ShigaTyper (seehttps://github.com/CFSAN-Biostatistics/shigatyper#example-prefix-hitstsv) ONT, PE, SE shigatyper_ipaB_presence_absence String Presence (+) or absence (-) of ipaB identified by ShigaTyper ONT, PE, SE shigatyper_notes String Any notes output from ShigaTyper ONT, PE, SE shigatyper_predicted_serotype String Serotype predicted by ShigaTyper ONT, PE, SE shigatyper_summary_tsv File TSV summary report from ShigaTyper (see https://github.com/CFSAN-Biostatistics/shigatyper#example-prefixtsv) ONT, PE, SE shigatyper_version String Version of ShigaTyper used ONT, PE, SE shigeifinder_cluster String Shigella/EIEC cluster identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_cluster_reads String Shigella/EIEC cluster identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_docker String ShigEiFinder docker image used FASTA, ONT, PE, SE shigeifinder_docker_reads String ShigEiFinder docker image used using read files as inputs PE, SE shigeifinder_H_antigen String H-antigen gene identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_H_antigen_reads String H-antigen gene identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_ipaH_presence_absence String Presence (+) or absence (-) of ipaH identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_ipaH_presence_absence_reads String Presence (+) or absence (-) of ipaH identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_notes String Any notes output from ShigEiFinder FASTA, ONT, PE, SE shigeifinder_notes_reads String Any notes output from ShigEiFinder using read files as inputs PE, SE shigeifinder_num_virulence_plasmid_genes String Number of virulence plasmid genes identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_num_virulence_plasmid_genes_reads String Number of virulence plasmid genes identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_O_antigen String O-antigen gene identified by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_O_antigen_reads String O-antigen gene identified by ShigEiFinder using read files as inputs PE, SE shigeifinder_report File TSV report from ShigEiFinder (see https://github.com/LanLab/ShigEiFinder#shigeifinder) FASTA, ONT, PE, SE shigeifinder_report_reads File TSV report from ShigEiFinder (see https://github.com/LanLab/ShigEiFinder#shigeifinder) using read files as inputs PE, SE shigeifinder_serotype String Serotype predicted by ShigEiFinder FASTA, ONT, PE, SE shigeifinder_serotype_reads String Serotype predicted by ShigEiFinder using read files as inputs PE, SE shigeifinder_version String ShigEiFinder version used FASTA, ONT, PE, SE shigeifinder_version_reads String ShigEiFinder version used using read files as inputs PE, SE shovill_pe_version String Shovill version used PE shovill_se_version String Shovill version used SE sistr_allele_fasta File FASTA file of novel cgMLST alleles from SISTR FASTA, ONT, PE, SE sistr_allele_json File JSON file of cgMLST allele sequences and information (see https://github.com/phac-nml/sistr_cmd#cgmlst-allele-search-results) FASTA, ONT, PE, SE sistr_cgmlst File CSV file of the cgMLST allelic profile from SISTR (see https://github.com/phac-nml/sistr_cmd#cgmlst-allelic-profiles-output---cgmlst-profiles-cgmlst-profilescsv) FASTA, ONT, PE, SE sistr_predicted_serotype String Serotype predicted by SISTR FASTA, ONT, PE, SE sistr_results File TSV results file produced by SISTR (see https://github.com/phac-nml/sistr_cmd#primary-results-output--o-sistr-results) FASTA, ONT, PE, SE sistr_version String Version of SISTR used FASTA, ONT, PE, SE sonneityping_final_genotype String Final genotype call from Mykrobe, via sonneityper ONT, PE, SE sonneityping_final_report_tsv File Detailed TSV report from mykrobe, via sonneityper (see https://github.com/katholt/sonneityping#example-output) ONT, PE, SE sonneityping_genotype_confidence String Confidence in the final genotype call from sonneityper ONT, PE, SE sonneityping_genotype_name String Human readable alias for genotype, where available provided by sonneityper ONT, PE, SE sonneityping_mykrobe_docker String sonneityping docker image used ONT, PE, SE sonneityping_mykrobe_report_csv File CSV report from mykrobe via sonneityper (see https://github.com/Mykrobe-tools/mykrobe/wiki/AMR-prediction-output#csv-file) ONT, PE, SE sonneityping_mykrobe_report_json File JSON report from mykrobe via sonneityper (see https://github.com/Mykrobe-tools/mykrobe/wiki/AMR-prediction-output#json-file) ONT, PE, SE sonneityping_mykrobe_version String Version of sonneityping used ONT, PE, SE sonneityping_species String Species call from Mykrobe via sonneityping ONT, PE, SE spatyper_docker String spatyper docker image used FASTA, ONT, PE, SE spatyper_repeats String order of identified repeats FASTA, ONT, PE, SE spatyper_tsv File TSV report with spatyper results FASTA, ONT, PE, SE spatyper_type String spa type FASTA, ONT, PE, SE spatyper_version String spatyper version used FASTA, ONT, PE, SE srst2_vibrio_biotype String Biotype classification according to tcpA gene sequence (Classical or ElTor) PE, SE srst2_vibrio_ctxA String Presence or absence of the ctxA gene PE, SE srst2_vibrio_detailed_tsv File Detailed https://github.com/katholt/srst2 output file PE, SE srst2_vibrio_ompW String Presence or absence of the ompW gene PE, SE srst2_vibrio_serogroup String Serotype classification as O1 (wbeN gene), O139 (wbfR gene) or not detected. PE, SE srst2_vibrio_toxR String Presence or absence of the toxR gene PE, SE srst2_vibrio_version String The SRST2 version run PE, SE staphopiasccmec_docker String staphopia-sccmec docker image used FASTA, ONT, PE, SE staphopiasccmec_hamming_distance_tsv File staphopia-sccmec version FASTA, ONT, PE, SE staphopiasccmec_results_tsv File sccmec types and mecA presence FASTA, ONT, PE, SE staphopiasccmec_types_and_mecA_presence String staphopia-sccmec Hamming distance file FASTA, ONT, PE, SE staphopiasccmec_version String staphopia-sccmec presence and absence TSV file FASTA, ONT, PE, SE stxtyper_all_hits String Comma-separated list of matches of all types. Includes complete, partial, frameshift, internal stop, and novel hits. List is de-duplicated so multiple identical hits are only listed once. For example if 5 partial stx2 hits are detected in the genome, only 1 \"stx2\" will be listed in this field. To view the potential subtype for each partial hit, the user will need to view the stxtyper_report TSV file. FASTA, ONT, PE, SE stxtyper_complete_operons String Comma-separated list of all COMPLETE operons detected by StxTyper. Show multiple hits if present in results. FASTA, ONT, PE, SE stxtyper_docker String Name of docker image used by the stxtyper task. FASTA, ONT, PE, SE stxtyper_novel_hits String Comma-separated list of matches that have the OPERON output of \"COMPLETE_NOVEL\". Possible outputs \"stx1\", \"stx2\", or \"stx1,stx2\" FASTA, ONT, PE, SE stxtyper_num_hits Int Number of \"hits\" or rows present in the stxtyper_report TSV file FASTA, ONT, PE, SE stxtyper_partial_hits String Possible outputs \"stx1\", \"stx2\", or \"stx1,stx2\". Tells the user that there was a partial hit to either the A or B subunit, but does not describe which subunit, only the possible types from the PARTIAL matches. FASTA, ONT, PE, SE stxtyper_report File Raw results TSV file produced by StxTyper FASTA, ONT, PE, SE stxtyper_stx_frameshifts_or_internal_stop_hits String Comma-separated list of matches that have the OPERON output of \"FRAMESHIFT\" or \"INTERNAL_STOP\". Possible outputs \"stx1\", \"stx2\", or \"stx1,stx2\" FASTA, ONT, PE, SE stxtyper_version String Version of StxTyper used FASTA, ONT, PE, SE taxon_table_status String Status of the taxon table upload FASTA, ONT, PE, SE tbp_parser_average_genome_depth Float Optional output. Average genome depth across the reference genome ONT, PE, SE tbp_parser_coverage_report File Optional output. TSV file with breadth of coverage of each gene associated with antimicrobial resistance in mycobacterium tuberculosis. ONT, PE, SE tbp_parser_docker String Optional output. The docker image for tbp-parser ONT, PE tbp_parser_genome_percent_coverage Float Optional output. The percent of the genome covered at a depth greater than the specified minimum (default 10) ONT, PE, SE tbp_parser_laboratorian_report_csv File Optional output. Human-readable laboratorian report file containing the list of mutations found to be conferring resistance, both by WHO classification and expert rule implementation. The file contains the following columns: sample_id, tbprofiler_gene_name, tbprofiler_variant_locus_tag, tbprofiler_variant_substitution_type, tbprofiler_variant_substitution_nt, tbprofiler_variant_substitution_aa, confidence according to WHO, antimicrobial, depth, frequency, read_support, rationale ( WHO or expert rule), and warning if the coverage is below specified minimum (default 10) ONT, PE, SE tbp_parser_lims_report_csv File Optional output. LIMS digestable CSV report containing information on resistance for a set of antimicrobials ( No resistance to X detected, The detected genetic determinant(s) have uncertain significance, resistance to X cannot be ruled out and Genetic determinant(s) associated with resistance to X detected). For each antimicrobial, the mutations found are reported in the mutation_nucleotide; (mutation_protein) format, otherwise No mutations detected is reported. ONT, PE, SE tbp_parser_looker_report_csv File Optional output. Looker digestible CSV report containing information on resistance for a set of antimicrobials (R for resistant, S for susceptible) ONT, PE, SE tbp_parser_version String Optional output. The version of tbp-parser ONT, PE tbprofiler_dr_type String Drug resistance type predicted by TB-Profiler (sensitive, Pre-MDR, MDR, Pre-XDR, XDR) ONT, PE, SE tbprofiler_main_lineage String Lineage(s) predicted by TBProfiler ONT, PE, SE tbprofiler_median_depth Int The median depth of the H37Rv TB reference genome covered by the sample ONT, PE tbprofiler_output_bai File Index BAM file generated by mapping sequencing reads to reference genome by TBProfiler ONT, PE, SE tbprofiler_output_bam File BAM alignment file produced by TBProfiler ONT, PE, SE tbprofiler_output_file File CSV report from TBProfiler ONT, PE, SE tbprofiler_output_vcf File VCF file output from TBProfiler; the concatenation of all of the different VCF files produced during TBProfiler analysis ONT, PE, SE tbprofiler_pct_reads_mapped Float The percentage of reads mapped to the H37Rv TB reference genome ONT, PE tbprofiler_resistance_genes String List of resistance mutations detected by TBProfiler ONT, PE, SE tbprofiler_sub_lineage String Sub-lineage(s) predicted by TBProfiler ONT, PE, SE tbprofiler_version String Version of TBProfiler used ONT, PE, SE theiaprok_fasta_analysis_date String Date of TheiaProk FASTA workflow execution FASTA theiaprok_fasta_version String Version of TheiaProk FASTA workflow execution FASTA theiaprok_illumina_pe_analysis_date String Date of TheiaProk PE workflow execution PE theiaprok_illumina_pe_version String Version of TheiaProk PE workflow execution PE theiaprok_illumina_se_analysis_date String Date of TheiaProk SE workflow execution SE theiaprok_illumina_se_version String Version of TheiaProk SE workflow execution SE theiaprok_ont_analysis_date String Date of TheiaProk ONT workflow execution ONT theiaprok_ont_version String Version of TheiaProk ONT workflow execution ONT tiptoft_plasmid_replicon_fastq File File produced by tiptoft that contains reads containing plasmid rep/inc genes ONT tiptoft_plasmid_replicon_genes String Rep/inc genes found in sample ONT tiptoft_version String Version of tiptoft used for analysis ONT trimmomatic_docker String Docker image used for trimmomatic PE, SE trimmomatic_version String Version of trimmomatic used PE, SE ts_mlst_allelic_profile String Profile of MLST loci and allele numbers predicted by MLST FASTA, ONT, PE, SE ts_mlst_docker String Docker image used for MLST FASTA, ONT, PE, SE ts_mlst_novel_alleles File FASTA file containing nucleotide sequence of any alleles that are not in the MLST database used by TheiaProk FASTA, ONT, PE, SE ts_mlst_predicted_st String ST predicted by MLST FASTA, ONT, PE, SE ts_mlst_pubmlst_scheme String PubMLST scheme used byMLST FASTA, ONT, PE, SE ts_mlst_results File TSV report with detailed MLST profile, including https://github.com/tseemann/mlst#missing-data FASTA, ONT, PE, SE ts_mlst_version String Version of Torsten Seeman\u2019s MLST tool used FASTA, ONT, PE, SE virulencefinder_docker String VirulenceFinder docker image used FASTA, ONT, PE, SE virulencefinder_hits String Virulence genes detected by VirulenceFinder FASTA, ONT, PE, SE virulencefinder_report_tsv File Output TSV file created by VirulenceFinder FASTA, ONT, PE, SE"},{"location":"workflows/genomic_characterization/vadr_update/","title":"VADR_Update","text":""},{"location":"workflows/genomic_characterization/vadr_update/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Genomic Characterization Viral PHB v2.2.0 Yes Sample-level"},{"location":"workflows/genomic_characterization/vadr_update/#vadr_update_phb","title":"Vadr_Update_PHB","text":"The VADR_Update workflow updates prior VADR assessments for each sample in line with the assessment criteria in an alternative docker image. This may be useful when samples have previously been subject to VADR alerts as updates to VADR assessment criteria may mean that the sample no longer raises concern about quality. The latest docker image SARS-CoV-2 for VADR can be found\u00a0here.
Various models are available for many organisms. The following table provides an overview of the recommended container to be used and what options should be passed on to VADR.
Organism docker vadr_opts max_length sars-cov-2 \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"--noseqnamemax --glsearch -s -r --nomisc --mkey sarscov2 --lowsim5seq 6 --lowsim3seq 6 --alt_fail lowscore,insertnn,deletinn --out_allfasta\" 30000 MPXV \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"--glsearch -s -r --nomisc --mkey mpxv --r_lowsimok --r_lowsimxd 100 --r_lowsimxl 2000 --alt_pass discontn,dupregin --out_allfasta --minimap2 --s_overhang 150\" 210000 WNV \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"--mkey flavi --mdir /opt/vadr/vadr-models-flavi/ --nomisc --noprotid --out_allfasta\" 11000 flu \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"--atgonly --xnocomp --nomisc --alt_fail extrant5,extrant3 --mkey flu\" 13500 rsv_a \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"-r --mkey rsv --xnocomp\" 15500 rsv_b \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3\" \"-r --mkey rsv --xnocomp\" 15500 HAV \"us-docker.pkg.dev/general-theiagen/staphb/vadr:1.6.3-hav\" \"-r -xnocomp -mkey hav.vadr\" 10500"},{"location":"workflows/genomic_characterization/vadr_update/#inputs","title":"Inputs","text":"Please note the default values are for SARS-CoV-2.
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status vadr_update assembly_length_unambiguous Int Number of unambiguous basecalls within the consensus assembly Required vadr_update docker String The Docker container to use for the task Required vadr_update genome_fasta File Consensus genome assembly Required vadr cpu Int Number of CPUs to allocate to the task 2 Optional vadr disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional vadr max_length Int Maximum length for the fasta-trim-terminal-ambigs.pl VADR script 30000 Optional vadr memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional vadr min_length Int Minimum length subsequence to possibly replace Ns for the fasta-trim-terminal-ambigs.pl VADR script 50 Optional vadr skip_length Int Minimum assembly length (unambiguous) to run vadr 10000 Optional vadr vadr_opts String Options for the v-annotate.pl VADR script ''--glsearch -s -r --nomisc --mkey sarscov2 --alt_fail lowscore,fstukcnf,insertnn,deletinn --mdir /opt/vadr/vadr-models/'' Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/genomic_characterization/vadr_update/#outputs","title":"Outputs","text":"Variable Type Description vadr_alerts_list File File containing all of the fatal alerts as determined by VADR vadr_docker String Docker image used to run VADR vadr_fastas_zip_archive File Archive file (in zip format) of all VADR outputs vadr_num_alerts String Number of fatal alerts as determined by VADR vadr_update_analysis_date String Date of analysis vadr_update_version String Version of the Public Health Bioinformatics (PHB) repository used"},{"location":"workflows/phylogenetic_construction/augur/","title":"Augur","text":""},{"location":"workflows/phylogenetic_construction/augur/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Viral PHB v2.3.0 Yes Sample-level, Set-level"},{"location":"workflows/phylogenetic_construction/augur/#augur-workflows","title":"Augur Workflows","text":"Genomic Epidemiology is an important approach in the effort to understand and mitigate against disease transmission. An often-critical step in viral genomic epidemiology is the generation of phylogenetic trees to explore the genetic relationship between viruses on a local, regional, national or global scale. The Augur workflows, currently only targeted for viral pathogens, facilitate this process by generating files for the visualization of phylogenetic trees with accompanying metadata.
Two workflows are offered: Augur_Prep_PHB and Augur_PHB. These must be run sequentially, respectively, to first prepare each individual sample for running Augur, and secondly to run Augur itself on the set of samples, generating the phylogenetic tree files with accompanying metadata. The outputs from these workflows can be visualized in\u00a0Auspice\u00a0and\u00a0UShER.
Helpful resources for epidemiological interpretation
The Augur_Prep_PHB workflow was written to prepare individual sample assemblies and their metadata for running the Augur_PHB analysis.
"},{"location":"workflows/phylogenetic_construction/augur/#augur_prep-inputs","title":"Augur_Prep Inputs","text":"The Augur_Prep_PHB workflow takes assembly FASTA files and associated metadata formatted in a data table. FASTA files may be generated with one of the TheiaCoV Characterization workflows and should adhere to quality control guidelines, (e.g.\u00a0QC guidelines produced by PHA4GE). The metadata can be uploaded to Terra as TSV file, formatted as in this\u00a0example.
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status augur_prep assembly File Assembly/consensus file (single FASTA file per sample) Required augur_prep collection_date String Collection date of the sample Optional augur_prep continent String Continent where sample was collected Optional augur_prep country String Country where sample was collected Optional augur_prep county String County (or smaller locality) where sample was collected Optional augur_prep nextclade_clade String The Nextclade clade of the sample Optional augur_prep pango_lineage String The Pangolin lineage of the sample Optional augur_prep state String State (or province) where sample was collected Optional prep_augur_metadata cpu Int Number of CPUs to allocate to the task 1 Optional prep_augur_metadata disk_size Int Amount of storage (in GB) to allocate to the task 10 Optional prep_augur_metadata docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1 Optional prep_augur_metadata memory Int Amount of memory/RAM (in GB) to allocate to the task 3 Optional prep_augur_metadata organism String The organism to be analyzed in Augur; options: \"sars-cov-2\", \"flu\", \"MPXV\", \"rsv-a\", \"rsv-b\" sars-cov-2 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/augur/#augur_prep-outputs","title":"Augur_Prep Outputs","text":"Variable Type Description augur_metadata File TSV file of the metadata provided as input to the workflow in the proper format for Augur analysis augur_prep_phb_analysis_date String Date of analysis augur_prep_phb_version String Version of the Public Health Bioinformatics (PHB) repository used"},{"location":"workflows/phylogenetic_construction/augur/#augur_phb","title":"Augur_PHB","text":"Helpful Hint
You may have to generate phylogenies multiple times, running the Augur_PHB workflow, assessing results, and amending inputs to generate a final tree with sufficient diversity and high-quality data of interest.
The Augur_PHB workflow takes a set of assembly/consensus files (FASTA format) and sample metadata files (TSV format) that have been reformatted using\u00a0Augur_Prep_PHB\u00a0and runs Augur to generate the phylogenetic tree files with accompanying metadata. Additionally, the workflow infers pairwise SNP distances.
"},{"location":"workflows/phylogenetic_construction/augur/#augur-inputs","title":"Augur Inputs","text":"The Augur_PHB workflow takes in a\u00a0set\u00a0of SARS-CoV-2 (or any other viral pathogen) FASTA and metadata files. If running the workflow via Terra, individual samples will need to be added to a set before running the workflow. Input FASTAs should meet QA metrics. Sets of FASTAs with highly discordant quality metrics may result in the inaccurate inference of genetic relatedness. There must be some sequence diversity among the set of input assemblies. If insufficient diversity is present, it may be necessary to add a more divergent sequence to the set.
Optional Inputs
There are many optional user inputs. For SARS-CoV-2, Flu, rsv-a, rsv-b, and mpxv, default values that mimic the NextStrain builds have been preselected. To use these defaults, you must write either \"sars-cov-2\",\"flu\", \"rsv-a\", \"rsv-b\", or \"mpxv\" for the organism variable.
For Flu - it is required to set flu_segment to either \"HA\" or \"NA\" & flu_subtype to either \"H1N1\" or \"H3N2\" or \"Victoria\" or \"Yamagata\" or \"H5N1\" (\"H5N1\" will only work with \"HA\") depending on your set of samples.
\"gs://theiagen-public-files-rp/terra/flu-references/lat_longs.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_h1n1pdm.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h1n1pdm_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/clades_h1n1pdm_ha.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h1n1pdm_na.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_h3n2.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h3n2_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/clades_h3n2_ha.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h3n2_na.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_vic.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_vic_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/clades_vic_ha.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_vic_na.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_yam.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_yam_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/clades_yam_ha.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_yam_na.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/auspice_config_h5n1.json\"\"gs://theiagen-public-files-rp/terra/flu-references/reference_h5n1_ha.gb\"\"gs://theiagen-public-files-rp/terra/flu-references/h5nx-clades.tsv\"\"gs://theiagen-public-files-rp/terra/augur-mpox-references/mpox_clades.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/lat_longs.tsv\"\"gs://theiagen-public-files-rp/terra/augur-mpox-references/NC_063383.1.reference.fasta\"\"gs://theiagen-public-files-rp/terra/augur-mpox-references/NC_063383.1_reference.gb\"\"gs://theiagen-public-files-rp/terra/augur-mpox-references/mpox_auspice_config_mpxv.json\"\"gs://theiagen-public-files-rp/terra/rsv_references/rsv_a_clades.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/lat_longs.tsv\"\"gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_a.fasta\"\"\"gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_a.gb\"\"\"gs://theiagen-public-files-rp/terra/rsv_references/rsv_auspice_config.json\"\"gs://theiagen-public-files-rp/terra/rsv_references/rsv_b_clades.tsv\"\"gs://theiagen-public-files-rp/terra/flu-references/lat_longs.tsv\"\"gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_b.fasta\"\"\"gs://theiagen-public-files-rp/terra/rsv_references/reference_rsv_b.gb\"\"\"gs://theiagen-public-files-rp/terra/rsv_references/rsv_auspice_config.json\"For more information regarding these optional inputs, please view Nextrain's detailed documentation on Augur
What's required or not?
For organisms other than SARS-CoV-2 or Flu, the required variables have both the \"required\" and \"optional\" tags.
This workflow runs on the set level. Please note that for every task, runtime parameters are modifiable (cpu, disk_size, docker, and memory); most of these values have been excluded from the table below for convenience.
Terra Task Name Variable Type Description Default Value Terra Status augur assembly_fastas Array[File] An array of the assembly files to use; use either the HA or NA segment for flu samples Required augur build_name String Name to give to the Augur build Required augur auspice_config File Auspice config file for customizing visualizations; takes priority over the other customization values available for augur_export Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, a minimal auspice config file is provided to prevent workflow failure, \"gs://theiagen-public-files-rp/terra/augur-defaults/minimal-auspice-config.json\", but will not be as useful as an organism specific config file. Optional augur clades_tsv File TSV file containing clade mutation positions in four columns Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, an empty clades file is provided to prevent workflow failure, \"gs://theiagen-public-files-rp/terra/augur-defaults/minimal-clades.tsv\", but will not be as useful as an organism specific clades file. Optional, Required augur distance_tree_only Boolean Create only a distance tree (skips all Augur steps after augur_tree) TRUE Optional augur flu_segment String Required if organism = \"flu\". The name of the segment to be analyzed; options: \"HA\" or \"NA\" \"HA\" (only used if organism = \"flu\") Optional, Required augur flu_subtype String Required if organism = \"flu\". The subtype of the flu samples being analyzed; options: \"H1N1\", \"H3N2\", \"Victoria\", \"Yamagata\", \"H5N1\" Optional, Required augur lat_longs_tsv File Tab-delimited file of geographic location names with corresponding latitude and longitude values Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, a minimal lat-long file is provided to prevent workflow failure, \"gs://theiagen-public-files-rp/terra/augur-defaults/minimal-lat-longs.tsv\", but will not be as useful as a detailed lat-longs file covering all the locations for the samples to be visualized. Optional augur min_date Float Minimum date to begin filtering or frequencies calculations Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default value is 0.0 Optional augur min_num_unambig Int Minimum number of called bases in genome to pass prefilter Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default value is 0 Optional augur organism String Organism used to preselect default values; options: \"sars-cov-2\", \"flu\", \"mpxv\", \"rsv-a\", \"rsv-b\" sars-cov-2 Optional augur reference_fasta File The reference FASTA file used to align the genomes and build the trees Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, a reference fasta file must be provided otherwise the workflow fails. Optional, Required augur reference_genbank File The GenBank .gb file for the same reference genome used for the reference_fasta Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, a reference genbank file must be provided otherwise the workflow fails. Optional, Required augur sample_metadata_tsvs Array[File] An array of the metadata files produced in Augur_Prep_PHB Optional augur build_name_updated String Internal component, do not modify. Used for replacing spaces with underscores _ Do Not Modify augur_align fill_gaps Boolean If true, gaps represent missing data rather than true indels and so are replaced by N after aligning. FALSE Optional augur_ancestral infer_ambiguous Boolean If true, infer nucleotides and ambiguous sites and replace with most likely FALSE Optional augur_ancestral inference String Calculate joint or marginal maximum likelihood ancestral sequence states; options: \"joint\", \"marginal\" joint Optional augur_ancestral keep_ambiguous Boolean If true, do not infer nucleotides at ambiguous (N) sides FALSE Optional augur_ancestral keep_overhangs Boolean If true, do not infer nucleotides for gaps on either side of the alignment FALSE Optional augur_export colors_tsv File Custom color definitions, one per line in TSV format with the following fields: TRAIT_TYPE TRAIT_VALUE HEX_CODE Optional augur_export description_md File Markdown file with description of build and/or acknowledgements Optional augur_export include_root_sequence Boolean Export an additional JSON containing the root sequence used to identify mutations FALSE Optional augur_export title String Title to be displayed by Auspice Optional augur_refine branch_length_inference String Branch length mode of timetree to use; options: \"auto\", \"joint\", \"marginal\", \"input\" auto Optional augur_refine clock_filter_iqd Int Remove tips that deviate more than n_iqd interquartile ranges from the root-to-tip vs time regression 4 Optional augur_refine clock_rate Float Fixed clock rate to use for time tree calculations Optional augur_refine clock_std_dev Float Standard deviation of the fixed clock_rate estimate Optional augur_refine coalescent String Coalescent time scale in units of inverse clock rate (float), optimize as scalar (\"opt\") or skyline (\"skyline\") Optional augur_refine covariance Boolean If true, account for covariation when estimating rates and/or rerooting TRUE Optional augur_refine date_confidence Boolean If true, calculate confidence intervals for node dates TRUE Optional augur_refine date_inference String Assign internal nodes to their marginally most likely dates; options: \"joint\", \"marginal\" marginal Optional augur_refine divergence_units String Units in which sequence divergences is exported; options: \"mutations\" or \"mutations-per-site\" mutations Optional augur_refine gen_per_year Int Number of generations per year 50 Optional augur_refine keep_polytomies Boolean If true, don't attempt to resolve polytomies FALSE Optional augur_refine keep_root Boolean If true, do not reroot the tree; use it as-is (overrides anything specified by root) TRUE Optional augur_refine precision String Precision used to determine the number of grid points; options: 0 (rough) to 3 (ultra fine) auto Optional augur_refine root String Rooting mechanism; options: \"best\", \"least-squares\", \"min_dev\", \"oldest\", etc. Optional augur_translate genes File A file containing a list of genes to translate (from nucleotides to amino acids) Optional augur_tree exclude_sites File File of one-based sites to exclude for raw tree building (BED format in .bed files, DRM format in tab-delimited files, or one position per line) Optional augur_tree method String Which method to use to build the tree; options: \"fasttree\", \"raxml\", \"iqtree\" iqtree Optional augur_tree override_default_args Boolean If true, override default tree builder arguments instead of augmenting them FALSE Optional augur_tree substitution_model String The substitution model to use; only available for iqtree. Specify \"auto\" to run ModelTest; model options can be found here GTR Optional augur_tree tree_builder_args String Additional tree builder arguments either augmenting or overriding the default arguments. FastTree defaults: \"-nt -nosupport\". RAxML defaults: \"-f d -m GTRCAT -c 25 -p 235813\". IQ-TREE defaults: \"-ninit 2 -n 2 -me 0.05 -nt AUTO -redo\" Optional sc2_defaults nextstrain_ncov_repo_commit String The version of the from which to draw default values for SARS-CoV-2.23d1243127e8838a61b7e5c1a72bc419bf8c5a0d Optional organism_parameters gene_locations_bed_file File Use to provide locations of interest where average coverage will be calculated Defaults are organism-specific. Please find default values for some organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, an empty file is provided, \"gs://theiagen-public-files/terra/theiacov-files/empty.bed\", but will not be as useful as an organism specific gene locations bed file. Optional organism_parameters genome_length_input Int Use to specify the expected genome length; provided by default for all supported organisms Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the genome length input must be provided otherwise the workflow fails. Optional, Required organism_parameters hiv_primer_version String The version of HIV primers used. Options are https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl#L156 and https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl#L164. This input is ignored if provided for TheiaCoV_Illumina_SE and TheiaCoV_ClearLabs v1 Optional organism_parameters kraken_target_organism_input String The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database. Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is \"\". Optional organism_parameters nextclade_dataset_name_input String NextClade organism dataset name Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is \"NA\". Optional organism_parameters nextclade_dataset_tag_input String NextClade organism dataset tag Defaults are organism-specific. Please find default values for all organisms (and for Flu - their respective genome segments and subtypes) here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is \"NA\". Optional organism_parameters pangolin_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/pangolin:4.3.1-pdata-1.26 Optional organism_parameters primer_bed_file File The bed file containing the primers used when sequencing was performed Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, an empty primer bed file is provided, \"gs://theiagen-public-files/terra/theiacov-files/empty.bed\", but will not be as useful as an organism specific primer bed file. Optional organism_parameters reference_gff_file File Reference GFF file for the organism being analyzed Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, an empty gff file is provided, \"gs://theiagen-public-files/terra/theiacov-files/empty.gff3\", but will not be as useful as an organism specific gff file. Optional organism_parameters vadr_max_length Int Maximum length for the fasta-trim-terminal-ambigs.pl VADR script Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is 0. Optional organism_parameters vadr_mem Int Memory, in GB, allocated to this task 32 (RSV-A and RSV-B) and 8 (all other TheiaCoV organisms) organism_parameters vadr_options String Options for the v-annotate.pl VADR script Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is \"NA\". Optional organism_parameters vadr_skip_length Int Minimum assembly length (unambiguous) to run VADR Defaults are organism-specific. Please find default values for all organisms here: https://github.com/theiagen/public_health_bioinformatics/blob/main/workflows/utilities/wf_organism_parameters.wdl. For an organism without set defaults, the default is 0. Optional mutation_context cpu Int CPUs requested for the mutation_context task that is specific to Mpox. 1 Optional mutation_context disk_size Int Disk size in GB requested for the mutation_context task that is specific to Mpox. 50 Optional mutation_context docker String Docker image used for the mutation_context task that is specific to Mpox. Do not modify. us-docker.pkg.dev/general-theiagen/theiagen/nextstrain-mpox-mutation-context:2024-06-27 Do Not Modify, Optional mutation_context memory Int Memory size in GB requested for the mutation_context task that is specific to Mpox. 4 Optional Workflow Tasks"},{"location":"workflows/phylogenetic_construction/augur/#augur-tasks","title":"Augur Workflow Tasks","text":"The Augur_PHB workflow uses the inputs to generate a phylogenetic tree in JSON format that is compatible with phylogenetic tree visualization software.
In Augur_PHB, the tasks below are called. For the Augur subcommands, please view the Nextstrain Augur documentation for more details and explanations.
cat_files - concatenate all of the input fasta files togethersc2_defaults - if organism is SARS-CoV-2, establish default parametersflu_defaults - if organism is Flu, establish default parametersfilter_sequences_by_length - remove any sequences that do not meet the quality threshold set by min_num_unambigtsv_join - merge the metadata filesfasta_to_ids - extract a list of remaining sequences so we know which ones were droppedaugur_align - perform MAFFT alignment on the sequencesaugur_tree - create a distance treeaugur_refine - create a timetreeaugur_ancestral - infer ancestral sequencesaugur_translate - translate gene regions from nucleotides to amino acidsmutation_context - if organism is MPXV, calculates the mutation fraction of G->A or C->T changesaugur_clades - if clade information is provided, assign clades to nodes based on amino-acid or nucleotide signaturesaugur_export - export all the results in a JSON file suitable for Auspice visualizationsnp_dists - create a SNP matrix from the alignmentreorder_matrix - reorder the SNP matrix to match the distance treeDiversity dependent
Note that the node & branch coloring by clade or lineage assignment might be dependent on the diversity of your input dataset. This is because the clade assignment is done using the ancestrally reconstructed amino acid or nucleotide changes at the tree nodes rather than a direct sequence-to-reference mutation comparison. You may notice this happening when you get clade/lineage assignments from NextClade when running TheiaCoV workflows, but no clade/lineage assignment on the Augur Auspice tree.
To get around this issue, you can upload the Augur output file merged-metadata.tsv to Auspice that includes the correct clade/lineage assignments to allow for coloring by Clade.
Flu clade assignments
Note that for flu, the clade assignment is usually mostly done for the more recent seasonal influenza viruses. Older strains may get an \"unassigned\" designation for clades. Therefore, it is important to counter check with the NextClade results from TheiaCoV if the lack of clade assignment is due to analyzing older sequences or sequence related.
The auspice_input_json is intended to be uploaded to\u00a0Auspice\u00a0to view the phylogenetic tree. This provides a visualization of the genetic relationships between your set of samples. The metadata_merged output can also be uploaded to add context to the phylogenetic visualization. The combined_assemblies output can be uploaded to\u00a0UShER\u00a0to view the samples on a global tree of representative sequences from the public repositories.
The Nextstrain team hosts documentation surrounding the Augur workflow \u2192 Auspice visualization here, which details the various components of the Auspice interface: How data is exported by Augur for visualisation in Auspice.
Variable Type Description aligned_fastas File A FASTA file of the aligned genomes augur_fasttree_version String The fasttree version used, blank if other tree method used augur_iqtree_model_used String The iqtree model used during augur tree, blank if iqtree not used augur_iqtree_version String The iqtree version used during augur tree (defualt), blank if other tree method used augur_mafft_version String The mafft version used in augur align augur_phb_analysis_date String The date the analysis was run augur_phb_version String The version of the Public Health Bioinformatics (PHB) repository used augur_raxml_version String The version of raxml used during augur tree, blank if other tree method used augur_version String Version of Augur used auspice_input_json File JSON file used as input to Auspice combined_assemblies File Concatenated FASTA file containing all samples distance_tree File The distance tree created in Newick (.nwk) format keep_list File A list of samples included in the phylogenetic tree metadata_merged File Tab-delimited text file of the merged augur_metadata input files from all samples snp_matrix File The SNP distance matrix for all samples used in the phylogenetic tree time_tree File The time tree created in Newick (.nwk) format traits_json File A JSON file containing sample traits"},{"location":"workflows/phylogenetic_construction/augur/#mpox-specific-auspice-output-json","title":"Mpox-specific Auspice Output JSON","text":"If you are building a tree for Mpox samples and set the optional input parameter organism to \"mpox\" , an additional step will be carried out in the Augur_PHB workflow. This additional step will calculate the mutation fraction of G\u2192A or C\u2192T changes. These mutations have been shown to be a characteristic of APOBEC3-type editing, which indicate adaptation of the virus to circulation among humans as was observed with the 2022 clade IIb outbreak, and more recently (2024) with the clade Ib outbreak in South Kivu, Democratic Republic of the Congo.
When visualizing the output auspice_input_json file, there will be 2 new choices in the drop-down menu for \"Color By\":
An example Mpox tree with these \"Color By\" options can be viewed here: https://nextstrain.org/mpox/clade-IIb?c=GA_CT_fraction
"},{"location":"workflows/phylogenetic_construction/augur/#references","title":"References","text":"When publishing work using the Augur_PHB workflow, please reference the following:
Nextstrain:\u00a0Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, Sagulenko P, Bedford T, Neher RA. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics. 2018 Dec 1;34(23):4121-3.
When publishing work using inferences from UShER, please reference:
UShER:\u00a0Turakhia Y, Thornlow B, Hinrichs AS, De Maio N, Gozashti L, Lanfear R, Haussler D, Corbett-Detig R. Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic. Nature Genetics. 2021 Jun;53(6):809-16.
"},{"location":"workflows/phylogenetic_construction/core_gene_snp/","title":"Core_Gene_SNP","text":""},{"location":"workflows/phylogenetic_construction/core_gene_snp/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.1.0 Yes, some optional features incompatible Set-level"},{"location":"workflows/phylogenetic_construction/core_gene_snp/#core_gene_snp_phb","title":"Core_Gene_SNP_PHB","text":"Core Gene SNP Workflow Diagram
The Core_Gene_SNP workflow is intended for pangenome analysis, core gene alignment, and phylogenetic analysis. The workflow takes in gene sequence data in GFF3 format from a set of samples. It first produces a pangenome summary using Pirate, which clusters genes within the sample set into orthologous gene families. By default, the workflow also instructs Pirate to produce both core gene and pangenome alignments. The workflow subsequently triggers the generation of a phylogenetic tree and SNP distance matrix from the core gene alignment using iqtree and snp-dists, respectively. Optionally, the workflow will also run this analysis using the pangenome alignment. This workflow also features an optional module, summarize_data, that creates a presence/absence matrix for the analyzed samples from a list of indicated columns (such as AMR genes, etc.) that can be used in Phandango.
Default Parameters
Please note that while default parameters for pangenome construction and phylogenetic tree generation are provided, these default parameters may not suit every dataset and have not been validated against known phylogenies. Users should take care to select the parameters that are most appropriate for their dataset. Please reach out to support@theiagen.com or one of the other resources listed at the bottom of this page if you would like assistance with this task.
"},{"location":"workflows/phylogenetic_construction/core_gene_snp/#inputs","title":"Inputs","text":"For further detail regarding Pirate options, please see [PIRATE's documentation)[https://github.com/SionBayliss/PIRATE). For further detail regarding IQ-TREE options, please see http://www.iqtree.org/doc/Command-Reference.
This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status core_gene_snp_workflow cluster_name String Name of sample set Required core_gene_snp_workflow gff3 Array[File] Array of gff3 files to include in analysis, output gff files from both prokka and bakta using TheiaProk workflows are compatible Required core_gene_snp_workflow midpoint_root_tree Boolean Boolean variable that will instruct the workflow to reroot the tree at the midpoint FALSE Optional core_gene_snp_workflow phandango_coloring Boolean Boolean variable that tells the data summary task and the reorder matrix task to include a suffix that enables consistent coloring on Phandango; by default, this suffix is not added. To add this suffix set this variable to true. FALSE Optional core_gene_snp_workflow data_summary_terra_table String The name of the Terra data table that you want data pulled from Optional core_gene_snp_workflow data_summary_column_names String A comma-delimited list of columns in the origin data table that contains contain that you would like a presence/absence .csv matrix generated for Optional core_gene_snp_workflow core_tree Boolean Boolean variable that instructs the workflow to create a phylogenetic tree and SNP distance matrix from the core gene alignment. Align must also be set to true. TRUE Optional core_gene_snp_workflow pan_tree Boolean Boolean variable that instructs the workflow to create a phylogenetic tree and SNP distance matrix from the pangenome alignment. Align must also be set to true. FALSE Optional core_gene_snp_workflow data_summary_terra_workspace String The name of the current Terra workspace you are in; this can be found at the top of the webpage, or in the URL after the billing project. Optional core_gene_snp_workflow align Boolean Boolean variable that instructs the workflow to generate core and pangenome alignments if \"true\". If \"false\", the workflow will produce only a pangenome summary. TRUE Optional core_gene_snp_workflow data_summary_terra_project String The billing project for the current workspace; can be found after the \"#workspaces/\" section in the workflow's URL Optional core_gene_snp_workflow sample_names Array[String] Array of sample_ids from the data table used Optional core_iqtree memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional core_iqtree disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional core_iqtree cpu Int Number of CPUs to allocate to the task 4 Optional core_iqtree docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/iqtree:1.6.7 Optional core_iqtree iqtree_model String Substitution model, frequency type (optional) and rate heterogeneity type (optional) used by IQ-TREE. This string follows the IQ-TREE \"-m\" option. For comparison to other tools use HKY for Bactopia, GTR+F+I for Grandeur, GTR+G4 for Nullarbor, GTR+G for Dryad GTR+I+G Optional core_iqtree iqtree_opts String Additional options for IQ-TREE, see http://www.iqtree.org/doc/Command-Reference Optional core_iqtree iqtree_bootstraps String Number of ultrafast bootstrap replicates. Follows IQ-TREE \"-bb\" option. 1000 Optional core_iqtree alrt String Number of replicates to perform SH-like approximate likelihood ratio test (SH-aLRT). Follows IQ-TREE \"-alrt\" option 1000 Optional core_reorder_matrix cpu Int Number of CPUs to allocate to the task 2 Optional core_reorder_matrix disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional core_reorder_matrix docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional core_reorder_matrix memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional core_snp_dists memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional core_snp_dists docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional core_snp_dists disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional core_snp_dists cpu Int Number of CPUs to allocate to the task 1 Optional pan_iqtree memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional pan_iqtree disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pan_iqtree cpu Int Number of CPUs to allocate to the task 4 Optional pan_iqtree alrt String Number of replicates to perform SH-like approximate likelihood ratio test (SH-aLRT). Follows IQ-TREE \"-alrt\" option 1000 Optional pan_iqtree iqtree_model String Substitution model, frequency type (optional) and rate heterogeneity type (optional) used by IQ-TREE. This string follows the IQ-TREE \"-m\" option. For comparison to other tools use HKY for Bactopia, GTR+F+I for Grandeur, GTR+G4 for Nullarbor, GTR+G for Dryad GTR+I+G Optional pan_iqtree iqtree_bootstraps String Number of ultrafast bootstrap replicates. Follows IQ-TREE \"-bb\" option. 1000 Optional pan_iqtree iqtree_opts String Additional options for IQ-TREE, see http://www.iqtree.org/doc/Command-Reference Optional pan_iqtree docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/iqtree:1.6.7 Optional pan_reorder_matrix cpu Int Number of CPUs to allocate to the task 2 Optional pan_reorder_matrix disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pan_reorder_matrix docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional pan_reorder_matrix memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional pan_snp_dists cpu Int Number of CPUs to allocate to the task 1 Optional pan_snp_dists disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional pan_snp_dists docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional pan_snp_dists memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional pirate disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional pirate cpu Int Number of CPUs to allocate to the task 4 Optional pirate nucl Boolean Boolean variable that instructs pirate to create a pangenome on CDS features using nucleotide identity, rather than amino acid identity, if true. FALSE Optional pirate memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional pirate panopt String Additional arguments for Pirate Optional pirate docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/pirate:1.0.5--hdfd78af_0 Optional pirate features String Features to use for pangenome construction [default: CDS] CDS Optional pirate steps String Identity thresholds to use for pangenome construction 50,60,70,80,90,95,98 Optional summarize_data disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional summarize_data docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16 Optional summarize_data memory Int Amount of memory/RAM (in GB) to allocate to the task 1 Optional summarize_data id_column_name String Use in the case your sample IDs are not in the table ID column 1 Optional summarize_data cpu Int Number of CPUs to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/core_gene_snp/#workflow-tasks","title":"Workflow Tasks","text":"By default, the Core_Gene_SNP workflow will begin by analyzing the input sample set using PIRATE. Pirate takes in GFF3 files and classifies the genes into gene families by sequence identity, outputting a pangenome summary file. The workflow will instruct Pirate to create core gene and pangenome alignments using this gene family data. Setting the \"align\" input variable to false will turn off this behavior, and the workflow will output only the pangenome summary. The workflow will then use the core gene alignment from Pirate to infer a phylogenetic tree using IQ-TREE. It will also produce an SNP distance matrix from this alignment using snp-dists. This behavior can be turned off by setting the core_tree input variable to false. The workflow will not create a pangenome tree or SNP-matrix by default, but this behavior can be turned on by setting the pan_tree input variable to true.
The optional summarize_data task performs the following only if all of the data_summary_* and sample_names optional variables are filled out:
\"amrfinderplus_virulence_genes,amrfinderplus_stress_genes\", etc. that can be found within the origin Terra data table.amrfinder_amr_genes column for a sample contains these values: \"aph(3')-IIIa,tet(O),blaOXA-193\", the summarize_data task will check each sample in the set to see if they also have those AMR genes detected.By default, this task appends a Phandango coloring tag to color all items from the same column the same; this can be turned off by setting the optional phandango_coloring variable to false.
Sion C Bayliss, Harry A Thorpe, Nicola M Coyle, Samuel K Sheppard, Edward J Feil, PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria,\u00a0GigaScience, Volume 8, Issue 10, October 2019, giz119,\u00a0https://doi.org/10.1093/gigascience/giz119
Lam-Tung Nguyen, Heiko A. Schmidt, Arndt von Haeseler, Bui Quang Minh, IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies,\u00a0Molecular Biology and Evolution, Volume 32, Issue 1, January 2015, Pages 268\u2013274,\u00a0https://doi.org/10.1093/molbev/msu300
https://github.com/tseemann/snp-dists
"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/","title":"CZGenEpi_Prep","text":""},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Viral PHB v1.3.0 No Set-level"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#czgenepi_prep_phb","title":"CZGenEpi_Prep_PHB","text":"The CZGenEpi_Prep workflow prepares data for upload to the Chan Zuckerberg GEN EPI platform, where phylogenetic trees and additional data processing can occur. This workflow extracts the necessary metadata fields from your Terra table.
"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#inputs","title":"Inputs","text":"In order to enable customization for where certain fields should be pulled from the Terra table, the user can specify different column names in the appropriate location. For example, if the user wants to use the \"clearlabs_fasta\" column for the assembly file instead of the default \"assembly_fasta\" column, they can write \"clearlabs_fasta\" for the assembly_fasta_column_name optional variable.
Variables with both the \"Optional\" and \"Required\" tag require the column (regardless of name) to be present in the data table.
This workflow runs on the set level.
Terra Task Name Variable Type Description Default Value Terra Status czgenepi_prep sample_names Array[String] The array of sample ids you want to prepare for CZ GEN EPI Required czgenepi_prep terra_table_name String The name of the Terra table where the data is hosted Required czgenepi_prep terra_project_name String The name of the Terra project where the data is hosted Required czgenepi_prep terra_workspace_name String The name of the Terra workspace where the data is hosted Required download_terra_table memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional download_terra_table docker String The Docker container to use for the task quay.io/theiagen/terra-tools:2023-06-21 Optional download_terra_table disk_size String The size of the disk used when running this task 1 Optional download_terra_table cpu Int Number of CPUs to allocate to the task 1 Optional czgenepi_prep assembly_fasta_column_name String The column name where the sample's assembly file can be found assembly_fasta Optional, Required czgenepi_prep county_column_name String The column name where the samples' originating county can be found county Optional, Required czgenepi_prep organism String The organism for data preparation. Options: \"mpox\" or \"sars-cov-2\" sars-cov-2 Optional czgenepi_prep is_private Boolean Sets whether sample status is provate, or not true Optional czgenepi_prep genbank_accession_column_name String The column name where the genbank accession for the sample can be found genbank_accession Optional czgenepi_prep country_column_name String The column name where the sample's originating country can be found country Optional, Required czgenepi_prep collection_date_column_name String The column name where the sample's collection date can be found collection_date Optional, Required czgenepi_prep state_column_name String The column name where the sample's originating state can be found state Optional, Required czgenepi_prep continent_column_name String The column name where the sample's originating continent can be found continent Optional, Required czgenepi_prep sequencing_date_column_name String The column name where the sample's sequencing data can be found sequencing_date Optional czgenepi_prep private_id_column_name String The column name where the Private ID for the sample can be found terra_table_name_id Optional, Required czgenepi_wrangling memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional czgenepi_wrangling docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-08-08-2 Optional czgenepi_wrangling disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional czgenepi_wrangling cpu Int Number of CPUs to allocate to the task 1 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#outputs","title":"Outputs","text":"The concatenated_czgenepi_fasta and concatenated_czgenepi_metadata files can be uploaded directly to CZ GEN EPI without any adjustments.
Variable Type Description concatenate_czgenepi_fasta File The concatenated fasta file with the renamed headers (the headers are renamed to account for clearlabs data which has unique headers) concatenate_czgenepi_metadata File The concatenated metadata that was extracted from the terra table using the specified columns czgenepi_prep_version String The version of PHB the workflow is in czgenepi_prep_analysis_date String The date the workflow was run"},{"location":"workflows/phylogenetic_construction/czgenepi_prep/#references","title":"References","text":"CZ GEN EPI Help Center \"Uploading Data\" https://help.czgenepi.org/hc/en-us/articles/6160372401172-Uploading-data
"},{"location":"workflows/phylogenetic_construction/find_shared_variants/","title":"Find_Shared_Variants","text":""},{"location":"workflows/phylogenetic_construction/find_shared_variants/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria, Mycotics PHB v2.0.0 Yes Set-level"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#find_shared_variants_phb","title":"Find_Shared_Variants_PHB","text":"Find_Shared_Variants_PHB is a workflow for concatenating the variant results produced by the Snippy_Variants_PHB workflow across multiple samples and reshaping the data to illustrate variants that are shared among multiple samples.
Find_Shared_Variants Workflow Diagram
"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#inputs","title":"Inputs","text":"The primary intended input of the workflow is the snippy_variants_results output from Snippy_Variants_PHB or the theiaeuk_snippy_variants_results output of the TheiaEuk workflow. Variant results files from other tools may not be compatible at this time.
All variant data included in the sample set should be generated from aligning sequencing reads to the same reference genome. If variant data was generated using different reference genomes, shared variants cannot be identified and results will be less useful.
Terra Task Name Variable Type Description Default Value Terra Status shared_variants_wf concatenated_file_name String String of your choice to prefix output files Required shared_variants_wf samplenames Array[String] The samples to be included in the analysis Required shared_variants_wf variants_to_cat Array[File] The result file from the Snippy_Variants workflow Required cat_variants docker_image String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1\" Optional shared_variants cpu Int Number of CPUs to allocate to the task 1 Optional shared_variants disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional shared_variants docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16\" Optional shared_variants memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#tasks","title":"Tasks","text":"Concatenate Variants Shared Variants Task"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#concatenate_variants_task","title":"Concatenate Variants Task","text":"The cat_variants task concatenates variant data from multiple samples into a single file concatenated_variants. It is very similar to the cat_files task, but also adds a column to the output file that indicates the sample associated with each row of data.
The concatenated_variants file will be in the following format:
Technical Details
Links Task /tasks/utilities/file_handling/task_cat_files.wdl Software Source Code task_cat_files.wdl"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#shared_variants_task","title":"Shared Variants Task","text":"The shared_variants task takes in the concatenated_variants output from the cat_variants task and reshapes the data so that variants are rows and samples are columns. For each variant, samples where the variant was detected are populated with a \"1\" and samples were either the variant was not detected or there was insufficient coverage to call variants are populated with a \"0\". The resulting table is available as the shared_variants_table output.
The shared_variants_table file will be in the following format:
Technical Details
Links Task task_shared_variants.wdl Software Source Code task_shared_variants.wdl"},{"location":"workflows/phylogenetic_construction/find_shared_variants/#outputs","title":"Outputs","text":"The outputs of this workflow are the concatenated_variants file and the shared_variants_table file.
The kSNP3 workflow is for phylogenetic analysis of bacterial genomes using single nucleotide polymorphisms (SNPs). The kSNP3 workflow identifies SNPs amongst a set of genome assemblies, then calculates a number of phylogenetic trees based on those SNPs:
_pan._core.This workflow also features an optional module, summarize_data that creates a presence/absence matrix for the analyzed samples from a list of indicated columns (such as AMR genes, plasmid types etc.). If the phandango_coloring variable is set to true, this will be formatted for visualization in Phandango, else it can be viewed in Excel.
You can learn more about the kSNP3 workflow, including how to visualize the outputs with MicrobeTrace in the following video: \ud83d\udcfa Using KSNP3 in Terra and Visualizing Bacterial Genomic Networks in MicrobeTrace
"},{"location":"workflows/phylogenetic_construction/ksnp3/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status ksnp3_workflow assembly_fasta Array[File] The assembly files to be analyzed Required ksnp3_workflow cluster_name String Free text string used to label output files Required ksnp3_workflow samplename Array[String] The set of sample names Required core_ksnp3_shared_snps_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional core_reorder_matrix cpu Int Number of CPUs to allocate to the task 2 Optional core_reorder_matrix disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional core_reorder_matrix docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional core_reorder_matrix memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional core_snp_dists cpu Int Number of CPUs to allocate to the task 1 Optional core_snp_dists disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional core_snp_dists docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional core_snp_dists memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional ksnp3_task cpu Int Number of CPUs to allocate to the task 4 Optional ksnp3_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ksnp3_task docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ksnp3:3.1 Optional ksnp3_task kmer_size Int The length of kmer containing the SNP you want kSNP3 to use 19 Optional ksnp3_task ksnp3_args String Additional arguments you want kSNP3 to use; e.g., \"-ML\" or \"-NJ\" Optional ksnp3_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional ksnp3_task previous_ksnp3_snps File File with existing SNPs for the current run to be appended to. Optional ksnp3_workflow data_summary_column_names String A comma-separated list of the column names from the sample-level data table for generating a data summary (presence/absence .csv matrix); e.g., \"amrfinderplus_amr_genes,amrfinderplus_virulence_genes\" Optional ksnp3_workflow data_summary_terra_project String The billing project for your current workspace. This can be found after the \"#workspaces/\" section in the workspace's URL Optional ksnp3_workflow data_summary_terra_table String The name of the sample-level Terra data table that will be used for generating a data summary Optional ksnp3_workflow data_summary_terra_workspace String The name of the Terra workspace you are in. This can be found at the top of the webpage, or in the URL after the billing project. Optional ksnp3_workflow midpoint_root_tree Boolean If true, midpoint root the final tree FALSE Optional ksnp3_workflow phandango_coloring Boolean Boolean variable that tells the data summary task and the reorder matrix task to include a suffix that enables consistent coloring on Phandango; by default, this suffix is not added. To add this suffix set this variable to true. FALSE Optional pan_reorder_matrix cpu Int Number of CPUs to allocate to the task 100 Optional pan_reorder_matrix disk_size Int Amount of storage (in GB) to allocate to the task 2 Optional pan_reorder_matrix docker String The Docker container to use for the task 100 Optional pan_reorder_matrix memory Int Amount of memory/RAM (in GB) to allocate to the task us-docker.pkg.dev/general-theiagen/staphb/mykrobe:0.12.1 Optional pan_snp_dists cpu Int Number of CPUs to allocate to the task 1 Optional pan_snp_dists disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional pan_snp_dists docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional pan_snp_dists memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional summarize_data cpu Int Number of CPUs to allocate to the task 8 Optional summarize_data disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional summarize_data docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-03-16 Optional summarize_data id_column_name String If the sample IDs are in a different column to samplenames, it can be passed here and it will be used instead. Optional summarize_data memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/ksnp3/#workflow-actions","title":"Workflow Actions","text":"The ksnp3 workflow is run on the set of assembly files to produce both pan-genome and core-genome phylogenies. This also results in alignment files which - are used by snp-dists to produce a pairwise SNP distance matrix for both the pan-genome and core-genomes.
If you fill out the data_summary_* and sample_names optional variables, you can use the optional summarize_data task. The task takes a comma-separated list of column names from the Terra data table, which should each contain a list of comma-separated items. For example, \"amrfinderplus_virulence_genes,amrfinderplus_stress_genes\" (with quotes, comma separated, no spaces) for these output columns from running TheiaProk. The task checks whether those comma-separated items are present in each row of the data table (sample), then creates a CSV file of these results. The CSV file indicates presence (TRUE) or absence (empty) for each item. By default, the task adds a Phandango coloring tag to group items from the same column, but you can turn this off by setting phandango_coloring to false.
Sample_Name,aph(3')-IIa,blaCTX-M-65,blaOXA-193,tet(O)\nsample1,TRUE,,TRUE,TRUE\nsample2,,,FALSE,TRUE\nsample3,,,FALSE,\nData summary produced using the phandango_coloring option, visualized alongside Newick tree at http://jameshadfield.github.io/phandango/#/main
Example phandango_coloring output
"},{"location":"workflows/phylogenetic_construction/ksnp3/#outputs","title":"Outputs","text":"Variable Type Description ksnp3_core_snp_matrix File The SNP matrix made with the core genome; formatted for Phandango ifphandango_coloring input is true ksnp3_core_snp_matrix_status String Will print either The core SNP matrix was produced OR The core SNP matrix could not be produced ksnp3_core_snp_table File Formatted version of ksnp3_vcf_ref_genome file with only core SNPs, sorted by number of occurrences in the sample set ksnp3_core_tree File The phylogenetic tree made with the core genome ksnp3_docker String The docker image used ksnp3_filtered_metadata File Optional output file with filtered metadata that is only produced if the optional summarize_data task is used. ksnp3_ml_tree File Maximum likelihood tree that is only produced if ksnp3_args includes \"-ML\" ksnp3_nj_tree File Neighbor joining tree that is only produced if ksnp3_args includes \"-NJ\" ksnp3_number_core_snps String Number of core SNPs in the sample set ksnp3_number_snps String Number of SNPs in the sample set ksnp3_pan_snp_matrix File The SNP matrix made with the pangenome; formatted for Phandango if phandango_coloring input is true ksnp3_pan_tree File The phylogenetic tree made with the pangenome ksnp3_snp_dists_version String The version of snp_dists used in the workflow ksnp3_snps File File containing the set of SNPs used in the analysis. Required if more trees are to be appended to the existing one. ksnp3_summarized_data File CSV presence/absence matrix generated by the summarize_data task from the list of columns provided; formatted for Phandango if phandango_coloring input is true ksnp3_vcf_ref_genome File A VCF file containing the variants detected in the core genome ksnp3_vcf_ref_samplename String The name of the (user-supplied) sample used as the reference for calling SNPs. ksnp3_vcf_snps_not_in_ref File A TSV file of the SNPs not present in the reference genome, but were identified by kSNP3. ksnp3_wf_analysis_date String The date the workflow was run ksnp3_wf_version String The version of the repository the workflow is hosted in"},{"location":"workflows/phylogenetic_construction/ksnp3/#references","title":"References","text":"Shea N Gardner, Tom Slezak, Barry G. Hall, kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome,\u00a0Bioinformatics, Volume 31, Issue 17, 1 September 2015, Pages 2877\u20132878,\u00a0https://doi.org/10.1093/bioinformatics/btv271
https://github.com/tseemann/snp-dists
"},{"location":"workflows/phylogenetic_construction/lyve_set/","title":"Lyve_SET","text":""},{"location":"workflows/phylogenetic_construction/lyve_set/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.1.0 Yes Set-level"},{"location":"workflows/phylogenetic_construction/lyve_set/#lyve_set_phb","title":"Lyve_SET_PHB","text":"The Lyve_SET WDL workflow runs the Lyve-SET pipeline developed by Lee Katz et al. for phylogenetic analysis of bacterial genomes using high quality single nucleotide polymorphisms (hqSNPs). The Lyve_SET workflow identifies SNPs amongst a set of samples by mapping sequencing reads to a reference genome, identifying high quality SNPs, and inferring phylogeny using RAxML.
"},{"location":"workflows/phylogenetic_construction/lyve_set/#lyve-set-pipeline-from-lyve-set-paper","title":"Lyve-SET Pipeline (from Lyve-SET paper)","text":"Lyve-SET Workflow Diagram
"},{"location":"workflows/phylogenetic_construction/lyve_set/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status lyveset_workflow dataset_name String Free text string used to label output files Required lyveset_workflow read1 Array[File] Array of read1 files for sample set. We recommend using cleaned rather than raw reads. Required lyveset_workflow read2 Array[File] Array of read2 files for sample set. We recommend using cleaned rather than raw reads. Required lyveset_workflow reference_genome File Path to reference genome in a Terra-accessible Google bucket. For considerations when choosing a reference genome, see: https://github.com/lskatz/lyve-SET/blob/master/docs/FAQ.md Required lyveset allowedFlanking Int Allowed flanking distance in base pairs. Nucleotides this close together cannot be considered as high-quality. 0 Optional lyveset cpu Int Number of CPUs to allocate to the task 4 Optional lyveset disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional lyveset docker_image String Docker image used for running Lyve-SET \"us-docker.pkg.dev/general-theiagen/staphb/lyveset:1.1.4f\" Optional lyveset downsample Boolean If true, downsample all reads to 50x. Approximated according to the ref genome assembly FALSE Optional lyveset fast Boolean Shorthand for--downsample --mapper snap --nomask-phages --nomask-cliffs --sample-sites FALSE Optional lyveset mapper String Which mapper? Choices: \"smalt\", \"snap\" \"smalt\" Optional lyveset mask_cliffs Boolean If true, search for and mask 'Cliffs' in pileups FALSE Optional lyveset mask_phages Boolean If true, search for and mask phages in the reference genome FALSE Optional lyveset memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional lyveset min_alt_frac Float The percent consensus that needs to be reached before a SNP is called. Otherwise, 'N' 0.75 Optional lyveset min_coverage Int Minimum coverage needed before a SNP is called. Otherwise, 'N' 10 Optional lyveset nomatrix Boolean If true, do not create an hqSNP matrix FALSE Optional lyveset nomsa Boolean If true, do not make a multiple sequence alignment FALSE Optional lyveset notrees Boolean If true, do not make phylogenies FALSE Optional lyveset presets String See presets.conf for more information Optional lyveset read_cleaner String Which read cleaner? Choices: \"none\", \"CGP\", \"BayesHammer\" \"CGP\" Optional lyveset sample_sites Boolean If true, randomly choose a genome and find SNPs in a quick and dirty way. Then on the SNP-calling stage, only interrogate those sites for SNPs for each genome (including the randomly-sampled genome). FALSE Optional lyveset snpcaller String Which SNP caller? Choices: \"varscan\", \"vcftools\" \"varscan\" Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/lyve_set/#workflow-actions","title":"Workflow Actions","text":"The Lyve_SET WDL workflow is run using read data from a set of samples. The workflow will produce a pairwise SNP matrix for the sample set and a maximum likelihood phylogenetic tree. Details regarding the default implementation of Lyve_SET and optional modifications are listed below.
read_cleaner input variable.mask_cliffs and mask_phages variables to \"true\".smalt and varscan). Additional options for each are available using the mapper and snpcaller input variables.min_alt_frac and min_coverage input variables.nomsa = true, nomatrix = true, or notrees = true, respectively.For full descriptions of Lyve-SET pipeline outputs, we recommend consulting the Lyve-SET documentation: https://github.com/lskatz/lyve-SET/blob/master/docs/OUTPUT.md
The following output files are populated to the Terra data table. However, please note that certain files may not appear in the data table following a run for two main reasons:
notrees = true, no tree file will appearIn addition to these outputs, all of the files produced by the Lyve-SET pipeline are available in the task-level outputs, including intermediate files and individual bam and vcf files for each sample. These files can be accessed viewing the execution directory for the run.
"},{"location":"workflows/phylogenetic_construction/lyve_set/#references","title":"References","text":"Lyve-SET Katz LS, Griswold T, Williams-Newkirk AJ, Wagner D, Petkau A, et al. (2017) A Comparative Analysis of the Lyve-SET Phylogenomics Pipeline for Genomic Epidemiology of Foodborne Pathogens. Frontiers in Microbiology 8.
"},{"location":"workflows/phylogenetic_construction/mashtree_fasta/","title":"MashTree_FASTA","text":""},{"location":"workflows/phylogenetic_construction/mashtree_fasta/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.1.0 Yes Set-level"},{"location":"workflows/phylogenetic_construction/mashtree_fasta/#mashtree_fasta_phb","title":"MashTree_FASTA_PHB","text":"MashTree_FASTA creates a phylogenetic tree using Mash distances.
Mash distances are representations of how many kmers two sequences have in common. These distances are generated by transforming all kmers from a sequence into an integer value with hashing and Bloom filters. The hashed kmers are sorted and a \"sketch\" is created by only using the kmers that appear at the top of the sorted list. These sketches can be compared by counting the number of hashed kmers they have in common. Mashtree uses a neighbor-joining algorithm to cluster these \"distances\" into phylogenetic trees.
This workflow also features an optional module, summarize_data, that creates a presence/absence matrix for the analyzed samples from a list of indicated columns (such as AMR genes, etc.) that can be used in Phandango.
MashTree_Fasta is run on a set of assembly fastas and creates a phylogenetic tree and matrix. These outputs are passed to a task that will rearrange the matrix to match the order of the terminal ends in the phylogenetic tree.
The optional summarize_data task performs the following only if all of the data_summary_* and sample_names optional variables are filled out:
\"amrfinderplus_virulence_genes,amrfinderplus_stress_genes\", etc. that can be found within the origin Terra data table.amrfinder_amr_genes column for a sample contains these values: \"aph(3')-IIIa,tet(O),blaOXA-193\", the summarize_data task will check each sample in the set to see if they also have those AMR genes detected.By default, this task appends a Phandango coloring tag to color all items from the same column the same; this can be turned off by setting the optional phandango_coloring variable to false.
summarize_data task is used mashtree_matrix File The SNP matrix made mashtree_summarized_data File CSV presence/absence matrix generated by the summarize_data task from the list of columns provided; formatted for Phandango if phandango_coloring input is true mashtree_tree File The phylogenetic tree made mashtree_version String The version of mashtree used in the workflow mashtree_wf_analysis_date String The date the workflow was run mashtree_wf_version String The version of PHB the workflow is hosted in"},{"location":"workflows/phylogenetic_construction/mashtree_fasta/#references","title":"References","text":"Katz, L. S., Griswold, T., Morrison, S., Caravas, J., Zhang, S., den Bakker, H.C., Deng, X., and Carleton, H. A., (2019). Mashtree: a rapid comparison of whole genome sequence files. Journal of Open Source Software, 4(44), 1762,\u00a0https://doi.org/10.21105/joss.01762
Ondov, B. D., Treangen, T. J., Melsted, P., Mallonee, A. B., Bergman, N. H., Koren, S., & Phillippy, A. M. (2016). Mash: Fast genome and metagenome distance estimation using minhash. Genome Biology, 17(1), 132. doi:10.1186/s13059-016-0997-x
"},{"location":"workflows/phylogenetic_construction/snippy_streamline/","title":"Snippy_Streamline","text":""},{"location":"workflows/phylogenetic_construction/snippy_streamline/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.3.0 Yes; some optional features incompatible Set-level"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#snippy_streamline_phb","title":"Snippy_Streamline_PHB","text":"Snippy_Streamline_PHB Workflow Diagram
The Snippy_Streamline workflow is an all-in-one approach to generating a reference-based phylogenetic tree and associated SNP-distance matrix. The workflow can be run in multiple ways with options for:
Centroid task and Assembly_Fetch sub-workflow to find a close reference genome to your datasetassembly_fasta field for automatic reference genome selection.snippy_core_bed)core_genome; default = true)use_gubbins; default=true)iqtree2_model), or allowing IQ-Tree's ModelFinder to identify the best model for your dataset (default)Sequencing Data Requirements
Sequencing data used in the Snippy_Streamline workflow must:
Gubbins, input data should represent complete genomes from the same strain/lineage (e.g. MLST) that share a recent common ancestor.Reference Genomes
If reference genomes have multiple contigs, they will not be compatible with using Gubbins to mask recombination in the phylogenetic tree. The automatic selection of a reference genome by the workflow may result in a reference with multiple contigs. In this case, an alternative reference genome should be sought.
"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#inputs","title":"Inputs","text":"To run Snippy_Streamline, either a reference genome must be provided (reference_genome_file), or you must provide assemblies of the samples in your tree so that the workflow can automatically find and download the closest reference genome to your dataset (via assembly_fasta)
Guidance for optional inputs
Several core and optional tasks can be used to generate the Snippy phylogenetic tree, making it highly flexible and suited to a wide range of datasets. You will need to decide which tasks to use depending on the genomes that you are analyzing. Some guidelines for the optional tasks to use for different genome types are provided below.
Default settings (suitable for most bacteria)The default settings are as follows and are suitable for generating phylogenies for most bacteria
core_genome = true (creates core genome phylogeny)use_gubbins = true (recombination masked)Phylogenies of MTBC are typically constructed
reference_genome_file = gs://theiagen-public-files-rp/terra/theiaprok-files/Mtb_NC_000962.3.fastasnippy_core_bed = gs://theiagen-public-files/terra/theiaprok-files/Mtb_NC_000962.3.beduse_gubbins = falsecore_genome = true (as default)For automatic reference selection by the workflow (optional):
Centroid (optional) Assembly_Fetch workflow (optional)For all cases:
Snippy_Variants workflow Snippy_Tree workflow"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#centroid","title":"Centroid","text":"Centroid selects the most central genome among a list of assemblies by computing pairwise mash distances. In Snippy_Streamline, this centroid assembly is then used to find a closely related reference genome that can be used to generate the tree. In order to use Centroid, should complete the samplenames input.
Centroid Technical Details
Links Task task_centroid.wdl Software Source Code https://github.com/theiagen/centroid Software Documentation https://github.com/theiagen/centroid"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#assembly_fetch","title":"Assembly_Fetch","text":"The Assembly_Fetch workflow compares the centroid assembly with the RefSeq database to identify the closest reference and then downloads this assembly in FASTA format, and optionally also in GFF3 and/or GBFF format. The Reference database is for bacteria by default but this can be changed by adjusting the referenceseeker_db input to the appropriate database. See the Assembly_Fetch workflow documentation for more information.
Call-Caching Disabled
If using Snippy_Streamline workflow (which runs the Assembly_Fetch workflow if no reference genome is provided by user) version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is downloaded fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#snippy_variants","title":"Snippy_Variants","text":"Snippy_Variants aligns reads for each sample against the reference genome. As part of Snippy_Streamline, the only output from this workflow is the snippy_variants_outdir_tarball which is provided in the set-level data table. Please see the full documentation for Snippy_Variants for more information.
This task also extracts QC metrics from the Snippy output for each sample and saves them in per-sample TSV files (snippy_variants_qc_metrics). These per-sample QC metrics include the following columns:
min_coverage threshold (default is 10).These per-sample QC metrics are then combined into a single file (snippy_combined_qc_metrics). The combined QC metrics file includes the same columns as above for all samples. Note that the last set of columns (#rname to meanmapq) may repeat for each chromosome or contig in the reference genome.
QC Metrics for Phylogenetic Analysis
These QC metrics provide valuable insights into the quality and coverage of your sequencing data relative to the reference genome. Monitoring these metrics can help identify samples with low coverage, poor alignment, or potential issues that may affect downstream analyses
Snippy Variants Technical Details
Links Task task_snippy_variants.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#snippy_tree","title":"Snippy_Tree","text":"A simplified version of Snippy_Tree is used to build the phylogeny in the Snippy_Streamline workflow. The tasks undertaken are exactly the same between both workflows, but the user inputs and outputs have been reduced for clarity and ease. Please see the full documentation for Snippy_Tree for more information.
In Snippy Streamline, the nucleotide substitution model used by gubbins will always be GTR+GAMMA.
"},{"location":"workflows/phylogenetic_construction/snippy_streamline/#outputs","title":"Outputs","text":"Variable Type Description snippy_centroid_docker String Docker file used for Centroid snippy_centroid_fasta File FASTA file for the centroid sample snippy_centroid_mash_tsv File TSV file containing mash distances computed by centroid snippy_centroid_samplename String Name of the centroid sample snippy_centroid_version String Centroid version used snippy_cg_snp_matrix File CSV file of core genome pairwise SNP distances between samples, calculated from the final alignment snippy_concatenated_variants File The concatenated variants file snippy_combined_qc_metrics File Combined QC metrics file containing concatenated QC metrics from all samples. snippy_filtered_metadata File TSV recording the columns of the Terra data table that were used in the summarize_data task snippy_final_alignment File Final alignment (FASTA file) used to generate the tree (either after snippy alignment, gubbins recombination removal, and/or core site selection with SNP-sites) snippy_final_tree File Final phylogenetic tree produced by Snippy_Streamline snippy_gubbins_branch_stats File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_gubbins_docker String Docker file used for Gubbins snippy_gubbins_recombination_gff File Recombination statistics in GFF format; these can be viewed in Phandango against the phylogenetic tree snippy_gubbins_version String Gubbins version used snippy_iqtree2_docker String Docker file used for IQTree2 snippy_iqtree2_model_used String Nucleotide substitution model used by IQTree2 snippy_iqtree2_version String IQTree2 version used snippy_msa_snps_summary File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_ncbi_datasets_docker String Docker file used for NCBI datasets snippy_ncbi_datasets_version String NCBI datasets version used snippy_ref File Reference genome used by Snippy snippy_ref_metadata_json File Metadata associated with the refence genome used by Snippy, in JSON format snippy_referenceseeker_database String ReferenceSeeker database used snippy_referenceseeker_docker String Docker file used for ReferenceSeeker snippy_referenceseeker_top_hit_ncbi_accession String NCBI Accession for the top it identified by Assembly_Fetch snippy_referenceseeker_tsv File TSV file of the top hits between the query genome and the Reference Seeker database snippy_referenceseeker_version String ReferenceSeeker version used snippy_snp_dists_docker String Docker file used for SNP-dists snippy_snp_dists_version String SNP-dists version used snippy_snp_sites_docker String Docker file used for SNP-sites snippy_snp_sites_version String SNP-sites version used snippy_streamline_analysis_date String Date of workflow run snippy_streamline_version String Version of Snippy_Streamline used snippy_summarized_data File CSV presence/absence matrix generated by the summarize_data task (within Snippy_Tree workflow) from the list of columns provided snippy_tree_snippy_docker String Docker file used for Snippy in the Snippy_Tree subworkfow snippy_tree_snippy_version String Version of Snippy_Tree subworkflow used snippy_variants_outdir_tarball Array[File] A compressed file containing the whole directory of snippy output files. This is used when running Snippy_Tree snippy_variants_percent_reads_aligned Float Percentage of reads aligned to the reference genome snippy_variants_percent_ref_coverage Float Proportion of the reference genome covered by reads with a depth greater than or equal to themin_coverage threshold (default is 10). snippy_variants_snippy_docker Array[String] Docker file used for Snippy in the Snippy_Variants subworkfow snippy_variants_snippy_version Array[String] Version of Snippy_Tree subworkflow used snippy_wg_snp_matrix File CSV file of whole genome pairwise SNP distances between samples, calculated from the final alignment"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/","title":"Snippy_Streamline_FASTA","text":""},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.3.0 Yes; some optional features incompatible Set-level"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#snippy_streamline_fasta_phb","title":"Snippy_Streamline_FASTA_PHB","text":"This workflow is a FASTA-compatible version of Snippy_Streamline. Please see the Snippy_Streamline documentation for more information regarding the workflow tasks.
Snippy_Streamline_FASTA_PHB Workflow Diagram
The Snippy_Streamline_FASTA workflow is an all-in-one approach to generating a reference-based phylogenetic tree and associated SNP-distance matrix. The workflow can be run in multiple ways with options for:
Centroid task and Assembly_Fetch sub-workflow to find a close reference genome to your datasetsnippy_core_bed)core_genome; default = true)use_gubbins; default=true)iqtree2_model), or allowing IQ-Tree's ModelFinder to identify the best model for your dataset (default)Assembly Data Requirements
Input data used in the Snippy_Streamline_FASTA workflow must:
Gubbins, input data should represent complete genomes from the same strain/lineage (e.g. MLST) that share a recent common ancestor.Reference Genomes
If reference genomes have multiple contigs, they will not be compatible with using Gubbins to mask recombination in the phylogenetic tree. The automatic selection of a reference genome by the workflow may result in a reference with multiple contigs. In this case, an alternative reference genome should be sought.
"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#workflow-tasks","title":"Workflow Tasks","text":"Snippy_Variants QC Metrics Concatenation (optional)"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#snippy_variants","title":"Snippy_Variants QC Metric Concatenation (optional)","text":"Optionally, the user can provide the snippy_variants_qc_metrics file produced by the Snippy_Variants workflow as input to the workflow to concatenate the reports for each sample in the tree. These per-sample QC metrics include the following columns:
min_coverage threshold (default is 10).The combined QC metrics file includes the same columns as above for all samples. Note that the last set of columns (#rname to meanmapq) may repeat for each chromosome or contig in the reference genome.
QC Metrics for Phylogenetic Analysis
These QC metrics provide valuable insights into the quality and coverage of your sequencing data relative to the reference genome. Monitoring these metrics can help identify samples with low coverage, poor alignment, or potential issues that may affect downstream analyses, and we recommend examining them before proceeding with phylogenetic analysis if performing Snippy_Variants and Snippy_Tree separately.
Snippy Variants Technical Details
Links Task task_snippy_variants.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status snippy_streamline_fasta assembly_fasta Array[File] The assembly files for your samples Required snippy_streamline_fasta samplenames Array[String] The names of your samples Required snippy_streamline_fasta tree_name String String of your choice to prefix output files Required snippy_streamline_fasta reference_genome_file File Reference genome in FASTA or GENBANK format (must be the same reference used in Snippy_Variants workflow); provide this if you want to skip the detection of a suitable reference Optional centroid cpu Int Number of CPUs to allocate to the task 1 Optional centroid disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional centroid docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/centroid:0.1.0 Optional centroid memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional ncbi_datasets_download_genome_accession cpu Int Number of CPUs to allocate to the task 1 Optional ncbi_datasets_download_genome_accession disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional ncbi_datasets_download_genome_accession docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ncbi-datasets:14.13.2 Optional ncbi_datasets_download_genome_accession include_gbff3 Boolean When set to true, outputs a gbff3 file (Genbank file) FALSE Optional ncbi_datasets_download_genome_accession include_gff Boolean When set to true, outputs a gff file (Annotation file) FALSE Optional ncbi_datasets_download_genome_accession memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional referenceseeker cpu Int Number of CPUs to allocate to the task 4 Optional referenceseeker disk_size Int Amount of storage (in GB) to allocate to the task 200 Optional referenceseeker docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/referenceseeker:1.8.0--pyhdfd78af_0 Optional referenceseeker memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional referenceseeker referenceseeker_ani_threshold Float Bidirectional average nucleotide identity to use as a cut off for identifying reference assemblies with ReferenceSeeker; default value set according to https://github.com/oschwengers/referenceseeker#description 0.95 Optional referenceseeker referenceseeker_conserved_dna_threshold Float Conserved DNA % to use as a cut off for identifying reference assemblies with ReferenceSeeker; default value set according to https://github.com/oschwengers/referenceseeker#description 0.69 Optional referenceseeker referenceseeker_db File Database to use with ReferenceSeeker gs://theiagen-public-files-rp/terra/theiaprok-files/referenceseeker-bacteria-refseq-205.v20210406.tar.gz Optional snippy_tree_wf call_shared_variants Boolean Activates the shared variants analysis task TRUE Optional snippy_tree_wf core_genome Boolean When \"true\", workflow generates core genome phylogeny; when \"false\", whole genome is used TRUE Optional snippy_tree_wf data_summary_column_names String A comma-separated list of the column names from the sample-level data table for generating a data summary (presence/absence .csv matrix) Optional snippy_tree_wf data_summary_terra_project String The billing project for your current workspace. This can be found after the \"#workspaces/\" section in the workspace's URL Optional snippy_tree_wf data_summary_terra_table String The name of the sample-level Terra data table that will be used for generating a data summary Optional snippy_tree_wf data_summary_terra_workspace String The name of the Terra workspace you are in. This can be found at the top of the webpage, or in the URL after the billing project. Optional snippy_tree_wf gubbins_cpu Int Number of CPUs to allocate to the task 4 Optional snippy_tree_wf gubbins_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_tree_wf gubbins_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/gubbins:3.3--py310pl5321h8472f5a_0 Optional snippy_tree_wf gubbins_memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional snippy_tree_wf iqtree2_bootstraps String Number of replicates for http://www.iqtree.org/doc/Tutorial#assessing-branch-supports-with-ultrafast-bootstrap-approximation (Minimum recommended= 1000) 1000 Optional snippy_tree_wf iqtree2_cpu Int Number of CPUs to allocate to the task 4 Optional snippy_tree_wf iqtree2_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_tree_wf iqtree2_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/iqtree2:2.1.2 Optional snippy_tree_wf iqtree2_memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional snippy_tree_wf iqtree2_model String Nucelotide substitution model to use when generating the final tree with IQTree2. By default, IQtree runs its ModelFinder algorithm to identify the model it thinks best fits your dataset Optional snippy_tree_wf iqtree2_opts String Additional options to pass to IQTree2 Optional snippy_tree_wf midpoint_root_tree Boolean A True/False option that determines whether the tree used in the SNP matrix re-ordering task should be re-rooted or not. Options: true of false TRUE Optional snippy_tree_wf phandango_coloring Boolean Boolean variable that tells the data summary task and the reorder matrix task to include a suffix that enables consistent coloring on Phandango; by default, this suffix is not added. To add this suffix set this variable to true. FALSE Optional snippy_tree_wf snippy_core_bed File User-provided bed file to mask out regions of the genome when creating multiple sequence alignments Optional snippy_tree_wf snippy_core_cpu Int Number of CPUs to allocate to the task 8 Optional snippy_tree_wf snippy_core_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_tree_wf snippy_core_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snippy:4.6.0 Optional snippy_tree_wf snippy_core_memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional snippy_tree_wf snp_dists_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2 Optional snippy_tree_wf snp_sites_cpu Int Number of CPUs to allocate to the task 1 Optional snippy_tree_wf snp_sites_disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_tree_wf snp_sites_docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snp-sites:2.5.1 Optional snippy_tree_wf snp_sites_memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional snippy_tree_wf use_gubbins Boolean When \"true\", workflow removes recombination with gubbins tasks; when \"false\", gubbins is not used TRUE Optional snippy_variants_wf base_quality Int Minimum quality for a nucleotide to be used in variant calling 13 Optional snippy_variants_wf cpu Int Number of CPUs to allocate to the task 4 Optional snippy_variants_wf docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snippy:4.6.0 Optional snippy_variants_wf map_qual Int Minimum mapping quality to accept in variant calling Optional snippy_variants_wf maxsoft Int Number of bases of alignment to soft-clip before discarding the alignment Optional snippy_variants_wf memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional snippy_variants_wf min_coverage Int Minimum read coverage of a position to identify a mutation 10 Optional snippy_variants_wf min_frac Float Minimum fraction of bases at a given position to identify a mutation 0.9 Optional snippy_variants_wf min_quality Int Minimum VCF variant call \"quality\" 100 Optional snippy_variants_wf query_gene String Indicate a particular gene of interest Optional snippy_variants_wf read1 File Internal component, do not modify. Do Not Modify, Optional snippy_variants_wf read2 File Internal component, do not modify. Do Not Modify, Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/snippy_streamline_fasta/#outputs","title":"Outputs","text":"Variable Type Description snippy_centroid_docker String Docker file used for Centroid snippy_centroid_fasta File FASTA file for the centroid sample snippy_centroid_mash_tsv File TSV file containing mash distances computed by centroid snippy_centroid_samplename String Name of the centroid sample snippy_centroid_version String Centroid version used snippy_cg_snp_matrix File CSV file of core genome pairwise SNP distances between samples, calculated from the final alignment snippy_combined_qc_metrics File Combined QC metrics file containing concatenated QC metrics from all samples. snippy_concatenated_variants File The concatenated variants file snippy_filtered_metadata File TSV recording the columns of the Terra data table that were used in the summarize_data task snippy_final_alignment File Final alignment (FASTA file) used to generate the tree (either after snippy alignment, gubbins recombination removal, and/or core site selection with SNP-sites) snippy_final_tree File Final phylogenetic tree produced by Snippy_Streamline snippy_gubbins_branch_stats File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_gubbins_docker String Docker file used for Gubbins snippy_gubbins_recombination_gff File Recombination statistics in GFF format; these can be viewed in Phandango against the phylogenetic tree snippy_gubbins_version String Gubbins version used snippy_iqtree2_docker String Docker file used for IQTree2 snippy_iqtree2_model_used String Nucleotide substitution model used by IQTree2 snippy_iqtree2_version String IQTree2 version used snippy_msa_snps_summary File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_ncbi_datasets_docker String Docker file used for NCBI datasets snippy_ncbi_datasets_version String NCBI datasets version used snippy_ref File Reference genome used by Snippy snippy_ref_metadata_json File Metadata associated with the refence genome used by Snippy, in JSON format snippy_referenceseeker_database String ReferenceSeeker database used snippy_referenceseeker_docker String Docker file used for ReferenceSeeker snippy_referenceseeker_top_hit_ncbi_accession String NCBI Accession for the top it identified by Assembly_Fetch snippy_referenceseeker_tsv File TSV file of the top hits between the query genome and the Reference Seeker database snippy_referenceseeker_version String ReferenceSeeker version used snippy_snp_dists_docker String Docker file used for SNP-dists snippy_snp_dists_version String SNP-dists version used snippy_snp_sites_docker String Docker file used for SNP-sites snippy_snp_sites_version String SNP-sites version used snippy_streamline_analysis_date String Date of workflow run snippy_streamline_version String Version of Snippy_Streamline used snippy_summarized_data File CSV presence/absence matrix generated by the summarize_data task (within Snippy_Tree workflow) from the list of columns provided snippy_tree_snippy_docker String Docker file used for Snippy in the Snippy_Tree subworkfow snippy_tree_snippy_version String Version of Snippy_Tree subworkflow used snippy_variants_outdir_tarball Array[File] A compressed file containing the whole directory of snippy output files. This is used when running Snippy_Tree snippy_variants_snippy_docker Array[String] Docker file used for Snippy in the Snippy_Variants subworkfow snippy_variants_snippy_version Array[String] Version of Snippy_Tree subworkflow used snippy_wg_snp_matrix File CSV file of whole genome pairwise SNP distances between samples, calculated from the final alignment"},{"location":"workflows/phylogenetic_construction/snippy_tree/","title":"Snippy_Tree","text":""},{"location":"workflows/phylogenetic_construction/snippy_tree/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria PHB v2.3.0 Yes; some optional features incompatible Set-level"},{"location":"workflows/phylogenetic_construction/snippy_tree/#snippy_tree_phb","title":"Snippy_Tree_PHB","text":"Snippy_Tree is a workflow for generating high-quality bacterial phylogenies. It produces a phylogenetic tree and pairwise SNP-distance matrix, with the option to summarize additional metadata to visualize with the tree.
The tree produced by Snippy_Tree will always be a maximum-likelihood phylogeny using a reference-based alignment. There are key options for whether to:
core_genome)bed_file)use_gubbins)Snippy_Tree is intended to be run after the Snippy_Variants workflow. It is a set-level workflow that takes in an array of directories generated by the Snippy_Variants workflow, which must be run for each sample that you wish to include in the phylogenetic tree. You should ensure that for all samples included in the phylogeny, Snippy_Variants has been run with identical inputs including the same reference genome. When running the Snippy_Tree workflow, you will need to provide the same reference genome that you used when running Snippy_Variants. Snippy_Variants and Snippy_Tree can both automatically be run by using the Snippy_Streamline workflow.
Sequencing data used in the Snippy_Tree workflow must:
Gubbins, input data should represent whole genomes from the same strain/lineage (e.g. MLST) that share a recent common ancestor.Guidance for optional inputs
Several core and optional tasks can be used to generate the Snippy phylogenetic tree, making it highly flexible and suited to a wide range of datasets. You will need to decide which tasks to use depending on the genomes that you are analyzing. Some guidelines for the optional tasks to use for different genome types are provided below.
Default settings (suitable for most bacteria)The default settings are as follows and are suitable for generating phylogenies for most bacteria
core_genome = true (creates core genome phylogeny)use_gubbins = true (recombination masked)Phylogenies of MTBC are typically constructed
reference_genome_file = gs://theiagen-public-files-rp/terra/theiaprok-files/Mtb_NC_000962.3.fastasnippy_core_bed = gs://theiagen-public-files/terra/theiaprok-files/Mtb_NC_000962.3.beduse_gubbins = falsecore_genome = true (as default)Snippy is a pipeline for calling SNPs and INDELs in haploid genomes. Before running Snippy_Tree, you must run Snippy_Variants, another workflow that uses the Snippy tool to align reads against a reference genome for individual samples. In Snippy_Tree, the snippy tool is used again to generate a whole-genome multiple sequence alignment (fasta file) of reads from all the samples we'd like in our tree.
When generating the multiple sequence alignment, a bed file can be provided by users to mask certain areas of the genome in the alignment. This is particularly relevant for masking known repetitive regions in Mycobacterium tuberculosis genomes, or masking known regions containing phage sequences.
Why do I see snippy_core in Terra?
In Terra, this task is named \"snippy_core\" after the name of the command in the original Snippy tool. Despite the name, this command is NOT being used to make a core genome, but instead a multiple sequence alignment of the whole genome (without any sections masked using a bed file).
Snippy Technical Details
Links Task task_snippy_core.wdl Default software version v4.6.0 (us-docker.pkg.dev/general-theiagen/staphb/snippy:4.6.0) Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_tree/#gubbins_task","title":"Gubbins (optional)","text":"Optional
Gubbins is used when use_gubbins is set to true (default=true).
Genealogies Unbiased By recomBinations In Nucleotide Sequences (Gubbins) identifies and masks genomic regions that are predicted to have arisen via recombination. It works by iteratively identifying loci containing elevated densities of SNPs and constructing phylogenies based on the putative single nucleotide variants outside these regions (for more details, see here). By default, these phylogenies are constructed using RaxML and a GTR-GAMMA nucleotide substitution model, which will be the most suitable model for most bacterial phylogenetics, though this can be modified with the tree_builder and nuc_subst_model inputs.
Gubbins is the industry standard for masking recombination from bacterial genomes when building phylogenies, but limitations to recombination removal exist. Gubbins cannot distinguish recombination from high densities of SNPs that may result from assembly or alignment errors, mutational hotspots, or regions of the genome with relaxed selection. The tool is also intended only to find recombinant regions that are short relative to the length of the genome, so large regions of recombination may not be masked. These factors should be considered when interpreting resulting phylogenetic trees, but overwhelmingly Gubbins improves our ability to understand ancestral relationships between bacterial genomes.
There are few optional inputs for Gubbins that can be modified by the user:
iterations: Gubbins works by iteratively identifying loci containing elevated densities of SNPs, while constructing phylogenies based on the putative single nucleotide variants outside these regions. It may take many iterations for Gubbins to converge on an alignment that it considers free of recombination, especially for phylogenies that contain large numbers of genomes. By default, Gubbins is limited to 5 iterations though this may be increased by the user with the iterationsoptional input (incurring increased computing time and cost, and possibly requiring increased memory allocation).nuc_subst_model, tree_builder and tree_args: When Gubbins constructs phylogenies, it can use a number of phylogenetic inference tools, each with different nucleotide substitution models and tree-building models. By default, the Snippy_Tree workflow uses a GTRGAMMA substitution model and RaxML for tree building (typically suitable for bacterial genomes), but these can be modified by the user depending on the genome sequences being used with the nuc_subst_model and tree_builder optional inputs, respectively. The nucleotide substitution models that are available depend on the tree building algorithm being used (see here). Additional options for generating the phylogenetic trees in Gubbins can be specified with the tree_args optional input, providing an input string that is consistent with the option formats of the Gubbins command.filter_percent: By default, Gubbins removes genomes from the multiple sequence alignment if more than 25 % of the genome is represented by gaps. The percentage of gaps can be modified by the user using the filter_percent optional input.Gubbins Technical Details
Links Task task_gubbins.wdl Software Source Code Gubbins on GitHub Software Documentation Gubbins v3.3 manual Original Publication(s) Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins Default software version us-docker.pkg.dev/general-theiagen/biocontainers/gubbins:3.3--py310pl5321h8472f5a_0"},{"location":"workflows/phylogenetic_construction/snippy_tree/#snp_sites_task","title":"SNP-sites (optional)","text":"Turn on SNP-Sites with core_genome
SNP-sites runs when the core_genome option is set to true.
SNP-sites is used to filter out invariant sites in the whole-genome alignment, thereby creating a core genome alignment for phylogenetic inference. The output is a fasta file containing the core genome of each sample only. If Gubbins has been used, this output fasta will not contain any sites that are predicted to have arisen via recombination.
SNP-sites technical details
Links Task task_snp_sites.wdl Default software version 2.5.1 (us-docker.pkg.dev/general-theiagen/biocontainers/snp-sites:2.5.1--hed695b0_0) Software Source Code SNP-sites on GitHub Software Documentation SNP-sites on GitHub Original Publication(s) SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments"},{"location":"workflows/phylogenetic_construction/snippy_tree/#iqtree2_task","title":"IQTree2","text":"IQTree2 is used to build the final phylogeny. It uses the alignment generated in the previous steps of the workflow. The contents of this alignment will depend on whether any sites were masked with recombination.
The phylogeny is generated using the maximum-likelihood method and a specified nucleotide substitution model. By default, the Snippy_Tree workflow will run Model Finder to determine the most appropriate nucleotide substitution model for your data, but you may specify the nucleotide substitution model yourself using the iqtree2_model optional input (see here for available models).
IQTree will perform assessments of the tree using the Shimodaira\u2013Hasegawa approximate likelihood-ratio test (SH-aLRT test), and ultrafast bootstrapping with UFBoot2, a quicker but less biased alternative to standard bootstrapping. A clade should not typically be trusted if it has less than 80% support from the SH-aLRT test and less than 95% support with ultrafast bootstrapping.
Nucleotide substitution model
When core_genome= true, the default nucleotide substitution model is set to the General Time Reverside model with Gamma distribution (GTR+G).
When the user sets core_genome= false, the default nucleotide substitution model is set to the General Time Reversible model with invariant sites and Gamma distribution (GTR+I+G).
IQTree2 technical details
Links Task task_iqtree2.wdl Software Source Code IQ-TREE on GitHub Software Documentation IQTree documentation for the latest version (not necessarily the version used in this workflow) Original Publication(s) IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era Publication for the SH-alRT test New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0 Publication for ultrafast bootstrapping integration to IQTree Ultrafast Approximation for Phylogenetic Bootstrap; UFBoot2: Improving the Ultrafast Bootstrap Approximation Publication for ModelFinder ModelFinder: fast model selection for accurate phylogenetic estimates"},{"location":"workflows/phylogenetic_construction/snippy_tree/#snp_dists_task","title":"SNP-dists","text":"SNP-dists computes pairwise SNP distances between genomes. It takes the same alignment of genomes used to generate your phylogenetic tree and produces a matrix of pairwise SNP distances between sequences. This means that if you generated pairwise core-genome phylogeny, the output will consist of pairwise core-genome SNP (cgSNP) distances. Otherwise, these will be whole-genome SNP distances. Regardless of whether core-genome or whole-genome SNPs, this SNP distance matrix will exclude all SNPs in masked regions (i.e. masked with a bed file or gubbins).
The SNP-distance output can be visualized using software such as Phandango to explore the relationships between the genomic sequences. The task adds a Phandango coloring tag (:c1) to the column names in the output matrix to ensure that all columns are colored with the same color scheme throughout.
SNP-dists Technical Details
Links Task task_snp_dists.wdl Default software version 0.8.2 (us-docker.pkg.dev/general-theiagen/staphb/snp-dists:0.8.2) Software Source Code SNP-dists on GitHub Software Documentation SNP-dists on GitHub Original Publication(s) Not known to be published"},{"location":"workflows/phylogenetic_construction/snippy_tree/#data_summary_task","title":"Data Summary (optional)","text":"If you fill out the data_summary_* and sample_names optional variables, you can use the optional summarize_data task. The task takes a comma-separated list of column names from the Terra data table, which should each contain a list of comma-separated items. For example, \"amrfinderplus_virulence_genes,amrfinderplus_stress_genes\" (with quotes, comma separated, no spaces) for these output columns from running TheiaProk. The task checks whether those comma-separated items are present in each row of the data table (sample), then creates a CSV file of these results. The CSV file indicates presence (TRUE) or absence (empty) for each item. By default, the task adds a Phandango coloring tag to group items from the same column, but you can turn this off by setting phandango_coloring to false.
Sample_Name,aph(3')-IIa,blaCTX-M-65,blaOXA-193,tet(O)\nsample1,TRUE,,TRUE,TRUE\nsample2,,,FALSE,TRUE\nsample3,,,FALSE,\nData summary produced using the phandango_coloring option, visualized alongside Newick tree at http://jameshadfield.github.io/phandango/#/main
Example phandango_coloring output
Data summary technical details
Links Task task_summarize_data.wdl"},{"location":"workflows/phylogenetic_construction/snippy_tree/#concatenate_variants_task","title":"Concatenate Variants (optional)","text":"The cat_variants task concatenates variant data from multiple samples into a single file concatenated_variants. It is very similar to the cat_files task, but also adds a column to the output file that indicates the sample associated with each row of data.
The concatenated_variants file will be in the following format:
Technical Details
Links Task /tasks/utilities/file_handling/task_cat_files.wdl Software Source Code task_cat_files.wdl"},{"location":"workflows/phylogenetic_construction/snippy_tree/#shared_variants_task","title":"Shared Variants (optional)","text":"The shared_variants task takes in the concatenated_variants output from the cat_variants task and reshapes the data so that variants are rows and samples are columns. For each variant, samples where the variant was detected are populated with a \"1\" and samples were either the variant was not detected or there was insufficient coverage to call variants are populated with a \"0\". The resulting table is available as the shared_variants_table output.
The shared_variants_table file will be in the following format:
Technical Details
Links Task task_shared_variants.wdl Software Source Code task_shared_variants.wdl"},{"location":"workflows/phylogenetic_construction/snippy_tree/#snippy_variants","title":"Snippy_Variants QC Metric Concatenation (optional)","text":"Optionally, the user can provide the snippy_variants_qc_metrics file produced by the Snippy_Variants workflow as input to the workflow to concatenate the reports for each sample in the tree. These per-sample QC metrics include the following columns:
min_coverage threshold (default is 10).The combined QC metrics file includes the same columns as above for all samples. Note that the last set of columns (#rname to meanmapq) may repeat for each chromosome or contig in the reference genome.
QC Metrics for Phylogenetic Analysis
These QC metrics provide valuable insights into the quality and coverage of your sequencing data relative to the reference genome. Monitoring these metrics can help identify samples with low coverage, poor alignment, or potential issues that may affect downstream analyses, and we recommend examining them before proceeding with phylogenetic analysis if performing Snippy_Variants and Snippy_Tree separately.
Snippy Variants Technical Details
Links Task task_snippy_variants.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_tree/#outputs","title":"Outputs","text":"Variable Type Description snippy_cg_snp_matrix File CSV file of core genome pairwise SNP distances between samples, calculated from the final alignment snippy_concatenated_variants File Concatenated snippy_results file across all samples in the set snippy_combined_qc_metrics File Combined QC metrics file containing concatenated QC metrics from all samples. snippy_filtered_metadata File TSV recording the columns of the Terra data table that were used in the summarize_data task snippy_final_alignment File Final alignment (FASTA file) used to generate the tree (either after snippy alignment, gubbins recombination removal, and/or core site selection with SNP-sites) snippy_final_tree File Newick tree produced from the final alignment. Depending on user input for core_genome, the tree could be a core genome tree (default when core_genome is true) or whole genome tree (if core_genome is false) snippy_gubbins_branch_stats File CSV file showing https://github.com/nickjcroucher/gubbins/blob/master/docs/gubbins_manual.md#output-statistics for each branch of the tree snippy_gubbins_docker String Docker file used for running Gubbins snippy_gubbins_recombination_gff File Recombination statistics in GFF format; these can be viewed in Phandango against the phylogenetic tree snippy_gubbins_version String Gubbins version used snippy_iqtree2_docker String Docker file used for running IQTree2 snippy_iqtree2_model_used String Nucleotide substitution model used by IQTree2 snippy_iqtree2_version String IQTree2 version used snippy_msa_snps_summary File TXT file containing summary statistics for each alignment of each input genome against the reference. This indicates how good the alignment is. Pay particular attention to # unaligned sites, and heterogeneous positions. snippy_ref File Reference genome (FASTA or GenBank file) used for generating phylogeny snippy_shared_snp_table File Table illustrating variants shared among samples snippy_snp_dists_docker String Docker file used for running SNP-dists snippy_snp_dists_version String SNP-dists version used snippy_snp_sites_docker String Docker file used for running SNP-sites snippy_snp_sites_version String SNP-sites version used snippy_summarized_data File CSV presence/absence matrix generated by the summarize_data task from the list of columns provided; formatted for Phandango if phandango_coloring input is true snippy_tree_analysis_date String Date of workflow run snippy_tree_snippy_docker String Docker file used for running Snippy snippy_tree_snippy_version String Snippy version used snippy_tree_version String Version of Snippy_Tree workflow snippy_wg_snp_matrix File CSV file of whole genome pairwise SNP distances between samples, calculated from the final alignment"},{"location":"workflows/phylogenetic_construction/snippy_tree/#references","title":"References","text":"Gubbins: Croucher, Nicholas J., Andrew J. Page, Thomas R. Connor, Aidan J. Delaney, Jacqueline A. Keane, Stephen D. Bentley, Julian Parkhill, and Simon R. Harris. 2015. \"Rapid Phylogenetic Analysis of Large Samples of Recombinant Bacterial Whole Genome Sequences Using Gubbins.\" Nucleic Acids Research 43 (3): e15.
SNP-sites: Page, Andrew J., Ben Taylor, Aidan J. Delaney, Jorge Soares, Torsten Seemann, Jacqueline A. Keane, and Simon R. Harris. 2016. \"SNP-Sites: Rapid Efficient Extraction of SNPs from Multi-FASTA Alignments.\" Microbial Genomics 2 (4): e000056.
IQTree: Nguyen, Lam-Tung, Heiko A. Schmidt, Arndt von Haeseler, and Bui Quang Minh. 2015. \"IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies.\" Molecular Biology and Evolution 32 (1): 268\u201374.
"},{"location":"workflows/phylogenetic_construction/snippy_variants/","title":"Snippy_Variants","text":""},{"location":"workflows/phylogenetic_construction/snippy_variants/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Construction Bacteria, Mycotics, Viral PHB v2.3.0 Yes Sample-level"},{"location":"workflows/phylogenetic_construction/snippy_variants/#snippy_variants_phb","title":"Snippy_Variants_PHB","text":"The Snippy_Variants workflow aligns single-end or paired-end reads (in FASTQ format), or assembled sequences (in FASTA format), against a reference genome, then identifies single-nucleotide polymorphisms (SNPs), multi-nucleotide polymorphisms (MNPs), and insertions/deletions (INDELs) across the alignment. If a GenBank file is used as the reference, mutations associated with user-specified query strings (e.g. genes of interest) can additionally be reported to the Terra data table.
Snippy_Variants Workflow Diagram
Example Use Cases
Snippy_Variants may be used to identify these heterogeneous positions by aligning reads to the assembly of the same reads, or to a closely related reference genome and lowering the thresholds to call SNPs.Snippy_Variants produces a BAM file of the reads aligned to the reference genome. This BAM file can be visualized in IGV (see Theiagen Office Hours recordings) to assess the position of a mutation in supporting reads, or if the assembly of the reads was used as a reference, the position in the contig.read2assembly_fasta input and omit read1 and read2.fa, .fasta) or full GenBank (.gbk) format. The mutations identified by Snippy_Variants are highly dependent on the choice of reference genome. Mutations cannot be identified in genomic regions that are present in your query sequence and not the reference.Query String
The query string can be a gene or any other annotation that matches the GenBank file/output VCF EXACTLY
Terra Task Name Variable Type Description Default Value Terra Status snippy_variants_wf reference_genome_file File Reference genome (GenBank file or fasta) Required snippy_variants_wf samplename String Names of samples Required snippy_gene_query cpu Int Number of CPUs to allocate to the task 8 Optional snippy_gene_query disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_gene_query docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-06-21 Optional snippy_gene_query memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional snippy_variants disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional snippy_variants_wf assembly_fasta File Assembly file Optional snippy_variants_wf base_quality Int Minimum quality for a nucleotide to be used in variant calling 13 Optional snippy_variants_wf cpus Int Number of CPUs to use 4 Optional snippy_variants_wf docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/snippy:4.6.0 Optional snippy_variants_wf map_qual Int Minimum mapping quality to accept in variant calling, default from snippy tool is 60 Optional snippy_variants_wf maxsoft Int Number of bases of alignment to soft-clip before discarding the alignment, default from snippy tool is 10 Optional snippy_variants_wf memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional snippy_variants_wf min_coverage Int Minimum read coverage of a position to identify a mutation 10 Optional snippy_variants_wf min_frac Float Minimum fraction of bases at a given position to identify a mutation, default from snippy tool is 0 0.9 Optional snippy_variants_wf min_quality Int Minimum VCF variant call \"quality\" 100 Optional snippy_variants_wf query_gene String Comma-separated strings (e.g. gene names) in which to search for mutations to output to data table Optional snippy_variants_wf read1 File Forward read file Optional snippy_variants_wf read2 File Reverse read file Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_construction/snippy_variants/#workflow-tasks","title":"Workflow Tasks","text":"Snippy_Variants uses Snippy to align reads to the reference and call SNPs, MNPs and INDELs according to optional input parameters. The output includes a file of variants that is then queried using the grep bash command to identify any mutations in specified genes or annotations of interest. The query string MUST match the gene name or annotation as specified in the GenBank file and provided in the output variant file in the snippy_results column.
Quality Control Metrics
Additionally, Snippy_Variants extracts quality control (QC) metrics from the Snippy output for each sample. These per-sample QC metrics are saved in TSV files (snippy_variants_qc_metrics). The QC metrics include:
snippy_variants_percent_reads_aligned output column.min_coverage threshold (default is 10); also available in the snippy_variants_percent_ref_coverage output column.Note that the last set of columns (#rname to meanmapq) may repeat for each chromosome or contig in the reference genome.
QC Metrics for Phylogenetic Analysis
These QC metrics provide valuable insights into the quality and coverage of your sequencing data relative to the reference genome. Monitoring these metrics can help identify samples with low coverage, poor alignment, or potential issues that may affect downstream analyses, and we recommend examining them before proceeding with phylogenetic analysis if performing Snippy_Variants and Snippy_Tree separately.
These per-sample QC metrics can also be combined into a single file (snippy_combined_qc_metrics) in downstream workflows, such as snippy_tree, providing an overview of QC metrics across all samples.
Snippy Variants Technical Details
Links Task task_snippy_variants.wdltask_snippy_gene_query.wdl Software Source Code Snippy on GitHub Software Documentation Snippy on GitHub"},{"location":"workflows/phylogenetic_construction/snippy_variants/#outputs","title":"Outputs","text":"Visualize your outputs in IGV
Output bam/bai files may be visualized using IGV to manually assess read placement and SNP support.
Note on coverage calculations
The outputs from samtools coverage (found in the snippy_variants_coverage_tsv file) may differ from the snippy_variants_percent_ref_coverage due to different calculation methods. samtools coverage computes genome-wide coverage metrics (e.g., the proportion of bases covered at depth \u2265 1), while snippy_variants_percent_ref_coverage uses a user-defined minimum coverage threshold (default is 10), calculating the proportion of the reference genome with a depth greater than or equal to this threshold.
samtools coverage command, providing genome-wide metrics such as the proportion of bases covered (depth \u2265 1), mean depth, and other related statistics. snippy_variants_docker String Docker image for snippy variants task snippy_variants_gene_query_results File CSV file detailing results for mutations associated with the query strings specified by the user snippy_variants_hits String A summary of mutations associated with the query strings specified by the user snippy_variants_num_reads_aligned Int Number of reads that aligned to the reference genome as calculated by samtools view -c command snippy_variants_num_variants Int Number of variants detected between sample and reference genome snippy_variants_outdir_tarball File A compressed file containing the whole directory of snippy output files. This is used when running Snippy_Tree snippy_variants_percent_reads_aligned Float Percentage of reads aligned to the reference genome snippy_variants_percent_ref_coverage Float Proportion of the reference genome covered by reads with a depth greater than or equal to the min_coverage threshold (default is 10). snippy_variants_qc_metrics File TSV file containing quality control metrics for the sample snippy_variants_query String Query strings specified by the user when running the workflow snippy_variants_query_check String Verification that query strings are found in the reference genome snippy_variants_results File CSV file detailing results for all mutations identified in the query sequence relative to the reference snippy_variants_summary File A summary TXT fie showing the number of mutations identified for each mutation type snippy_variants_version String Version of Snippy used snippy_variants_wf_version String Version of Snippy_Variants used"},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/","title":"Samples_to_Ref_Tree","text":""},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Placement Viral PHB v2.1.0 Yes Sample-level, Set-level"},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/#samples_to_ref_tree_phb","title":"Samples_to_Ref_Tree_PHB","text":"Nextclade rapidly places new samples onto an existing reference phylogenetic tree. Phylogenetic placement is done by comparing the mutations of the query sequence (relative to the reference) with the mutations of every node and tip in the reference tree, and finding the node which has the most similar set of mutations. This operation is repeated for each query sequence, until all of them are placed onto the tree. This workflow uses the Nextstrain-maintained nextclade datasets for SARS-CoV-2, mpox, influenza A and B, and RSV-A and RSV-B. The organism must be specified as input in the field organism, and these align with the nextclade dataset names, i.e. \" sars-cov-2\", \"flu_h1n1pdm_ha\", \"flu_h1n1pdm_na\", \"flu_h3n2_ha\", \"flu_h3n2_na\", \"flu_vic_ha\", \"flu_vic_na\", \"flu_yam_ha\", \"hMPXV\", \"hMPXV_B1\", \"MPXV\", \"rsv_a\" and \"rsv_b\".
However, nextclade can be used on any organism as long as an an existing, high-quality input reference tree with representative samples on it is provided, in addition to other optional inputs. Contact us if you need help generating your own mutation-annotated tree, or follow the instructions available on the Augur wiki here.
Placement not construction
This workflow is not for building a tree from scratch, but rather for the placement of new sequences onto an existing high-quality input reference tree with representative samples on it. In effect, query samples are only compared to reference samples and never to the other query samples.
"},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status nextclade_addToRefTree assembly_fasta File A fasta file with query sequence(s) to be placed onto the global tree Required nextclade_addToRefTree nextclade_dataset_name String What nextclade dataset name to run nextclade on; the options are: \"sars-cov-2\", \"flu_h1n1pdm_ha\", \"flu_h1n1pdm_na\", \"flu_h3n2_ha\", \"flu_h3n2_na\", \"flu_vic_ha\", \"flu_vic_na\", \"flu_yam_ha\", \"hMPXV\", \"hMPXV_B1\", \"MPXV\", \"rsv_a\" and \"rsv_b\" Required nextclade_add_ref cpu Int Number of CPUs to allocate to the task 2 Optional nextclade_add_ref disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional nextclade_add_ref docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/nextstrain/nextclade:3.3.1 Optional nextclade_add_ref memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional nextclade_add_ref verbosity String Set the nextclade output verbosity level. Options: off, error, warn, info, debug, trace \"warn\" Optional nextclade_addToRefTree dataset_tag String nextclade dataset tag Uses the dataset tag associated with the nextclade docker image version Optional nextclade_addToRefTree gene_annotations_gff File A genome annotations file for codon-aware alignment, gene translation and calling of aminoacid mutations Uses the genome annotation associated with the nextclade dataset name Optional nextclade_addToRefTree input_ref File An optional FASTA file containing reference sequence. This file should contain exactly 1 sequence. Uses the reference fasta associated with the specified nextclade dataset name Optional nextclade_addToRefTree nextclade_pathogen_json File An optional pathogen JSON file containing configuration and data specific to a pathogen. Uses the reference pathogen JSON file associated with the specified nextclade dataset name Optional nextclade_addToRefTree reference_tree_json File An optional phylogenetic reference tree file which serves as a target for phylogenetic placement Uses the reference tree associated with the specified nextclade dataset name Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_placement/samples_to_ref_tree/#outputs","title":"Outputs","text":"Variable Type Description treeUpdate_auspice_json File Phylogenetic tree with user placed samples treeUpdate_nextclade_docker String Nextclade docker image used treeUpdate_nextclade_json File JSON file with the results of the Nextclade analysis treeUpdate_nextclade_tsv File Tab-delimited file with Nextclade results treeUpdate_nextclade_version String Nextclade version used samples_to_ref_tree_analysis_date String Date of analysis samples_to_ref_tree_version String Version of the Public Health Bioinformatics (PHB) repository used"},{"location":"workflows/phylogenetic_placement/usher/","title":"Usher","text":""},{"location":"workflows/phylogenetic_placement/usher/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Phylogenetic Placement Viral PHB v2.1.0 Yes Sample-level, set-level"},{"location":"workflows/phylogenetic_placement/usher/#usher_phb","title":"Usher_PHB","text":"UShER (Ultrafast Sample Placement on Existing Trees) rapidly places new samples onto an existing phylogeny using maximum parsimony. This workflow uses the UCSC-maintained global trees for SARS-CoV-2, mpox, RSV-A, and RSV-B if those organisms are specified in the organism input field. However, UShER can be used on any organism as long as a mutation-annotated tree (MAT) is provided in protobuf format. Contact us if you need help generating your own mutation-annotated tree, or follow the instructions available on the UShER wiki here.
While this workflow is technically a set-level workflow, it works on the sample-level too. When run on the set-level, the samples are placed with respect to each other.
Terra Task Name Variable Type Description Default Value Terra Status usher_workflow assembly_fasta Array[File] The assembly files for the samples you want to place on the pre-existing; can either be a set of samples, an individual sample, or multiple individual samples Required usher_workflow organism String What organism to run UShER on; the following organism have default global phylogenies and reference files provided: sars-cov-2, mpox, RSV-A, RSV-B. Required usher_workflow tree_name String The output prefix for the uncondensed tree output and the clades output. Required usher cpu Int Number of CPUs to allocate to the task 8 Optional usher disk_size Int Amount of storage (in GB) to allocate to the task 200 Optional usher docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/pathogengenomics/usher:0.6.2 Optional usher memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional usher mutation_annotated_tree_pb File Required for organisms other than sars-cov-2, mpox, RSV-A or RSV-B. This is the mutation-annotated global phylogeny upon which your samples will be placed Optional, Required usher reference_genome File Required for organisms other than sars-cov-2, mpox, RSV-A or RSV-B. This is the reference genome used to determine your sequence's mutations to accurately place the sample on the phylogeny. Optional, Required usher subtree_size Int Indicates how many of the closest-related samples you want to show in a subtree; more subtrees are made if there is more sequence diversity in the set of input samples (multiple subtrees are only generated if this workflow is run on the set level). 20 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/phylogenetic_placement/usher/#outputs","title":"Outputs","text":"Variable Type Description usher_clades File The clades predicted for the samples usher_phb_analysis_date String The date the analysis was run usher_phb_version String The version of PHB the workflow is from usher_protobuf_version String The version of the mutation-annotated protobuf tree (what day and what samples are included, if a default organism was used; otherwise, says it was user-provided) usher_subtree_mutations Array[File] An array of files showing the mutations at each internal node for the subtree usher_subtrees Array[File] An array of subtrees where your samples have been placed usher_uncondensed_tree File The entire global tree with your samples included (warning: may be a very large file if the organism is \"sars-cov-2\") usher_version String The version of UShER used"},{"location":"workflows/public_data_sharing/fetch_srr_accession/","title":"Fetch SRR Accession Workflow","text":""},{"location":"workflows/public_data_sharing/fetch_srr_accession/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Data Import Any Taxa PHB v2.3.0 Yes Sample-level"},{"location":"workflows/public_data_sharing/fetch_srr_accession/#fetch-srr-accession","title":"Fetch SRR Accession","text":"This workflow retrieves the Sequence Read Archive (SRA) accession (SRR) associated with a given sample accession. The primary inputs are BioSample IDs (e.g., SAMN00000000) or SRA Experiment IDs (e.g., SRX000000), which link to sequencing data in the SRA repository.
The workflow uses the fastq-dl tool to fetch metadata from SRA and specifically parses this metadata to extract the associated SRR accession and outputs the SRR accession.
"},{"location":"workflows/public_data_sharing/fetch_srr_accession/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status fetch_srr_metadata sample_accession String SRA-compatible accession, such as a BioSample ID (e.g., \"SAMN00000000\") or SRA Experiment ID (e.g., \"SRX000000\"), used to retrieve SRR metadata. Required fetch_srr_metadata cpu Int Number of CPUs allocated for the task. 2 Optional fetch_srr_metadata disk_size Int Disk space in GB allocated for the task. 10 Optional fetch_srr_metadata docker String Docker image for metadata retrieval.us-docker.pkg.dev/general-theiagen/biocontainers/fastq-dl:2.0.4--pyhdfd78af_0 Optional fetch_srr_metadata memory Int Memory in GB allocated for the task. 8 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/public_data_sharing/fetch_srr_accession/#workflow-tasks","title":"Workflow Tasks","text":"This workflow has a single task that performs metadata retrieval for the specified sample accession.
fastq-dl: Fetches SRR metadata for sample accession When provided a BioSample accession or SRA experiment ID, 'fastq-dl' collects metadata and returns the appropriate SRR accession.
fastq-dl Technical Details
Links Task Task on GitHub Software Source Code fastq-dl Source Software Documentation fastq-dl Documentation Original Publication fastq-dl: A fast and reliable tool for downloading SRA metadata"},{"location":"workflows/public_data_sharing/fetch_srr_accession/#outputs","title":"Outputs","text":"Variable Type Description srr_accession String The SRR accession's associated with the input sample accession. fetch_srr_accession_version String The version of the fetch_srr_accession workflow. fetch_srr_accession_analysis_date String The date the fetch_srr_accession analysis was run."},{"location":"workflows/public_data_sharing/fetch_srr_accession/#references","title":"References","text":"Valieris, R. et al., \"fastq-dl: A fast and reliable tool for downloading SRA metadata.\" Bioinformatics, 2021.
"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/","title":"Mercury_Prep_N_Batch","text":""},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Public Data Sharing Viral PHB v2.3.0 Yes Set-level"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#mercury_prep_n_batch_phb","title":"Mercury_Prep_N_Batch_PHB","text":"Mercury prepares and formats metadata and sequencing files\u00a0located in Google Cloud Platform (GCP) buckets\u00a0for submission to national & international databases, currently NCBI & GISAID. Mercury was initially developed to ingest read, assembly, and metadata files associated with SARS-CoV-2 amplicon reads from clinical samples and format that data for submission per the\u00a0Public Health Alliance for Genomic Epidemiology (PH4GE)'s SARS-CoV-2 Contextual Data Specifications.
Currently, Mercury supports submission preparation for SARS-CoV-2, mpox, and influenza. These organisms have different metadata requirements, and are submitted to different repositories; the following table lists the repositories for each organism & what is supported in Mercury:
BankIt (NCBI) BioSample (NCBI) GenBank (NCBI) GISAID SRA (NCBI)\"flu\" \u2713 \u2713 \"mpox\" \u2713 \u2713 \u2713 \u2713 \"sars-cov-2\" \u2713 \u2713 \u2713 \u2713 Mercury expects data tables made with TheiaCoV
Mercury was designed to work with metadata tables that were partially created after running the TheiaCoV workflows. If you are using a different pipeline, please ensure that the metadata table is formatted correctly. See this file for the hard-coded list of all of the different metadata fields expected for each organism.
"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#metadata-formatters","title":"Metadata Formatters","text":"To help users collect all required metadata, we have created the following Excel spreadsheets that can help you collect the necessary metadata and allow for easy upload of this metadata into your Terra data tables:
For fluFlu Metadata Formatter
Flu uses the same metadata formatter as the Terra_2_NCBI Pathogen BioSample package.
If neither strain nor isolate are found in the Terra data table, Mercury will automatically generate an isolate, using the following format ABRicate flu type / State / sample name / year (ABRicate flu subtype). Example: A/California/Sample-01/2024 (H1N1)
The ABRicate flu type and subtype (abricate_flu_type and abricate_flu_subtype columns) are extracted from your table, and are required to generate the isolate field if it is not provided.
Mpox Metadata Formatter
For sars-cov-2SARS-CoV-2 Metadata Formatter
Usage on Terra
"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#usage-on-terra","title":"Usage on Terra","text":"A note on the using_clearlabs_data\u00a0&\u00a0using_reads_dehosted optional input parameters
The\u00a0using_clearlabs_data\u00a0and\u00a0using_reads_dehosted\u00a0arguments change the default values for the\u00a0read1_column_name,\u00a0assembly_fasta_column_name, and\u00a0assembly_mean_coverage_column_name\u00a0metadata columns. The default values are shown in the table below in addition to what they are changed to depending on what arguments are used.
using_clearlabs_data with\u00a0using_reads_dehosted with both\u00a0 using_clearlabs_data and using_reads_dehosted read1_column_name \"read1_dehosted\" \"clearlabs_fastq_gz\" \"reads_dehosted\" \"reads_dehosted\" assembly_fasta_column_name \"assembly_fasta\" \"clearlabs_fasta\" \"assembly_fasta\" \"clearlabs_fasta\" assembly_mean_coverage_column_name \"assembly_mean_coverage\" \"clearlabs_sequencing_depth\" \"assembly_mean_coverage\" \"clearlabs_sequencing_depth\""},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#inputs","title":"Inputs","text":"Use the sample table for the terra_table_name input
Make sure your entry for terra_table_name is for the sample table! While the root entity needs to be the set table, the input value for terra_table_name should be the sample table.
This workflow runs on the set-level.
Terra Task Name Variable Type Description Default Value Terra Status mercury_prep_n_batch gcp_bucket_uri String Google bucket where your SRA reads will be temporarily stored before transferring to SRA. Example: \"gs://theiagen_sra_transfer\" Required mercury_prep_n_batch sample_names Array[String] The samples you want to submit Required mercury_prep_n_batch terra_project_name String The name of your Terra project. You can find this information in the URL of the webpage of your Terra dashboard. For example, if your URL contains#workspaces/example/my_workspace/ then your project name is example Required mercury_prep_n_batch terra_table_name String The name of the Terra table where your samples can be found. Do not include the entity: prefix, the _id suffix, or the _set_id suffix, just the name of the sample-level data table as listed in the sidebar on lefthand side of the Terra Data tab. Required mercury_prep_n_batch terra_workspace_name String The name of your Terra workspace where your samples can be found. For example, if your URL contains #workspaces/example/my_workspace/ then your project name is my_workspace Required download_terra_table cpu Int Number of CPUs to allocate to the task 1 Optional download_terra_table disk_size Int Amount of storage (in GB) to allocate to the task 10 Optional download_terra_table docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-06-21 Optional download_terra_table memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional mercury cpu Int Number of CPUs to allocate to the task 2 Optional mercury disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional mercury docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/mercury:1.0.9 Optional mercury memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional mercury number_N_threshold Int Only for \"sars-cov-2\" submissions; used to filter out any samples that contain more than the indicated number of Ns in the assembly file 5000 Optional mercury single_end Boolean Set to true if your data is single-end; this ensures that a read2 column is not included in the metadata FALSE Optional mercury skip_county Boolean Use if your Terra table contains a county column that you do not want to include in your submission. FALSE Optional mercury usa_territory Boolean If true, the \"state\" column will be used in place of the \"country\" column. For example, if \"state\" is Puerto Rico, then the GISAID virus name will be\u00a0hCoV-19/Puerto Rico/<name>/<year>. The NCBI\u00a0geo_loc_name\u00a0will be\u00a0\"USA: Puerto Rico\". This optional Boolean variable should only be used with clear understanding of what it does. FALSE Optional mercury using_clearlabs_data Boolean When set to true will change read1_dehosted \u2192 clearlabs_fastq_gz; assembly_fasta \u2192 clearlabs_fasta; assembly_mean_coverage \u2192 clearlabs_sequencing_depth FALSE Optional mercury using_reads_dehosted Boolean When set to true will only change read1_dehosted \u2192 reads_dehosted. Takes priority over the replacement for read1_dehosted made with the using_clearlabs_data Boolean input FALSE Optional mercury vadr_alert_limit Int Only for \"sars-cov-2\" submissions; used to filter out any samples that contain more than the indicated number of vadr alerts 0 Optional mercury_prep_n_batch authors_sbt File Only for \"mpox\" submissions; a file that contains author information. This file can be created here: Optional mercury_prep_n_batch organism String The organism that you want submission prepare for \u2014 each organism requires different metadata fields so please ensure this field is accurate. Options: \"flu\", \"mpox\"\" or \"sars-cov-2\" sars-cov-2 Optional mercury_prep_n_batch output_name String Free text prefix for all output files mercury Optional mercury_prep_n_batch skip_ncbi Boolean Set to true if you only want to prepare GISAID submission files FALSE Optional table2asn cpu Int Number of CPUs to allocate to the task 1 Optional table2asn disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional table2asn docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/ncbi-table2asn:1.26.678 Optional table2asn memory Int Amount of memory/RAM (in GB) to allocate to the task 1 Optional trim_genbank_fastas cpu Int Number of CPUs to allocate to the task 1 Optional trim_genbank_fastas disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional trim_genbank_fastas docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/vadr:1.3 Optional trim_genbank_fastas max_length Int Only for \"sars-cov-2\" submissions; the maximum genome length for trimming terminal ambiguous nucleotides. If your sample's genome is higher than this value, the workflow will error/fail. 30000 Optional trim_genbank_fastas memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional trim_genbank_fastas min_length Int Only for \"sars-cov-2\" submissions; the minimum genome length for trimming terminal ambiguous nucleotides. If your sample's genome is lower than this value, the workflow will error/fail. 50 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#outputs","title":"Outputs","text":"Variable Type Description bankit_sqn_to_email File Only for mpox submission: the sqn file that you will use to submit mpox assembly files to NCBI via email biosample_metadata File BioSample metadata TSV file for upload to NCBI excluded_samples File A file that contains the names and reasons why a sample was excluded from submission. For SARS-CoV-2, there are two sections: First, a section for any samples that failed to meet pre-determined quality thresholds (number_N and vadr_num_alert). Second, a section that includes a table that describes any missing required metadata for each sample. This table has the sample name for rows and any columns that have missing metadata as headers. If a sample is missing a piece of required metadata, the corresponding cell will be blank. However, if a different sample does have metadata for that column, the associated value will appear in the corresponding cell. For flu and mpox, only the second section described above exists. Please see the example below for more details. genbank_fasta File Only for SARS-CoV-2 submission: GenBank fasta file for upload genbank_metadata File Only for SARS-CoV-2 submission: GenBank metadata for upload gisaid_fasta File Only for mpox and SARS-CoV-2 submission: GISAID fasta file for upload gisaid_metadata File Only for mpox and SARS-CoV-2 submission: GISAID metadata for upload mercury_prep_n_batch_analysis_date String Date analysis was run mercury_prep_n_batch_version String Version of the PHB repository that hosts this workflow mercury_script_version String Version of the Mercury tool that was used in this workflow sra_metadata File SRA metadata TSV file for upload An example excluded_samples.tsv file"},{"location":"workflows/public_data_sharing/mercury_prep_n_batch/#example-excluded-samples","title":"An example excluded_samples.tsv file","text":"Due to the nature of tsv files, it may be easier to download and open this file in Excel.
example_excluded_samples.tsv
Samples excluded for quality thresholds:\nsample_name message \nsample2 VADR skipped due to poor assembly\nsample3 VADR number alerts too high: 3 greater than limit of 0\nsample4 Number of Ns was too high: 10000 greater than limit of 5000\n\nSamples excluded for missing required metadata (will have empty values in indicated columns):\ntablename_id organism country library_layout\nsample5 paired\nsample6 SARS-CoV-2 USA\nThis example informs the user that samples 2-4 were excluded for quality reasons (the exact reason is listed in the message column), and that samples 5 and 6 were excluded because they were missing required metadata fields (sample5 was missing the organism and country fields, and sample6 was missing the library_layout field).
This tool can also be used on the command-line. Please see the Mercury GitHub for more information on how to run Mercury with a Docker image or in your local command-line environment.
"},{"location":"workflows/public_data_sharing/terra_2_gisaid/","title":"Terra_2_GISAID","text":""},{"location":"workflows/public_data_sharing/terra_2_gisaid/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Public Data Sharing Viral PHB v1.2.1 Yes Set-level"},{"location":"workflows/public_data_sharing/terra_2_gisaid/#terra_2_gisaid_phb","title":"Terra_2_GISAID_PHB","text":"Terra_2_GISAID programmatically submits SARS-CoV-2 assembly files to GISAID.
This workflow expects data that has been prepared for submission using either Mercury_Batch or Mercury_Prep_N_Batch (recommended).
client-ID
To obtain a client-ID, contact clisupport@gisaid.org and include your username in your request.
The optional variable frameshift_notification has three options that correspond to the associated web-browser options:
GISAID Credentials
Please note that the user must provide either an authentication_file or a gisaid_credentials file to run this workflow; explanations for both can be found in the table below.
This workflow runs on the sample level.
Terra Task Name Variable Type Description Default Value Terra Status Terra_2_GISAID client_id String This value should be filled with the client-ID provided by GISAID Required Terra_2_GISAID concatenated_fastas File The GISAID FASTA file generated by Mercury_Prep_N_Batch (or Mercury_Prep) Required Terra_2_GISAID concatenated_metadata File The GISAID metadata file generated by Mercury_Prep_N_Batch (or Mercury_Prep) Required gisaid_upload authentication_file File [EITHER] The GISAID authentication file generated by running cli3 authenticate for the submitter. Optional, Required gisaid_upload cpu Int Number of CPUs to allocate to the task 1 Optional gisaid_upload disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional gisaid_upload docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/broadinstitute/gisaid-cli:3.0 Optional gisaid_upload frameshift_notification String See top of inputs section for explanation; the notification preference regarding frameshifts in your submission catch_novel Optional gisaid_upload gisaid_credentials File [EITHER] A tab-delimited file containing the submitter's GISAID username followed by their password, used to generate the GISAID authentication file. Optional, Required gisaid_upload memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/public_data_sharing/terra_2_gisaid/#outputs","title":"Outputs","text":"Variable Type Description failed_uploads Boolean The metadata for any failed uploads gisaid_cli_version String The verison of the GISAID CLI tool gisaid_logs File The log files regarding the submission terra_2_gisaid_analysis_date String The date of the analysis terra_2_gisaid_version String The version of the PHB repository that this workflow is hosted in"},{"location":"workflows/public_data_sharing/terra_2_ncbi/","title":"Terra_2_NCBI","text":""},{"location":"workflows/public_data_sharing/terra_2_ncbi/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Public Data Sharing Bacteria, Mycotics Viral PHB v2.3.0 No Set-level"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#terra_2_ncbi_phb","title":"Terra_2_NCBI_PHB","text":"Do not resubmit!
If the Terra_2_NCBI workflow fails, DO NOT resubmit.
Resubmission risks duplicate submissions and future failures.
Contact Theiagen (support@theiagen.com) to determine the reason for failure, and only move forward with Theiagen's guidance.
Key Resources
The Terra_2_NCBI workflow is a programmatic data submission method to share metadata information with NCBI BioSample and paired-end Illumina reads with NCBI SRA directly from Terra without having to use the NCBI portal.
"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#prerequisites","title":"Prerequisites","text":"Before running the Terra_2_NCBI workflowThe user must have access to the NCBI FTP. To gain these credentials, we recommend emailing **sra@ncbi.nlm.nih.gov** a variation of the following example, including all the information:
Hello,
We would like to automate submissions to the Submission Portal using XML metadata to accompany our cloud-hosted data files.\u00a0\u00a0We would like to upload via FTP and need to create a submission group.
Here is the relevant information:
We will be using an existing submission pipeline that is known to work and would like to request that the production folder be activated.\u00a0Thank you for your assistance!
From NCBI, you will need to get in response:
Please confirm that the production folder has been activated, or else the submission pipeline will either fail or only run test submissions and not actually submit to NCBI.
Before you can run the workflow for the first time, we also recommend scheduling a meeting with Theiagen to get additional things set up, including
The configuration file tells the workflow your username and password so you can access the FTP. It also provides important information about who should be contacted regarding the submission. We recommend contacting a member of Theiagen for help in the creation of this configuration file to ensure that everything is formatted correctly.
In order to create BioSamples, you need to choose the correct BioSample package and have the appropriate metadata included in your data table.
Currently, Terra_2_NCBI only supports Pathogen, Virus, and Microbe BioSample packages. Most organisms should be submitted using the Pathogen package unless you have been specifically directed otherwise (either through CDC communications or another reliable source). Definitions of packages supported by Terra_2_NCBI are listed below with more requirements provided via the links:
For each package, we have created a metadata template spreadsheet to help you organize your metadata:
Please note that the pathogen metadata formatter is for the clinical pathogen package, not the environmental pathogen.
We are constantly working on improving these spreadsheets and they will be updated in due course.
"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#running-the-workflow","title":"Running the Workflow","text":"We recommend running a test submission before your first production submission to ensure that all data has been formatted correctly. Please contact Theiagen (support@theiagen.com) to get this set up.
In the test submission, any real BioProject accession numbers you provide will not be recognized. You will have to make a \"fake\" or \"test\" BioProject. This cannot be done through the NCBI portal. Theiagen can provide assistance in creating this as it requires manual command-line work on the NCBI FTP using the account they provided for you.
What's the difference between a test submission and a production submission?A production submission means that your submission using Terra_2_NCBI will be submitted to NCBI as if you were using the online portal. That means that anything you submit on production will be given to the *real* NCBI servers and appear and become searchable on the NCBI website.
A test submission gives your data to a completely detached replica of the production server. This means that any data you submit as a test will behave exactly like a real submission, but since it's detached, nothing will appear on the NCBI website, and anything returned from the workflow (such as BioSample accession numbers) will be fake. If you search for these test BioSample accession numbers on the NCBI website, either (a) nothing will appear, or (b) it will link to a random sample.
If you want your data to be on NCBI, you must run a production submission. Initially, NCBI locks the production folder so that the user doesn't accidentally submit test data to the main database. You must have requested activation of the production folder prior to your first production submission.
"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#inputs","title":"Inputs","text":"This workflow runs on set-level data tables.
Production Submissions
Please note that an optional Boolean variable, submit_to_production, is required for a production submission.
The workflow will perform the following tasks, each highlighted as code
prune_tableformats all incoming metadata for submission.If you are submitting BioSamples:
biosample_submit_tsv_ftp_upload will add_biosample_accessions will
If BioSample accessions fail to be generated, this task ends the workflow and users should contact Theiagen for further support. Otherwise, the workflow will continue and outputs are returned to the Terra data table.
If BioSample accessions were generated or if BioSample submission was skipped
sra_tsv_to_xml converts the SRA metadata (including any generated or pre-provided BioSample accessions) into XML format.ncbi_sftp_upload If the workflow ends successfully, it returns the outputs to the Terra data table and the XML communications from NCBI will say that submission is underway. The workflow does not declare successful sample submission since SRA sometimes takes a while to do this. If the submission was successful, the point of contact for the submission will receive the SRA accessions via email from NCBI.
If the workflow ends unsuccessfully, no outputs will be shown on Terra and the biosample_status output variable will indicate that the BioSample submission failed.
The output files contain information mostly for debugging purposes. Additionally, if your submission is successful, the point of contact for the submission should also receive an email from NCBI notifying them of their submission success.
Variable Description Type biosample_failures Text file listing samples that failed BioSample submission File biosample_metadata Metadata used for BioSample submission in proper BioSample formatting File biosample_report_xmls One or more XML files that contain the response from NCBI regarding your BioSample submission. These can be pretty cryptic, but often contain information to determine if anything went wrong Array[File] biosample_status String showing whether BioSample submission was successful String biosample_submission_xml XML file used to submit your BioSamples to NCBI File excluded_samples Text file listing samples that were excluded from BioSample submission for missing required metadata File generated_accessions Text file mapping the BioSample accession with its sample name. File sra_metadata Metadata used for SRA submission in proper SRA formatting File sra_report_xmls One or more XML files containing the response from NCBI regarding your SRA submission. These can be pretty cryptic, but often contain information to determine if anything went wrong Array[File] sra_submission_xml XML file that was used to submit your SRA reads to NCBI File terra_2_ncbi_analysis_date Date that the workflow was run String terra_2_ncbi_version Version of the PHB repository where the workflow is hosted String An example excluded_samples.tsv file"},{"location":"workflows/public_data_sharing/terra_2_ncbi/#example-excluded-samples","title":"An example excluded_samples.tsv file","text":"Due to the nature of tsv files, it may be easier to download and open this file in Excel.
example_excluded_samples.tsv
Samples excluded for quality thresholds:\nsample_name message \nsample2 VADR skipped due to poor assembly\nsample3 VADR number alerts too high: 3 greater than limit of 0\nsample4 Number of Ns was too high: 10000 greater than limit of 5000\n\nSamples excluded for missing required metadata (will have empty values in indicated columns):\ntablename_id organism country library_layout\nsample5 paired\nsample6 SARS-CoV-2 USA\nThis example informs the user that samples 2-4 were excluded for quality reasons (the exact reason is listed in the message column), and that samples 5 and 6 were excluded because they were missing required metadata fields (sample5 was missing the organism and country fields, and sample6 was missing the library_layout field).
This workflow would not have been possible without the invaluable contributions of Dr. Danny Park.
"},{"location":"workflows/standalone/cauris_cladetyper/","title":"Cauris_CladeTyper","text":"NEEDS WORK!!!!
This page is under construction and will be updated soon.
"},{"location":"workflows/standalone/cauris_cladetyper/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Mycotics PHB v1.0.0 Yes Sample-level"},{"location":"workflows/standalone/cauris_cladetyper/#cauris_cladetyper_phb","title":"Cauris_CladeTyper_PHB","text":"The Cauris_CladeTyper_PHB Workflow is designed to assign clade to Candida auris Whole Genome Sequencing assemblies based on their genomic sequence similarity to the five clade-specific reference files. Clade typing is essential for understanding the epidemiology and evolutionary dynamics of this emerging multidrug-resistant fungal pathogen.
"},{"location":"workflows/standalone/cauris_cladetyper/#inputs","title":"Inputs","text":""},{"location":"workflows/standalone/cauris_cladetyper/#workflow-tasks","title":"Workflow Tasks","text":"The Cauris_Cladetyper Workflow for Candida auris employs GAMBIT for taxonomic identification, comparing whole genome sequencing data against reference databases to accurately classify Candida auris isolates. A custom database featuring five clade-specific Candida auris reference genomes facilitates clade typing. Sequences undergo genomic signature comparison against the custom database, enabling assignment to one of the five Candida auris clades (Clade I to Clade V) based on sequence similarity and phylogenetic relationships. This integrated approach ensures precise clade assignments, crucial for understanding the genetic diversity and epidemiology of Candida auris.
"},{"location":"workflows/standalone/cauris_cladetyper/#outputs","title":"Outputs","text":""},{"location":"workflows/standalone/concatenate_illumina_lanes/","title":"Concatenate Illumina Lanes","text":""},{"location":"workflows/standalone/concatenate_illumina_lanes/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB 2.3.0 Yes Sample-level"},{"location":"workflows/standalone/concatenate_illumina_lanes/#concatenate_illumina_lanes_phb","title":"Concatenate_Illumina_Lanes_PHB","text":"Some Illumina machines produce multi-lane FASTQ files for a single sample. This workflow concatenates the multiple lanes into a single FASTQ file per read type (forward or reverse).
"},{"location":"workflows/standalone/concatenate_illumina_lanes/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status concatenate_illumina_lanes read1_lane1 File The first lane for the forward reads Required concatenate_illumina_lanes read1_lane2 File The second lane for the forward reads Required concatenate_illumina_lanes samplename String The name of the sample, used to name the output files Required cat_lanes cpu Int Number of CPUs to allocate to the task 2 Optional cat_lanes disk_size Int Amount of storage (in GB) to allocate to the task 50 Optional cat_lanes docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/utility:1.2\" Optional cat_lanes memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional concatenate_illumina_lanes read1_lane3 File The third lane for the forward reads Optional concatenate_illumina_lanes read1_lane4 File The fourth lane for the forward reads Optional concatenate_illumina_lanes read2_lane1 File The first lane for the reverse reads Optional concatenate_illumina_lanes read2_lane2 File The second lane for the reverse reads Optional concatenate_illumina_lanes read2_lane3 File The third lane for the reverse reads Optional concatenate_illumina_lanes read2_lane4 File The fourth lane for the reverse reads Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/standalone/concatenate_illumina_lanes/#workflow-tasks","title":"Workflow Tasks","text":"This workflow concatenates the Illumina lanes for forward and (if provided) reverse reads. The output files are named as followed:
<samplename>_merged_R1.fastq.gz<samplename>_merged_R2.fastq.gzThe GAMBIT_Query_PHB workflow performs taxon assignment of a genome assembly using the GAMBIT task.
"},{"location":"workflows/standalone/gambit_query/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status gambit_query assembly_fasta File Assembly file in FASTA format Required gambit_query samplename String Sample name Required gambit cpu Int Number of CPUs to allocate to the task 8 Optional gambit disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional gambit memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional gambit docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/gambit:1.0.0\" Optional gambit gambit_db_genomes File Database of metadata for assembled query genomes; requires complementary signatures file. If not provided, uses default database \"/gambit-db\" \"gs://gambit-databases-rp/2.0.0/gambit-metadata-2.0.0-20240628.gdb\" Optional gambit gambit_db_signatures File Signatures file; requires complementary genomes file. If not specified, the file from the docker container will be used. \"gs://gambit-databases-rp/2.0.0/gambit-signatures-2.0.0-20240628.gs\" Optional"},{"location":"workflows/standalone/gambit_query/#workflow-tasks","title":"Workflow Tasks","text":"GAMBIT determines the taxon of the genome assembly using a k-mer based approach to match the assembly sequence to the closest complete genome in a database, thereby predicting its identity. Sometimes, GAMBIT can confidently designate the organism to the species level. Other times, it is more conservative and assigns it to a higher taxonomic rank.
For additional details regarding the GAMBIT tool and a list of available GAMBIT databases for analysis, please consult the GAMBIT tool documentation.
GAMBIT Technical Details
Links Task task_gambit.wdl Software Source Code GAMBIT on GitHub Software Documentation GAMBIT ReadTheDocs Original Publication(s) GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification"},{"location":"workflows/standalone/gambit_query/#outputs","title":"Outputs","text":"Variable Type Description gambit_closest_genomes File CSV file listing genomes in the GAMBIT database that are most similar to the query assembly gambit_db_version String Version of the GAMBIT database used gambit_docker String GAMBIT Docker used gambit_predicted_taxon String Taxon predicted by GAMBIT gambit_predicted_taxon_rank String Taxon rank of GAMBIT taxon prediction gambit_query_wf_analysis_date String Date of analysis gambit_query_wf_version String PHB repository version gambit_report File GAMBIT report in a machine-readable format gambit_version String Version of gambit software usedGAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification. Lumpe et al. PLOS ONE, 2022. DOI: 10.1371/journal.pone.0277575
"},{"location":"workflows/standalone/kraken2/","title":"Kraken2","text":""},{"location":"workflows/standalone/kraken2/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.3.0 Yes Sample-level"},{"location":"workflows/standalone/kraken2/#kraken2-workflows","title":"Kraken2 Workflows","text":"The Kraken2 workflows assess the taxonomic profile of raw sequencing data (FASTQ files).
Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
There are three Kraken2 workflows:
Kraken2_PE is compatible with Illumina paired-end dataKraken2_SE is compatible with Illumina single-end dataKraken2_ONT is compatible with Oxford Nanopore dataBesides the data input types, there are minimal differences between these two workflows.
Kraken2 Workflow Diagram
"},{"location":"workflows/standalone/kraken2/#databases","title":"Databases","text":"Database selection
The Kraken2 software is database-dependent and taxonomic assignments are highly sensitive to the database used. An appropriate database should contain the expected organism(s) (e.g. Escherichia coli) and other taxa that may be present in the reads (e.g. Citrobacter freundii, a common contaminant).
"},{"location":"workflows/standalone/kraken2/#suggested-databases","title":"Suggested databases","text":"Database name Database Description Suggested Applications GCP URI (for usage in Terra) Source Database Size (GB) Date of Last Update Kalamari v5.1 Kalamari is a database of complete public assemblies, that has been fine-tuned for enteric pathogens and is backed by trusted institutions. Full list available here ( in chromosomes.tsv and plasmids.tsv) Single-isolate enteric bacterial pathogen analysis (Salmonella, Escherichia, Shigella, Listeria, Campylobacter, Vibrio, Yersinia)gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2.kalamari_5.1.tar.gz \u2023 1.5 18/5/2022 standard 8GB Standard RefSeq database (archaea, bacteria, viral, plasmid, human, UniVec_Core) capped at 8GB Prokaryotic or viral organisms, but for enteric pathogens, we recommend Kalamari gs://theiagen-large-public-files-rp/terra/databases/kraken2/k2_standard_08gb_20240112.tar.gz https://benlangmead.github.io/aws-indexes/k2 7.5 12/1/2024 standard 16GB Standard RefSeq database (archaea, bacteria, viral, plasmid, human, UniVec_Core) capped at 16GB Prokaryotic or viral organisms, but for enteric pathogens, we recommend Kalamari gs://theiagen-large-public-files-rp/terra/databases/kraken2/k2_standard_16gb_20240112.tar.gz https://benlangmead.github.io/aws-indexes/k2 15 12/1/2024 standard Standard RefSeq database (archaea, bacteria, viral, plasmid, human, UniVec_Core) Prokaryotic or viral organisms, but for enteric pathogens, we recommend Kalamari gs://theiagen-large-public-files-rp/terra/databases/kraken2/k2_standard_20240112.tar.gz https://benlangmead.github.io/aws-indexes/k2 72 18/4/2023 viral RefSeq viral Viral metagenomics gs://theiagen-large-public-files-rp/terra/databases/kraken2/k2_viral_20240112.tar.gz https://benlangmead.github.io/aws-indexes/k2 0.6 12/1/2024 EuPathDB48 Eukaryotic pathogen genomes with contaminants removed. Full list available here Eukaryotic organisms (Candida spp., Aspergillus spp., etc) gs://theiagen-public-files-rp/terra/theiaprok-files/k2_eupathdb48_20201113.tar.gz https://benlangmead.github.io/aws-indexes/k2 30.3 13/11/2020 EuPathDB48 Eukaryotic pathogen genomes with contaminants removed. Full list available here Eukaryotic organisms (Candida spp., Aspergillus spp., etc) gs://theiagen-large-public-files-rp/terra/databases/kraken/k2_eupathdb48_20230407.tar.gz https://benlangmead.github.io/aws-indexes/k2 11 7/4/2023"},{"location":"workflows/standalone/kraken2/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status Workflow *workflow_name kraken2_db File A Kraken2 database in .tar.gz format Required ONT, PE, SE *workflow_name read1 File Required ONT, PE, SE *workflow_name read2 File Required for PE only PE *workflow_name samplename String Required ONT, PE, SE kraken2_pe or kraken2_se classified_out String Allows user to rename the classified FASTQ files output. Must include .fastq as the suffix classified#.fastq Optional ONT, PE, SE kraken2_pe or kraken2_se cpu Int Number of CPUs to allocate to the task 4 Optional ONT, PE, SE kraken2_pe or kraken2_se disk_size Int GB of storage to request for VM used to run the kraken2 task. Increase this when using large (>30GB kraken2 databases such as the \"k2_standard\" database) 100 Optional ONT, PE, SE kraken2_pe or kraken2_se docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/kraken2:2.1.2-no-db Optional ONT, PE, SE kraken2_pe or kraken2_se kraken2_args String Allows a user to supply additional kraken2 command-line arguments Optional ONT, PE, SE kraken2_pe or kraken2_se memory Int Amount of memory/RAM (in GB) to allocate to the task 32 Optional ONT, PE, SE kraken2_pe or kraken2_se unclassified_out String Allows user to rename unclassified FASTQ files output. Must include .fastq as the suffix unclassified#.fastq Optional ONT, PE, SE krona cpu Int Number of CPUs to allocate to the task 4 Optional PE, SE krona disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional PE, SE krona docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/biocontainers/krona:2.7.1--pl526_5 Optional PE, SE krona memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional PE, SE kraken2_recalculate_abundances cpu Int Number of CPUs to allocate to the task 4 Optional ONT kraken2_recalculate_abundances disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional ONT kraken2_recalculate_abundances docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-08-28-v4 Optional ONT kraken2_recalculate_abundances memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional ONT kraken2_recalculate_abundances target_organism String Target organism for the kraken2 abundance to be exported to the data table Optional ONT version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional ONT, PE, SE version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional ONT, PE, SE"},{"location":"workflows/standalone/kraken2/#outputs","title":"Outputs","text":"Variable Type Description kraken2_classified_read1 File FASTQ file of classified forward/R1 reads kraken2_classified_read2 File FASTQ file of classified reverse/R2 reads (if PE) kraken2_classified_report File Standard Kraken2 output report. TXT filetype, but can be opened in Excel as a TSV file kraken2_docker String Docker image used to run kraken2 kraken2_*_wf_analysis_date String Date the workflow was run kraken2_*_wf_version String Workflow version kraken2_report File TXT document describing taxonomic prediction of every FASTQ record. This file is usually very large and cumbersome to open and view kraken2_unclassified_read1 File FASTQ file of unclassified forward/R1 reads kraken2_unclassified_read2 File FASTQ file of unclassified reverse/R2 reads (if PE) kraken2_version String kraken2 version krona_docker String Docker image used to run krona (if PE or SE) krona_html File HTML report of krona with visualisation of taxonomic classification of reads (if PE or SE) krona_version String krona version (if PE or SE)"},{"location":"workflows/standalone/kraken2/#interpretation-of-results","title":"Interpretation of results","text":"The most important outputs of the Kraken2 workflows are the kraken2_report files. These will include a breakdown of the number of sequences assigned to a particular taxon, and the percentage of reads assigned. A complete description of the report format can be found here.
When assessing the taxonomic identity of a single isolate's sequence, it is normal that a few reads are assigned to very closely rated taxa due to the shared sequence identity between them. \"Very closely related taxa\" may be genetically similar species in the same genus, or taxa with which the dominant species have undergone horizontal gene transfer. Unrelated taxa or a high abundance of these closely related taxa is indicative of contamination or sequencing of non-target taxa. Interpretation of the results is dependent on the biological context.
Example Kraken2 reportBelow is an example kraken2_report for a Klebsiella pneumoniae sample. Only the first 30 lines are included here since rows near the bottom are often spurious results with only a few reads assigned to a non-target organism.
From this report, we can see that 84.35 % of the reads were assigned at the species level (S in the 4th column) to \"Klebsiella pneumoniae\". Given almost 6 % of reads were \"unclassified\" and ~2 % of reads were assigned to very closely related taxa (in the Klebsiella genus), this suggests the reads are from Klebsiella pneumoniae with very little -if any- read contamination.
5.98 108155 108155 U 0 unclassified\n 94.02 1699669 0 C 1 \n 94.02 1699669 1862 C1 131567 cellular organisms\n 93.91 1697788 2590 D 2 Bacteria\n 93.75 1694805 6312 P 1224 Proteobacteria\n 93.39 1688284 37464 C 1236 Gammaproteobacteria\n 91.31 1650648 35278 O 91347 Enterobacterales\n 89.31 1614639 43698 F 543 Enterobacteriaceae\n 86.40 1561902 22513 G 570 Klebsiella\n **84.35 1524918 1524918 S 573 Klebsiella pneumoniae**\n 0.75 13596 13596 S 548 Klebsiella aerogenes\n 0.03 600 600 S 244366 Klebsiella variicola\n 0.01 253 253 S 571 Klebsiella oxytoca\n 0.00 17 17 S 1134687 Klebsiella michiganensis\n 0.00 3 0 G1 2608929 unclassified Klebsiella\n 0.00 3 3 S 1972757 Klebsiella sp. PO552\n 0.00 2 2 S 1463165 Klebsiella quasipneumoniae\n 0.17 3035 129 G 590 Salmonella\n 0.15 2728 909 S 28901 Salmonella enterica\n 0.03 582 582 S1 9000010 Salmonella enterica subsp. IIa\n 0.02 306 306 S1 59201 Salmonella enterica subsp. enterica\n 0.01 230 230 S1 9000014 Salmonella enterica subsp. IIIa\n 0.01 221 221 S1 9000015 Salmonella enterica subsp. IIIb\n 0.01 136 136 S1 9000016 Salmonella enterica subsp. IX\n 0.01 132 132 S1 9000011 Salmonella enterica subsp. IIb\n 0.01 122 122 S1 59208 Salmonella enterica subsp. VII\n 0.00 41 41 S1 59207 Salmonella enterica subsp. indica\n 0.00 25 25 S1 9000017 Salmonella enterica subsp. X\n 0.00 24 24 S1 9000009 Salmonella enterica subsp. VIII\n 0.01 178 178 S 54736 Salmonella bongori\nKrona produces an interactive report that allows hierarchical data, such as the one from Kraken2, to be explored with zooming, multi-layered pie charts. These pie charts are intuitive and highly responsive.
Krona will only output hierarchical results for bacterial organisms in its current implementation.
Example Krona reportBelow is an example of the krona_html for a metagenomic sample. Taxonomic rank is organised from the centre of the pie chart to the edge, with each slice representing the relative abundance of a given taxa in the sample.
Kraken2 Technical Details
Links Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown Original Publication(s) Improved metagenomic analysis with Kraken 2"},{"location":"workflows/standalone/ncbi_amrfinderplus/","title":"NCBI-AMRFinderPlus","text":""},{"location":"workflows/standalone/ncbi_amrfinderplus/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Bacteria, Mycotics PHB v2.2.0 Yes Sample-level"},{"location":"workflows/standalone/ncbi_amrfinderplus/#ncbiamrfinderplus_phb","title":"NCBIAMRFinderPlus_PHB","text":"AMRFinderPlus identifies acquired antimicrobial resistance (AMR) genes, virulence genes, and stress genes. Such AMR genes confer resistance to antibiotics, metals, biocides, heat, or acid. For some taxa (see here), AMRFinderPlus will provide taxa-specific results including filtering out genes that are almost ubiquitous in the taxa (intrinsic genes) and identifying resistance-associated point mutations. In TheiaProk, the taxon used by AMRFinderPlus is specified based on the gambit_predicted_taxon or a user-provided expected_taxon.
You can check if a gene or point mutation is in the AMRFinderPlus database here, find the sequences of reference genes here, and search the query Hidden Markov Models (HMMs) used by AMRFinderPlus to identify AMR genes and some stress and virulence proteins (here). The AMRFinderPlus database is updated frequently. You can ensure you are using the most up-to-date version by specifying the docker image as a workflow input. You might like to save this docker image as a workspace data element to make this easier.
"},{"location":"workflows/standalone/ncbi_amrfinderplus/#use-cases","title":"\ud83d\udccb Use Cases","text":"Feldgarden M, Brover V, Gonzalez-Escalona N, Frye JG, Haendiges J, Haft DH, Hoffmann M, Pettengill JB, Prasad AB, Tillman GE, Tyson GH, Klimke W. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep. 2021 Jun 16;11(1):12728. doi: 10.1038/s41598-021-91456-0. PMID: 34135355; PMCID: PMC8208984. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8208984/
https://github.com/ncbi/amr
"},{"location":"workflows/standalone/ncbi_scrub/","title":"NCBI_Scrub","text":""},{"location":"workflows/standalone/ncbi_scrub/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.2.1 Yes Sample-level"},{"location":"workflows/standalone/ncbi_scrub/#ncbi-scrub-workflows","title":"NCBI Scrub Workflows","text":"NCBI Scrub, also known as the human read removal tool (HRRT), is based on the SRA Taxonomy Analysis Tool that will take as input a FASTQ file, and produce as output a FASTQ file in which all reads identified as potentially of human origin are either removed (default) or masked with 'N'. There are three Kraken2 workflows:
NCBI_Scrub_PE is compatible with Illumina paired-end dataNCBI_Scrub_SE is compatible with Illumina single-end dataThis workflow is composed of two tasks, one to dehost the input reads and another to screen the clean reads with kraken2 and the viral+human database.
ncbi_scrub: human read removal tool Briefly, the HRRT employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records and subtracts any k-mers found in non-Eukaryota RefSeq records. The remaining set of k-mers compose the database used to identify human reads by the removal tool.
Tool Name Technical Details
Links Task task_ncbi_scrub.wdl Software Source Code HRRT on GitHub Software Documentation HRRT on NCBIkraken2: taxonomic profiling Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.
Kraken2 is run on the set of raw reads, provided as input, as well as the set of clean reads that are resulted from the read_QC_trim workflow
Database-dependent
TheiaCoV automatically uses a viral-specific Kraken2 database.
Kraken2 Technical Details
Links Task task_kraken2.wdl Software Source Code Kraken2 on GitHub Software Documentation https://github.com/DerrickWood/kraken2/wiki Original Publication(s) Improved metagenomic analysis with Kraken 2"},{"location":"workflows/standalone/ncbi_scrub/#outputs","title":"Outputs","text":"Variable Type Description Workflow kraken_human_dehosted Float Percent of human read data detected using the Kraken2 software after host removal PE, SE kraken_report_dehosted File Full Kraken report after host removal PE, SE kraken_sc2_dehosted Float Percent of SARS-CoV-2 read data detected using the Kraken2 software after host removal PE, SE kraken_version_dehosted String Version of Kraken2 software used PE, SE ncbi_scrub_docker String Docker image used to run HRRT PE, SE ncbi_scrub_human_spots_removed Int Number of spots removed (or masked) PE, SE ncbi_scrub_pe_analysis_date String Date of analysis PE, SE ncbi_scrub_pe_version String Version of HRRT software used PE, SE read1_dehosted File Dehosted forward reads PE, SE read2_dehosted File Dehosted reverse reads PE"},{"location":"workflows/standalone/rasusa/","title":"RASUSA","text":""},{"location":"workflows/standalone/rasusa/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.0.0 Yes Sample-level"},{"location":"workflows/standalone/rasusa/#rasusa_phb","title":"RASUSA_PHB","text":"RASUSA functions to randomly downsample the number of raw reads to a user-defined threshold.
"},{"location":"workflows/standalone/rasusa/#use-cases","title":"\ud83d\udccb Use Cases","text":"Call-caching disabled
If using RASUSA_PHB workflow version v2.0.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is downloaded fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/standalone/rasusa/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Attribute Terra Status rasusa_workflow coverage Float Use to specify the desired coverage of reads after downsampling; actual coverage of subsampled reads will not be exact and may be slightly higher; always check the estimated clean coverage after performing downstream workflows to verify coverage values, when necessary Required rasusa_workflow genome_length String Input the approximate genome size expected in quotations; this is used to determine the number of bases required to achieve the desired coverage; acceptable metric suffixes include:b, k, m, g, and t for base, kilo, mega, giga, and tera, respectively Required rasusa_workflow read1 File FASTQ file containing read1 sequences Required rasusa_workflow read2 File FASTQ file containing read2 sequences Required rasusa_workflow samplename String Name of the sample to be analyzed Required rasusa_task bases String Explicitly define the number of bases required in the downsampled reads in quotations; when used, genome size and coverage are ignored; acceptable metric suffixes include: b, k, m, g, and t for base, kilo, mega, giga, and tera, respectively Optional rasusa_task cpu Int Number of CPUs to allocate to the task 4 Optional rasusa_task disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional rasusa_task docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/staphb/rasusa:0.7.0\" Optional rasusa_task frac Float Explicitly define the fraction of reads to keep in the subsample; when used, genome size and coverage are ignored; acceptable inputs include whole numbers and decimals, e.g. 50.0 will leave 50% of the reads in the subsample Optional rasusa_task memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional rasusa_task num Int Optional: explicitly define the number of reads in the subsample; when used, genome size and coverage are ignored; acceptable metric suffixes include: b, k, m, g, and t for base, kilo, mega, giga, and tera, respectively Optional rasusa_task seed Int Use to assign a name to the \"random seed\" that is used by the subsampler; i.e. this allows the exact same subsample to be produced from the same input file/s in subsequent runs when providing the seed identifier; do not input values for random downsampling Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/standalone/rasusa/#outputs","title":"Outputs","text":"Variable Type Description rasusa_version String Version of RASUSA used for the analysis rasusa_wf_analysis_date String Date of analysis rasusa_wf_version String Version of PHB used for the analysis read1_subsampled File New read1 FASTQ files downsampled to desired coverage read2_subsampled File New read2 FASTQ files downsampled to desired coverage Don't Forget!
Remember to use the subsampled reads in downstream analyses with this.read1_subsampled and this.read2_subsampled inputs.
Verify
Confirm reads were successfully subsampled before downstream analyses by comparing read file size/s to the original read file size/s
View file sizes by clicking on the read file listed in the Terra data table and looking at the file size
"},{"location":"workflows/standalone/rasusa/#references","title":"References","text":"Hall, M. B., (2022). Rasusa: Randomly subsample sequencing reads to a specified coverage. Journal of Open Source Software, 7(69), 3941,\u00a0https://doi.org/10.21105/joss.03941
"},{"location":"workflows/standalone/rename_fastq/","title":"Rename_FASTQ","text":""},{"location":"workflows/standalone/rename_fastq/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.1.0 Yes Sample-level"},{"location":"workflows/standalone/rename_fastq/#rename_fastq_phb","title":"Rename_FASTQ_PHB","text":"This sample-level workflow receives a read file or a pair of read files (FASTQ), compressed or uncompressed, and returns a new, renamed and compressed FASTQ file.
"},{"location":"workflows/standalone/rename_fastq/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status rename_fastq_files new_filename String New name for the FASTQ file(s) Required rename_fastq_files read1 File FASTQ file containing read1 sequences Required rename_fastq_files read2 File FASTQ file containing read2 sequences Optional rename_PE_files or rename_SE_files cpu Int Number of CPUs to allocate to the task 2 Optional rename_PE_files or rename_SE_files disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional rename_PE_files or rename_SE_files docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/ubuntu/ubuntu:jammy-20230816\" Optional rename_PE_files or rename_SE_files memory Int Amount of memory/RAM (in GB) to allocate to the task 2 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/standalone/rename_fastq/#outputs","title":"Outputs","text":"If a reverse read (read2) is provided, the files get renamed to the provided\u00a0new_filename\u00a0input with the notation\u00a0<new_filename>_R1.fastq.gz\u00a0and\u00a0<new_filename>_R2.fastq.gz. If only\u00a0read1\u00a0is provided, the file is renamed to\u00a0<new_filename>.fastq.gz.
This workflow is still in experimental research stages. Documentation is minimal as changes may occur in the code; it will be fleshed out when a stable state has been achieved.
"},{"location":"workflows/standalone/tbprofiler_tngs/#inputs","title":"Inputs","text":"Terra Task Name Variable Type Description Default Value Terra Status tbprofiler_tngs read1 File Illumina forward read file in FASTQ file format (compression optional) Required tbprofiler_tngs read2 File Illumina reverse read file in FASTQ file format (compression optional) Required tbprofiler_tngs samplename String Name of sample to be analyzed Required tbp_parser coverage_regions_bed File A file that contains the regions to perform coverage analysis on Optional tbp_parser coverage_threshold Int The minimum percentage of a region to exceed the minimum depth for a region to pass QC in tbp_parser 100 Optional tbp_parser cpu Int Number of CPUs to allocate to the task 1 Optional tbp_parser disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional tbp_parser docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/tbp-parser:2.1.1 Optional tbp_parser etha237_frequency Float Minimum frequency for a mutation in ethA at protein position 237 to pass QC in tbp-parser 0.1 Optional tbp_parser expert_rule_regions_bed File A file that contains the regions where R mutations and expert rules are applied Optional tbp_parser memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional tbp_parser min_depth Int Minimum depth for a variant to pass QC in tbp_parser 10 Optional tbp_parser min_frequency Float Minimum allele frequency for a variant to pass QC in tbp-parser 0.1 Optional tbp_parser min_read_support Int Minimum read support for a variant to pass QC in tbp-parser 10 Optional tbp_parser operator String Fills the \"operator\" field in the tbp_parser output files Optional tbp_parser rpob449_frequency Float Minimum frequency for a mutation at protein position 449 to pass QC in tbp-parser 0.1 Optional tbp_parser rrl_frequency Float Minimum frequency for a mutation in rrl to pass QC in tbp-parser 0.1 Optional tbp_parser rrl_read_support Int Minimum read support for a mutation in rrl to pass QC in tbp-parser 10 Optional tbp_parser rrs_frequency Float Minimum frequency for a mutation in rrs to pass QC in tbp-parser 0.1 Optional tbp_parser rrs_read_support Int Minimum read support for a mutation in rrs to pass QC in tbp-parser 10 Optional tbp_parser sequencing_method String Fills out the \"seq_method\" field in the tbp_parser output files Optional tbp_parser tbp_parser_debug Boolean Activate the debug mode on tbp_parser; increases logging outputs FALSE Optional tbprofiler cov_frac_threshold Int A cutoff used to calculate the fraction of the region covered by \u2264 this value 1 Optional tbprofiler cpu Int Number of CPUs to allocate to the task 8 Optional tbprofiler disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional tbprofiler mapper String The mapping tool used in TBProfiler to align the reads to the reference genome; see TBProfiler's original documentation for available options. bwa Optional tbprofiler memory Int Amount of memory/RAM (in GB) to allocate to the task 16 Optional tbprofiler min_af Float The minimum allele frequency to call a variant 0.1 Optional tbprofiler min_af_pred Float The minimum allele frequency to use a variant for resistance prediction 0.1 Optional tbprofiler min_depth Int The minimum depth for a variant to be called. 10 Optional tbprofiler ont_data Boolean Internal component; do not modify Do not modify, Optional tbprofiler tbprofiler_custom_db File TBProfiler uses by default the TBDB database; if you have a custom database you wish to use, you must provide a custom database in this field and set tbprofiler_run_custom_db to true Optional tbprofiler tbprofiler_docker_image String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/tbprofiler:4.4.2 Optional tbprofiler tbprofiler_run_custom_db Boolean FALSE Optional tbprofiler variant_caller String Select a different variant caller for TBProfiler to use by writing it in this block; see TBProfiler's original documentation for available options. freebayes Optional tbprofiler variant_calling_params String Enter additional variant calling parameters in this free text input to customize how the variant caller works in TBProfiler Optional tbprofiler bases_to_crop Int Indicate the number of bases to remove from the start and end of the read 30 Optional trimmomatic_pe cpu Int Number of CPUs to allocate to the task 4 Optional trimmomatic_pe disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional trimmomatic_pe docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/staphb/trimmomatic:0.39 Optional trimmomatic_pe memory Int Amount of memory/RAM (in GB) to allocate to the task 8 Optional trimmomatic_pe trimmomatic_args String Additional arguments to pass to trimmomatic. \"-phred33\" specifies the Phred Q score encoding which is almost always phred33 with modern sequence data. -phred33 Optional trimmomatic_pe trimmomatic_min_length Int Specifies minimum length of each read after trimming to be kept 75 Optional trimmomatic_pe trimmomatic_quality_trim_score Int The trimming quality score 30 Optional trimmomatic_pe trimmomatic_window_size Int The window size for trimming 4 Optional version_capture docker String The Docker container to use for the task \"us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0\" Optional version_capture timezone String Set the time zone to get an accurate date of analysis (uses UTC by default) Optional"},{"location":"workflows/standalone/tbprofiler_tngs/#terra-outputs","title":"Terra Outputs","text":"Variable Type Description tbp_parser_average_genome_depth Float The mean depth of coverage across all target regions included in the analysis tbp_parser_coverage_report File A file containing the breadth of coverage across each target loci tbp_parser_docker String The docker image and version tag for the tbp_parser tool tbp_parser_genome_percent_coverage Float The percent breadth of coverage across the entire genome tbp_parser_laboratorian_report_csv File An output file containing information regarding each mutation and its associated drug resistance profile in a CSV file. This file also contains two interpretation fields -- \"Looker\" and \"MDL\" which are generated using the CDC's expert rules for interpreting the severity of potential drug resistance mutations. tbp_parser_lims_report_csv File An output file formatted specifically for STAR LIMS. This CSV report summarizes the highest severity mutations for each antimicrobial and lists the relevant mutations for each gene. tbp_parser_looker_report_csv File An output file that contains condensed information suitable for generating a dashboard in Google's Looker studio. tbp_parser_version String The version number of tbp_parser tbprofiler_dr_type String The drug resistance category as determined by TBProfiler tbprofiler_main_lineage String The Mycobacterium tuberculosis lineage assignment as made by TBProfiler tbprofiler_median_coverage Int The median depth of coverage across the target loci tbprofiler_num_dr_variants String The total number of drug resistance conferring variants detected by TBProfiler tbprofiler_num_other_variants String The total number of non-drug resistance conferring variants detected by TBProfiler tbprofiler_output_alignment_bai File The index file associated with the binary alignment map of the input reads against the H37Rv genome tbprofiler_output_alignment_bam File The binary alignment map of the input reads against the H37Rv genome tbprofiler_pct_reads_mapped Float The percentage of reads that successfully mapped to the H37Rv genome tbprofiler_report_csv File The raw output file from TBProfiler tbprofiler_report_json File The json output file from TBProfiler tbprofiler_report_tsv File The TSV output file from TBProfiler tbprofiler_resistance_genes String The genes in which a mutation was detected that may be resistance conferring tbprofiler_sub_lineage String The Mycobacterium tuberculosis sub-lineage assignment as made by TBProfiler tbprofiler_tngs_wf_analysis_date String The date on which the workflow was run tbprofiler_tngs_wf_version String The version of the tbprofiler_tngs workflow used for this analysis tbprofiler_version String The version of TBProfiler used for this analysis trimmomatic_docker String The docker image used for the trimmomatic module in this workflow trimmomatic_read1_trimmed File The read1 file post trimming trimmomatic_read2_trimmed File The read2 file post trimming trimmomatic_stats File The read trimming statistics trimmomatic_version String The version of trimmomatic used in this analysis"},{"location":"workflows/standalone/theiavalidate/","title":"TheiaValidate","text":""},{"location":"workflows/standalone/theiavalidate/#quick-facts","title":"Quick Facts","text":"Workflow Type Applicable Kingdom Last Known Changes Command-line Compatibility Workflow Level Standalone Any Taxa PHB v2.0.0 No"},{"location":"workflows/standalone/theiavalidate/#theiavalidate_phb","title":"TheiaValidate_PHB","text":"TheiaValidate Workflow Diagram
TheiaValidate performs basic comparisons between user-designated columns in two separate tables. We anticipate this workflow being run to determine if any differences exist between version releases or two workflows, such as TheiaProk_ONT vs TheiaProk_Illumina_PE. A summary PDF report is produced in addition to a Excel spreadsheet that lists the values for any columns that do not have matching content for a sample.
Warning
The two tables being compared must have both identical sample names and an equal number of samples. If not, validation will not work or (in the case of unequal number of samples) not be attempted.
In order to enable this workflow to function for different workflow series, we require users to provide a list of columns they want to compare between the two tables. Feel free to use the information below that Theiagen uses to compare versions of the three main workflow series as a starting point for your own validations:
Validation Starting Points
Workflow Series Validation Criteria TSV Columns to Compare TheiaCoV Workflows TheiaCov Validation Criteria abricate_flu_subtype,abricate_flu_type,assembly_length_unambiguous,assembly_mean_coverage,irma_subtype,irma_type,kraken_human,kraken_human_dehosted,kraken_sc2,kraken_sc2_dehosted,kraken_target_org,kraken_target_org_dehosted,nextclade_aa_dels,nextclade_aa_subs,nextclade_clade,nextclade_lineage,nextclade_tamiflu_resistance_aa_subs,num_reads_clean1,num_reads_clean2,number_N,pango_lineage,percent_reference_coverage,vadr_num_alerts TheiaEuk Workflows TheiaEuk Validation Criteria assembly_length,busco_results,clade_type,est_coverage_clean,est_coverage_raw,gambit_predicted_taxon,n50_value,num_reads_clean1,num_reads_clean2,number_contigs,quast_gc_percent,theiaeuk_snippy_variants_hits TheiaProk Workflows TheiaProk Validation Criteria abricate_abaum_plasmid_type_genes,agrvate_agr_group,amrfinderplus_amr_core_genes,amrfinderplus_amr_plus_genes,amrfinderplus_stress_genes,amrfinderplus_virulence_genes,ani_highest_percent,ani_top_species_match,assembly_length,busco_results,ectyper_predicted_serotype,emmtypingtool_emm_type,est_coverage_clean,est_coverage_raw,gambit_predicted_taxon,genotyphi_final_genotype,hicap_genes,hicap_serotype,kaptive_k_type,kleborate_genomic_resistance_mutations,kleborate_key_resistance_genes,kleborate_mlst_sequence_type,legsta_predicted_sbt,lissero_serotype,meningotype_serogroup,midas_primary_genus,midas_secondary_genus,midas_secondary_genus_abundance,n50_value,ngmaster_ngmast_sequence_type,ngmaster_ngstar_sequence_type,num_reads_clean1,num_reads_clean2,number_contigs,pasty_serogroup,pbptyper_predicted_1A_2B_2X,plasmidfinder_plasmids,poppunk_gps_cluster,seqsero2_predicted_serotype,seroba_ariba_serotype,seroba_serotype,serotypefinder_serotype,shigatyper_ipaB_presence_absence,shigatyper_predicted_serotype,shigeifinder_cluster,shigeifinder_serotype,sistr_predicted_serotype,sonneityping_final_genotype,spatyper_type,srst2_vibrio_serogroup,staphopiasccmec_types_and_mecA_presence,tbprofiler_main_lineage,tbprofiler_resistance_genes,ts_mlst_predicted_st,virulencefinder_hitsIf additional validation metrics are desired, the user has the ability to provide a validation_criteria_tsv file that specifies what type of comparison should be performed. There are several options for additional validation checks:
amrfinder_plus_genes which is a comma-delimited list of genes) for identical content \u2014 order does not matter; that is, mdsA,mdsB is determined to be same as mdsB,mdsA. The EXACT match does not consider these to be the same, but the SET match does.If a column consists of only GCP URIs (Google Cloud file paths), the files will be localized and compared with either an EXACT match or a SET match. In the SET match, the lines in the file are ordered before comparison. Results are returned to the summary table as expected. The results of each file comparison can be found in the theiavalidate_diffs output column.
Please note that all string inputs must be enclosed in quotation marks; for example, \"column1,column2\" or \"workspace1\"
Terra Task Name Variable Type Description Default Value Terra Status theiavalidate columns_to_compare String A comma-separated list of the columns the user wants to compare. Do not include whitespace. Required theiavalidate output_prefix String The prefix for the output files Required theiavalidate table1_name String The name of the first table Required theiavalidate table2_name String The name of the second table Required theiavalidate terra_project1_name String The name of the Terra project where table1_name can be found Required theiavalidate terra_workspace1_name String The name of the Terra workspace where table1_name can be found Required theiavalidate column_translation_tsv File If the user wants to link two columns of different names, they may supply a TSV file that provides a \"column translation\" between the two files (see the section below this table). Optional theiavalidate terra_project2_name String If the table2_name is located in a different Terra project, indicate it here. Otherwise, the workflow will look for table2_name in the Terra project indicated in terra_project1_name. value forterra_project1_name Optional theiavalidate terra_workspace2_name String If the table2_name is located in a different Terra workspace, indicate it here. Otherwise, the workflow will look for table2_name in the Terra workspace indicated in terra_workspace1_name. value for terra_workspace1_name Optional theiavalidate validation_criteria_tsv File If the user wants to specify a different comparison than the default exact string match, they may supply a TSV file that indicates the different options (see the section below this table). Optional compare_two_tsvs cpu Int Number of CPUs to allocate to the task 2 Optional compare_two_tsvs debug_output Boolean Set to true to enable more outputs; useful when debugging FALSE Optional compare_two_tsvs disk_size Int Amount of storage (in GB) to allocate to the task 100 Optional compare_two_tsvs docker String The Docker container to use for the task us-docker.pkg.dev/general-theiagen/theiagen/theiavalidate:0.1.0 Optional compare_two_tsvs memory Int Amount of memory/RAM (in GB) to allocate to the task 4 Optional compare_two_tsvs na_values String If the user knows a particular value in either table that they would like to be considered N/A, they can indicate those values in a comma-separated list here. Any changes here will overwrite the default and not append to the default list. Do not include whitespace. -1.#IND,1.#QNAN,1.#IND,-1.#QNAN,#N/A,N/A,n/a,,#NA,NULL,null,NaN,-NaN,nan,-nan,None Optional export_two_tsvs cpu Int Number of CPUs to allocate to the task 1 Optional export_two_tsvs disk_size Int Amount of storage (in GB) to allocate to the task 10 Optional The optional validation_criteria_tsv file takes the following format (tab-delimited; a header line is required):
column_name criteria\ncolumnB SET\ncolumnC IGNORE\ncolumnD 0.01\ncolumnE EXACT\nPlease see above for a description of all available criteria options (EXACT, IGNORE, SET, ).
The optional column_translation_tsv file takes the following format (tab-delimited; there can be no header line):
column_name_in_table1 column_name_in_table2\ncolumn_name_in_table2 column_name_in_table1\ninternal_column_name display_column_name\nPlease note that the name in the second column will be displayed and used in all output files.
Known Bug
There must be more than one line in the column_translation_tsv file or else this error will appear: AttributeError: 'str' object has no attribute 'to_dict'. To fix this error, add an additional line in the column_translation_tsv file, like the following: columnA columnA
Known Bug
If performing a comparison, all samples must have values for that column.
Call Caching Disabled
If using TheiaValidate workflow version 1.3.0 or higher, the call-caching feature of Terra has been DISABLED to ensure that the workflow is run from the beginning and data is compared fresh. Call-caching will not be enabled, even if the user checks the box \u2705 in the Terra workflow interface.
"},{"location":"workflows/standalone/theiavalidate/#outputs","title":"Outputs","text":"Variable Type Description theiavalidate_criteria_differences File A TSV file that lists only the differences that fail to meet the validation criteria theiavalidate_date String The date the analysis was run theiavalidate_diffs Array[File] An array of files with a single file for each file comparison performed; only has values if a column with files is compared theiavalidate_exact_differences File A TSV file that lists all exact string match differences between samples theiavalidate_filtered_input_table1 File The first data table used for validation after removing unexamined columns and translating column names theiavalidate_filtered_input_table2 File The second data table used for validation after removing unexamined columns and translating column names theiavalidate_report File A PDF summary report theiavalidate_status String Indicates whether or not validation was attempted theiavalidate_version String The version of the TheiaValidate Python Docker theiavalidate_wf_version String The version of the PHB repository"},{"location":"workflows/standalone/theiavalidate/#example-data-and-outputs","title":"Example Data and Outputs","text":"To help demonstrate how TheiaValidate works, please observe the following example and outputs:
Table1 entity:example_table1_id columnA-string columnB-set columnC-ignore columnD-float columnE-missing sample1 option1 item1,item2,item3 cheese 1000 present sample2 option1 item1,item3,item2 cheesecake 12 present sample3 option2 item1,item2,item3 cake 14 present sample4 option1 item2,item1 cakebatter 3492 sample5 option2 item1,item2 batter 3 present Table2 entity:example_table2_id columnA-string columnB-set columnC-ignore columnD-float missing sample1 option1 item1,item3,item2 cheesecake 999 present sample2 option2 item1,item2,item3 batter 12 present sample3 option1 item1,item2 cheese 24 sample4 option1 item1,item2 cakebatter 728 sample5 option2 item1,item2,item3 batter 4 present Validation Criteria column criteria columnB-set SET columnC-ignore IGNORE columnD-float 0.01 columnE-missing EXACT Column Translation missing columnE-missing columnA-string columnA-stringNote: the second row translating columnA-string to itself is included to prevent the known bug explained above.
If the above inputs are provided, then the following output files will be generated:
filtered_example_table1.tsv
filtered_example_table2.tsv
example_summary.pdf
example_exact_differences.tsv
example_validation_criteria_differences.tsv
"},{"location":"workflows_overview/workflows_alphabetically/","title":"Alphabetical Workflows","text":"Sort by Workflow Type | Sort by Kingdom
Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Assembly_Fetch Download assemblies from NCBI, after optionally identifying the closest RefSeq reference genome to your own draft assembly Any taxa Sample-level Yes v1.3.0 Assembly_Fetch_PHB Augur Phylogenetic analysis for viral pathogens Viral Sample-level, Set-level Yes v2.3.0 Augur_Prep_PHB, Augur_PHB BaseSpace_Fetch Import data from BaseSpace into Terra Any taxa Sample-level Yes v2.0.0 BaseSpace_Fetch_PHB Cauris_CladeTyper C. auris clade assignment Mycotics Sample-level Yes v1.0.0 Cauris_CladeTyper_PHB Concatenate_Column_Content Concatenate contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Concatenate_Column_Content_PHB Concatenate_Illumina_Lanes Concatenate Illumina lanes for a single sample Any taxa Sample-level Yes v2.3.0 Concatenate_Illumina_Lanes_PHB Core_Gene_SNP Pangenome analysis Bacteria Set-level Some optional features incompatible, Yes v2.1.0 Core_Gene_SNP_PHB Create_Terra_Table Upload data to Terra and then run this workflow to have the table automatically created Any taxa Yes v2.2.0 Create_Terra_Table_PHB CZGenEpi_Prep Prepare metadata and fasta files for easy upload to the CZ GEN EPI platform. Monkeypox virus, SARS-CoV-2, Viral Set-level No v1.3.0 CZGenEpi_Prep_PHB Fetch_SRR_Accession Provided a BioSample accession, identify any associated SRR accession(s) Any taxa Sample-level Yes v2.3.0 *Fetch_SRR_Accession_PHB Find_Shared_Variants Combines and reshapes variant data from Snippy_Variants to illustrate variants shared across multiple samples Bacteria, Mycotics Set-level Yes v2.0.0 Find_Shared_Variants_PHB Freyja Workflow Series Recovers relative lineage abundances from mixed sample data and generates visualizations SARS-CoV-2, Viral Sample-level, Set-level Yes v2.2.0 Freyja_FASTQ_PHB, Freyja_Plot_PHB, Freyja_Dashboard_PHB, Freyja_Update_PHB GAMBIT_Query Taxon identification of genome assembly using GAMBIT Bacteria, Mycotics Sample-level Yes v2.0.0 Gambit_Query_PHB Kraken2 Taxa identification from reads Any taxa Sample-level Yes v2.3.0 Kraken2_PE_PHB, Kraken2_SE_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB Lyve_SET Alignment of reads to a reference genome, SNP calling, curation of high quality SNPs, phylogenetic analysis Bacteria Set-level Yes v2.1.0 Lyve_SET_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB Mercury_Prep_N_Batch Prepare metadata and sequence data for submission to NCBI and GISAID Influenza, Monkeypox virus, SARS-CoV-2, Viral Set-level No v2.3.0 Mercury_Prep_N_Batch_PHB NCBI-AMRFinderPlus Runs NCBI's AMRFinderPlus on genome assemblies (bacterial and fungal) Bacteria, Mycotics Sample-level Yes v2.0.0 NCBI-AMRFinderPlus_PHB NCBI_Scrub Runs NCBI's HRRT on Illumina FASTQs Any taxa Sample-level Yes v2.2.1 NCBI_Scrub_PE_PHB, NCBI_Scrub_SE_PHB Pangolin_Update Update Pangolin assignments SARS-CoV-2, Viral Sample-level Yes v2.0.0 Pangolin_Update_PHB RASUSA Randomly subsample sequencing reads to a specified coverage Any taxa Sample-level Yes v2.0.0 RASUSA_PHB Rename_FASTQ Rename paired-end or single-end read files in a Terra data table in a non-destructive way Any taxa Sample-level Yes v2.1.0 Rename_FASTQ_PHB Samples_to_Ref_Tree Use Nextclade to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Samples_to_Ref_Tree_PHB Snippy_Streamline Implementation of Snippy workflows for phylogenetic analysis from reads, with optional dynamic reference selection Bacteria Set-level Yes v2.3.0 Snippy_Streamline_PHB Snippy_Streamline_FASTA Implementation of Snippy workflows for phylogenetic analysis from assembled genomes (in FASTA format), with optional dynamic reference selection Bacteria Set-level Yes v2.3.0 Snippy_Streamline_FASTA_PHB Snippy_Tree SNP-based phylogenetic analysis from reads, with option to mask recombination Bacteria Set-level Some optional features incompatible, Yes v2.3.0 Snippy_Tree_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.3.0 Snippy_Variants_PHB SRA_Fetch Import publicly available reads from SRA using SRR#, ERR# or DRR# Any taxa Sample-level Yes v2.2.0 SRA_Fetch_PHB TBProfiler_tNGS Performs in silico antimicrobial susceptibility testing on Mycobacterium tuberculosis targeted-NGS samples with TBProfiler and tbp-parser Bacteria, TB Sample-level Yes v2.3.0 TBProfiler_tNGS_PHB Terra_2_GISAID Upload of assembly data to GISAID SARS-CoV-2, Viral Set-level Yes v1.2.1 Terra_2_GISAID_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.3.0 Terra_2_NCBI_PHB TheiaCov Workflow Series Viral genome assembly, QC and characterization from amplicon sequencing HIV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level, Set-level Some optional features incompatible, Yes v2.3.0 TheiaCoV_Illumina_PE_PHB, TheiaCoV_Illumina_SE_PHB, TheiaCoV_ONT_PHB, TheiaCoV_ClearLabs_PHB, TheiaCoV_FASTA_PHB, TheiaCoV_FASTA_Batch_PHB TheiaEuk Mycotic genome assembly, QC and characterization from WGS data Mycotics Sample-level Some optional features incompatible, Yes v2.3.0 TheiaEuk_Illumina_PE_PHB TheiaMeta Genome assembly and QC from metagenomic sequencing Any taxa Sample-level Yes v2.3.0 TheiaMeta_Illumina_PE_PHB TheiaProk Workflow Series Bacterial genome assembly, QC and characterization from WGS data Bacteria Sample-level Some optional features incompatible, Yes v2.3.0 TheiaProk_Illumina_PE_PHB, TheiaProk_Illumina_SE_PHB, TheiaProk_ONT_PHB, TheiaProk_FASTA_PHB TheiaValidate This workflow performs basic comparisons between user-designated columns in two separate tables. Any taxa No v2.0.0 TheiaValidate_PHB Transfer_Column_Content Transfer contents of a specified Terra data table column for many samples (\"entities\") to a GCP storage bucket location Any taxa Set-level Yes v1.3.0 Transfer_Column_Content_PHB Samples_to_Ref_Tree Use UShER to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Usher_PHB Usher_PHB Update VADR assignments HAV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level Yes v2.1.0 VADR_Update_PHB VADR_Update Update VADR assignments HAV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level Yes v2.2.0 VADR_Update_PHB Zip_Column_Content Zip contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Zip_Column_Content_PHBCommand-line compatibility is determined if the workflow can be run on a local command-line environment, providing all dependencies are installed, with either miniwdl or cromwell.\u00a0\u21a9
Sort by Type | Sort Alphabetically
"},{"location":"workflows_overview/workflows_kingdom/#any-taxa","title":"Any Taxa","text":"Name Description Taxa Workflow Level Command-line Compatible1 Last known changes Dockstore Assembly_Fetch Download assemblies from NCBI, after optionally identifying the closest RefSeq reference genome to your own draft assembly Any taxa Sample-level Yes v1.3.0 Assembly_Fetch_PHB BaseSpace_Fetch Import data from BaseSpace into Terra Any taxa Sample-level Yes v2.0.0 BaseSpace_Fetch_PHB Concatenate_Column_Content Concatenate contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Concatenate_Column_Content_PHB Concatenate_Illumina_Lanes Concatenate Illumina lanes for a single sample Any taxa Sample-level Yes v2.3.0 Concatenate_Illumina_Lanes_PHB Create_Terra_Table Upload data to Terra and then run this workflow to have the table automatically created Any taxa Yes v2.2.0 Create_Terra_Table_PHB Fetch_SRR_Accession Provided a BioSample accession, identify any associated SRR accession(s) Any taxa Sample-level Yes v2.3.0 Fetch_SRR_Accession_PHB Kraken2 Taxa identification from reads Any taxa Sample-level Yes v2.3.0 Kraken2_PE_PHB, Kraken2_SE_PHB NCBI_Scrub Runs NCBI's HRRT on Illumina FASTQs Any taxa Sample-level Yes v2.2.1 NCBI_Scrub_PE_PHB, NCBI_Scrub_SE_PHB RASUSA Randomly subsample sequencing reads to a specified coverage Any taxa Sample-level Yes v2.0.0 RASUSA_PHB Rename_FASTQ Rename paired-end or single-end read files in a Terra data table in a non-destructive way Any taxa Sample-level Yes v2.1.0 Rename_FASTQ_PHB SRA_Fetch Import publicly available reads from SRA using SRR#, ERR# or DRR# Any taxa Sample-level Yes v2.2.0 SRA_Fetch_PHB TheiaMeta Genome assembly and QC from metagenomic sequencing Any taxa Sample-level Yes v2.3.0 TheiaMeta_Illumina_PE_PHB TheiaValidate This workflow performs basic comparisons between user-designated columns in two separate tables. Any taxa No v2.0.0 TheiaValidate_PHB Transfer_Column_Content Transfer contents of a specified Terra data table column for many samples (\"entities\") to a GCP storage bucket location Any taxa Set-level Yes v1.3.0 Transfer_Column_Content_PHB Zip_Column_Content Zip contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Zip_Column_Content_PHB"},{"location":"workflows_overview/workflows_kingdom/#bacteria","title":"Bacteria","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Core_Gene_SNP Pangenome analysis Bacteria Set-level Some optional features incompatible, Yes v2.1.0 Core_Gene_SNP_PHB Find_Shared_Variants Combines and reshapes variant data from Snippy_Variants to illustrate variants shared across multiple samples Bacteria, Mycotics Set-level Yes v2.0.0 Find_Shared_Variants_PHB GAMBIT_Query Taxon identification of genome assembly using GAMBIT Bacteria, Mycotics Sample-level Yes v2.0.0 Gambit_Query_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB Lyve_SET Alignment of reads to a reference genome, SNP calling, curation of high quality SNPs, phylogenetic analysis Bacteria Set-level Yes v2.1.0 Lyve_SET_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB NCBI-AMRFinderPlus Runs NCBI's AMRFinderPlus on genome assemblies (bacterial and fungal) Bacteria, Mycotics Sample-level Yes v2.0.0 NCBI-AMRFinderPlus_PHB Snippy_Streamline Implementation of Snippy workflows for phylogenetic analysis from reads, with optional dynamic reference selection Bacteria Set-level Yes v2.3.0 Snippy_Streamline_PHB Snippy_Streamline_FASTA Implementation of Snippy workflows for phylogenetic analysis from assembled genomes (in FASTA format), with optional dynamic reference selection Bacteria Set-level Yes v2.3.0 Snippy_Streamline_FASTA_PHB Snippy_Tree SNP-based phylogenetic analysis from reads, with option to mask recombination Bacteria Set-level Some optional features incompatible, Yes v2.3.0 Snippy_Tree_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.3.0 Snippy_Variants_PHB TBProfiler_tNGS Performs in silico antimicrobial susceptibility testing on Mycobacterium tuberculosis targeted-NGS samples with TBProfiler and tbp-parser Bacteria, TB Sample-level Yes v2.3.0 TBProfiler_tNGS_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.1.0 Terra_2_NCBI_PHB TheiaProk Workflow Series Bacterial genome assembly, QC and characterization from WGS data Bacteria Sample-level Some optional features incompatible, Yes v2.3.0 TheiaProk_Illumina_PE_PHB, TheiaProk_Illumina_SE_PHB, TheiaProk_ONT_PHB, TheiaProk_FASTA_PHB"},{"location":"workflows_overview/workflows_kingdom/#mycotics","title":"Mycotics","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Cauris_CladeTyper C. auris clade assignment Mycotics Sample-level Yes v1.0.0 Cauris_CladeTyper_PHB Find_Shared_Variants Combines and reshapes variant data from Snippy_Variants to illustrate variants shared across multiple samples Bacteria, Mycotics Set-level Yes v2.0.0 Find_Shared_Variants_PHB GAMBIT_Query Taxon identification of genome assembly using GAMBIT Bacteria, Mycotics Sample-level Yes v2.0.0 Gambit_Query_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB NCBI-AMRFinderPlus Runs NCBI's AMRFinderPlus on genome assemblies (bacterial and fungal) Bacteria, Mycotics Sample-level Yes v2.0.0 NCBI-AMRFinderPlus_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.3.0 Snippy_Variants_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.3.0 Terra_2_NCBI_PHB TheiaEuk Mycotic genome assembly, QC and characterization from WGS data Mycotics Sample-level Some optional features incompatible, Yes v2.3.0 TheiaEuk_Illumina_PE_PHB"},{"location":"workflows_overview/workflows_kingdom/#viral","title":"Viral","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Augur Phylogenetic analysis for viral pathogens Viral Sample-level, Set-level Yes v2.3.0 Augur_Prep_PHB, Augur_PHB CZGenEpi_Prep Prepare metadata and fasta files for easy upload to the CZ GEN EPI platform. Monkeypox virus, SARS-CoV-2, Viral Set-level No v1.3.0 CZGenEpi_Prep_PHB Freyja Workflow Series Recovers relative lineage abundances from mixed sample data and generates visualizations SARS-CoV-2, Viral Sample-level, Set-level Yes v2.3.0 Freyja_FASTQ_PHB, Freyja_Plot_PHB, Freyja_Dashboard_PHB, Freyja_Update_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB Mercury_Prep_N_Batch Prepare metadata and sequence data for submission to NCBI and GISAID Influenza, Monkeypox virus, SARS-CoV-2, Viral Set-level No v2.3.0 Mercury_Prep_N_Batch_PHB Pangolin_Update Update Pangolin assignments SARS-CoV-2, Viral Sample-level Yes v2.0.0 Pangolin_Update_PHB Samples_to_Ref_Tree Use Nextclade to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Samples_to_Ref_Tree_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.3.0 Snippy_Variants_PHB Terra_2_GISAID Upload of assembly data to GISAID SARS-CoV-2, Viral Set-level Yes v1.2.1 Terra_2_GISAID_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.3.0 Terra_2_NCBI_PHB TheiaCov Workflow Series Viral genome assembly, QC and characterization from amplicon sequencing HIV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level, Set-level Some optional features incompatible, Yes v2.3.0 TheiaCoV_Illumina_PE_PHB, TheiaCoV_Illumina_SE_PHB, TheiaCoV_ONT_PHB, TheiaCoV_ClearLabs_PHB, TheiaCoV_FASTA_PHB, TheiaCoV_FASTA_Batch_PHB Usher_PHB Use UShER to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Usher_PHB VADR_Update Update VADR assignments HAV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level Yes v2.2.0 VADR_Update_PHBCommand-line compatibility is determined if the workflow can be run on a local command-line environment, providing all dependencies are installed, with either miniwdl or cromwell.\u00a0\u21a9\u21a9\u21a9\u21a9
Sort by Kingdom | Sort Alphabetically
"},{"location":"workflows_overview/workflows_type/#data-import","title":"Data Import","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Assembly_Fetch Download assemblies from NCBI, after optionally identifying the closest RefSeq reference genome to your own draft assembly Any taxa Sample-level Yes v1.3.0 Assembly_Fetch_PHB BaseSpace_Fetch Import data from BaseSpace into Terra Any taxa Sample-level Yes v2.0.0 BaseSpace_Fetch_PHB Create_Terra_Table Upload data to Terra and then run this workflow to have the table automatically created Any taxa Yes v2.2.0 Create_Terra_Table_PHB Fetch_SRR_Accession Provided a BioSample accession, identify any associated SRR accession(s) Any taxa Sample-level Yes v2.3.0 Fetch_SRR_Accession_PHB SRA_Fetch Import publicly available reads from SRA using SRR#, ERR# or DRR# Any taxa Sample-level Yes v2.2.0 SRA_Fetch_PHB"},{"location":"workflows_overview/workflows_type/#genomic-characterization","title":"Genomic Characterization","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Freyja Workflow Series Recovers relative lineage abundances from mixed sample data and generates visualizations SARS-CoV-2, Viral Sample-level, Set-level Yes v2.3.0 Freyja_FASTQ_PHB, Freyja_Plot_PHB, Freyja_Dashboard_PHB, Freyja_Update_PHB Pangolin_Update Update Pangolin assignments SARS-CoV-2, Viral Sample-level Yes v2.0.0 Pangolin_Update_PHB TheiaCov Workflow Series Viral genome assembly, QC and characterization from amplicon sequencing HIV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level, Set-level Some optional features incompatible, Yes v2.3.0 TheiaCoV_Illumina_PE_PHB, TheiaCoV_Illumina_SE_PHB, TheiaCoV_ONT_PHB, TheiaCoV_ClearLabs_PHB, TheiaCoV_FASTA_PHB, TheiaCoV_FASTA_Batch_PHB TheiaEuk Mycotic genome assembly, QC and characterization from WGS data Mycotics Sample-level Some optional features incompatible, Yes v2.3.0 TheiaEuk_Illumina_PE_PHB TheiaMeta Genome assembly and QC from metagenomic sequencing Any taxa Sample-level Yes v2.3.0 TheiaMeta_Illumina_PE_PHB TheiaProk Workflow Series Bacterial genome assembly, QC and characterization from WGS data Bacteria Sample-level Some optional features incompatible, Yes v2.3.0 TheiaProk_Illumina_PE_PHB, TheiaProk_Illumina_SE_PHB, TheiaProk_ONT_PHB, TheiaProk_FASTA_PHB VADR_Update Update VADR assignments HAV, Influenza, Monkeypox virus, RSV-A, RSV-B, SARS-CoV-2, Viral, WNV Sample-level Yes v2.2.1 VADR_Update_PHB"},{"location":"workflows_overview/workflows_type/#phylogenetic-construction","title":"Phylogenetic Construction","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Augur Phylogenetic analysis for viral pathogens Viral Sample-level, Set-level Yes v2.3.0 Augur_Prep_PHB, Augur_PHB Core_Gene_SNP Pangenome analysis Bacteria Set-level Some optional features incompatible, Yes v2.1.0 Core_Gene_SNP_PHB CZGenEpi_Prep Prepare metadata and fasta files for easy upload to the CZ GEN EPI platform. Monkeypox virus, SARS-CoV-2, Viral Set-level No v1.3.0 CZGenEpi_Prep_PHB Find_Shared_Variants Combines and reshapes variant data from Snippy_Variants to illustrate variants shared across multiple samples Bacteria, Mycotics Set-level Yes v2.0.0 Find_Shared_Variants_PHB kSNP3 SNP-based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 kSNP3_PHB Lyve_SET Alignment of reads to a reference genome, SNP calling, curation of high quality SNPs, phylogenetic analysis Bacteria Set-level Yes v2.1.0 Lyve_SET_PHB MashTree_FASTA Mash-distance based phylogenetic analysis from assemblies Bacteria, Mycotics, Viral Set-level Some optional features incompatible, Yes v2.1.0 MashTree_FASTA_PHB Snippy_Streamline Implementation of Snippy workflows for phylogenetic analysis from reads, with optional dynamic reference selection Bacteria Set-level Yes v2.3.0 Snippy_Streamline_PHB Snippy_Streamline_FASTA Implementation of Snippy workflows for phylogenetic analysis from assembled genomes (in FASTA format), with optional dynamic reference selection Bacteria Set-level Yes v2.3.0 Snippy_Streamline_FASTA_PHB Snippy_Tree SNP-based phylogenetic analysis from reads, with option to mask recombination Bacteria Set-level Some optional features incompatible, Yes v2.3.0 Snippy_Tree_PHB Snippy_Variants Alignment of reads to a reference genome, then SNP calling Bacteria, Mycotics, Viral Sample-level Yes v2.3.0 Snippy_Variants_PHB"},{"location":"workflows_overview/workflows_type/#phylogenetic-placement","title":"Phylogenetic Placement","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Samples_to_Ref_Tree Use Nextclade to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Samples_to_Ref_Tree_PHB Usher_PHB Use UShER to rapidly and accurately place your samples on any existing phylogenetic tree Monkeypox virus, SARS-CoV-2, Viral Sample-level, Set-level Yes v2.1.0 Usher_PHB"},{"location":"workflows_overview/workflows_type/#public-data-sharing","title":"Public Data Sharing","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Mercury_Prep_N_Batch Prepare metadata and sequence data for submission to NCBI and GISAID Influenza, Monkeypox virus, SARS-CoV-2, Viral Set-level No v2.3.0 Mercury_Prep_N_Batch_PHB Terra_2_GISAID Upload of assembly data to GISAID SARS-CoV-2, Viral Set-level Yes v1.2.1 Terra_2_GISAID_PHB Terra_2_NCBI Upload of sequence data to NCBI Bacteria, Mycotics, Viral Set-level No v2.1.0 Terra_2_NCBI_PHB"},{"location":"workflows_overview/workflows_type/#exporting-data-from-terra","title":"Exporting Data from Terra","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Concatenate_Column_Content Concatenate contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Concatenate_Column_Content_PHB Transfer_Column_Content Transfer contents of a specified Terra data table column for many samples (\"entities\") to a GCP storage bucket location Any taxa Set-level Yes v1.3.0 Transfer_Column_Content_PHB Zip_Column_Content Zip contents of a specified Terra data table column for many samples (\"entities\") Any taxa Set-level Yes v2.1.0 Zip_Column_Content_PHB"},{"location":"workflows_overview/workflows_type/#standalone","title":"Standalone","text":"Name Description Applicable Kingdom Workflow Level Command-line Compatibility1 Last Known Changes Dockstore Cauris_CladeTyper C. auris clade assignment Mycotics Sample-level Yes v1.0.0 Cauris_CladeTyper_PHB Concatenate_Illumina_Lanes Concatenate Illumina lanes for a single sample Any taxa Sample-level Yes v2.3.0 Concatenate_Illumina_Lanes_PHB GAMBIT_Query Taxon identification of genome assembly using GAMBIT Bacteria, Mycotics Sample-level Yes v2.0.0 Gambit_Query_PHB Kraken2 Taxa identification from reads Any taxa Sample-level Yes v2.3.0 Kraken2_PE_PHB, Kraken2_SE_PHB NCBI-AMRFinderPlus Runs NCBI's AMRFinderPlus on genome assemblies (bacterial and fungal) Bacteria, Mycotics Sample-level Yes v2.0.0 NCBI-AMRFinderPlus_PHB NCBI_Scrub Runs NCBI's HRRT on Illumina FASTQs Any taxa Sample-level Yes v2.2.1 NCBI_Scrub_PE_PHB, NCBI_Scrub_SE_PHB RASUSA Randomly subsample sequencing reads to a specified coverage Any taxa Sample-level Yes v2.0.0 RASUSA_PHB Rename_FASTQ Rename paired-end or single-end read files in a Terra data table in a non-destructive way Any taxa Sample-level Yes v2.1.0 Rename_FASTQ_PHB TBProfiler_tNGS Performs in silico antimicrobial susceptibility testing on Mycobacterium tuberculosis targeted-NGS samples with TBProfiler and tbp-parser Bacteria, TB Sample-level Yes v2.3.0 TBProfiler_tNGS_PHB TheiaValidate This workflow performs basic comparisons between user-designated columns in two separate tables. Any taxa No v2.0.0 TheiaValidate_PHBCommand-line compatibility is determined if the workflow can be run on a local command-line environment, providing all dependencies are installed, with either miniwdl or cromwell.\u00a0\u21a9\u21a9\u21a9\u21a9\u21a9\u21a9\u21a9
concatenate_illumina_lanes: Concatenate Multi-Lane Illumina FASTQs for Illumina onlyThe concatenate_illumina_lanes task concatenates Illumina FASTQ files from multiple lanes into a single file. This task only runs if the read1_lane2 input file has been provided. All read1 lanes are concatenated together and are used in subsequent tasks, as are the read2 lanes. These concatenated files are also provided as output.
Concatenate Illumina Lanes Technical Details
+The concatenate_illumina_lanes task is run before any downstream steps take place.
| + | Links | +
|---|---|
| Task | +wf_concatenate_illumina_lanes.wdl | +
screen: Total Raw Read Quantification and Genome Size EstimationThe screen task ensures the quantity of sequence data is sufficient to undertake genomic analysis. It uses fastq-scan and bash commands for quantification of reads and base pairs, and mash sketching to estimate the genome size and its coverage. At each step, the results are assessed relative to pass/fail criteria and thresholds that may be defined by optional user inputs. Samples that do not meet these criteria will not be processed further by the workflow: