Flumina

A pipeline for processing and calling high-frequency and low-frequency variants from Illumina sequence data for Influenza viruses

The pipeline accomplishes the following:

1. Organize raw read data
2. Remove adaptor contamination and trims low quality reads and bases
3. Assemble cleaned reads into consensus contigs
4. Calls high-frequency variants using GATK4
5. Calls low-freuqency variants using LoFreq
6. Summarizes variant calling data
7. Link variants to curated amino acids of interest

Flumina uses SnakeMake for program execution and cluster job submission management. With multi-job cluster management, analyzing many samples in bulk can be accomplished quite rapidly. The speed will increase as more threads are available, as individual pieces of the pipeline can be run in tandem.

Quick installation instructions

First, clone this repository to your computer to obtain the setup files and run script. Or alternatively go to the green "Code" button in top right of this repository and select "download ZIP".

git clone https://github.com/flu-crew/Flumina.git

Second, change your working directory in the terminal to the downloaded repository. The key file here is the "environment.yaml" anaconda environment file, which must be present in the working directory being used.

cd /PATH/TO/Flumina

The outside programs can be installed manually or more easily through the anaconda environment file provided (version numbers are provided in environment file for reporting and exact replication). To install with the environment file, the easiest and quickest way is to first install the Anaconda package manager. Anaconda can be downloaded and installed for different operating systems from https://anaconda.org. Miniconda is recommended as it has a smaller footprint (smaller size and fewer files). Once installed, you can create a new environment for Flumina by:

conda env create -f environment.yaml -n Flumina

To use the environment, it must first be activated in your current terminal session or placed in your cluster job script.

conda activate Flumina

If the environment is to be installed in a specific location, like a project directory on a cluster:

conda env create -f environment.yaml -p /PATH/TO/Flumina

conda activate /PATH/TO/Flumina

You should only need to activate the environment if running locally. On a cluster, the conda activate line should be placed in your job script.

Using Flumina

Create renaming file

Often the case with multiplexed samples in sequence capture projects, you will find that the names of the reads often are not the desired final names for the sample. PhyloProcessR offers a function to rename and organize all your samples given a spreadsheet of the file name and desired sample name. To create the renaming file, a .csv file is needed with only two columns: "File" and "Sample". An example is included in the setup-configuration_files folder in the main branch ("file_rename.csv").

The "File" column: the unique string that is part of the file name for the two read pairs, while excluding read and lane information. Example:

AX1212_L001_R1.fastq.gz

AX1212_L001_R2.fastq.gz

Are the two sets of reads for a given sample. Your "File" column value would then be:

AX1212

The "Sample" column: What you would like your sample name to be. This will be used up in all downstream analyses unless changed. Ensure that your samples all have unique names and are not contained within each other (e.g. Name_0 is contained within Name_01). Also exclude special characters and replace spaces with underscores. Hyphens are also ok. In this example, the "Sample" Column would be:

Influenza_virus_AX1212

An example is provided in the main repo (file_rename_example.csv)

Setting up configuration file

Flumina uses a configuration file to keep track of the parameters and easily add new ones. They are included in an example config file in the main repo, "config.cfg"

# The directory where output files will go
OUTPUT_DIRECTORY="/Full/Path/to/Flumina_analysis"

#The directory where your raw reads are located
READ_DIRECTORY="/Flumina/test_dataset"

#CSV with the file name that matches to both read pairs in the "File column" and sample name in the "Sample" column
RENAME_FILE="/Flumina/example_file_rename.csv"

#The path to the reference file
REFERENCE_FILE="/Flumina/reference.fa"

# curated csv database with the columns "Gene", "Amino_Acid", and "Type" (category of site) of interest
AA_DB="/Flumina/curated_database.csv"

# a metadata file with at least one column named "Sample" to join databases
METADATA="/Flumina/example_metadata.csv"

#Group column name from metadata to summarize and group data i.e. cow versus birds versus poultry
GROUP_NAMES="discrete_host"

#Whether to overwrite or not
OVERWRITE=FALSE

#If a job is killed mid job, sometimes snakemake will lock directories
FORCE_UNLOCK=TRUE

#number of threads to use (or jobs to run)
THREADS=24

#multi-job cluster mode, add in cluster job details. THREADS above becomes number of jobs. Set to FALSE to run without new jobs
#CLUSTER_JOBS="sbatch -p priority -D /Flumina_test/Flumina --mem 40G --cpus-per-task=2"
CLUSTER_JOBS=FALSE

Setting up a reference

A reference sequence is needed to map the reads and compare amino acid changes to. This reference should have each gene as a separate entry in the fasta file, with the header including only the gene name.

Optional metadata and other files

irma_config.sh

irma_config.sh, is a configuration file with an example provided here for the program IRMA. Any of the standard IRMA parameters can be changed here and included in your working directory. The 3 essential parameters are provided as an example:

TMP=/Flumina_test/Flumina/irma_tmp
SINGLE_LOCAL_PROC=2
DOUBLE_LOCAL_PROC=1

From IRMA documentation:

Note that SINGLE_LOCAL_PROC should match the number of threads in your job or if using multi-job cluster submission it should match "--cpus-per-task"

sample metadata

For the METADATA parameter in the config.cfg file, a CSV of metadata to join with the amino acid data and summary data can be provided. This CSV file must have at least a column titled "Sample" [capital S] to make the join possible.

curated amino acid databases

Normally Flumina will output databases of all the amino acid changes and then a reduced set to those that are nonsynonymous. To create a database of known amino acids of interest, these can be matched to the full amino acid database and separated into a more manageable table. The three columns this CSV must include are

"Gene" - The Gene that matches the names of the gene used in the reference "Amino_Acid" - The amino acid position in the reference "Type" - A summary of the function of the amino acid change

Running Flumina

After creating the file rename and other configuration files, including optional metadata, and moving them to your work directory with the Flumina scripts, you can run Flumina with this command:

bash Flumina config.cfg

You will have to run Flumina from the cloned git repo, but you can specify the output directory and config.cfg file location using the full paths.

Running Flumina with multi-job submission

Normally increasing the threads will have a small impact on speed. However, here with SnakeMake multiple jobs can submitted from the script itself. For example, setting threads to 24 in the configuration file will instead allow the script to have 24 simultaenous jobs running at once doing little bits and pieces of the pipeline automatically. All that is needed is to remove "CLUSTER_JOBS=FALSE" and insert your job submission script parameters. These are at the top of the job script used for cluster submission.

#number of threads to use (or jobs to run)
THREADS=24

#multi-job cluster mode, add in cluster job details. THREADS above becomes number of jobs. Set to FALSE to run without new jobs
CLUSTER_JOBS="sbatch -p priority -D /Flumina_test/Flumina --mem 40G --cpus-per-task=2"

Upcoming features

1. Apptainer installation (Singularity)
2. Code refinement and speedups