Virus-Phage Detection

Overview

This module is designed to function as both a standalone viral investigation pipeline as well as a component of the larger CAMP metagenomics analysis pipeline. As such, it is both self-contained (ex. instructions included for the setup of a versioned environment, etc.), and seamlessly compatible with other CAMP modules (ex. ingests and spawns standardized input/output config files, etc.).

The processed sequencing reads are assembled with MetaSPAdes, and viral contigs are subsequently identified using the output assembly graph and ViralVerify. Contigs containing putative viral genetic material are also identified using VIBRANT, VirSorter, and VirFinder. The aggregated lists of contigs from the three inference algorithms is dereplicated using VirClust and merged with the ViralVerify list, and the overall quality of the putative viruses is assessed using CheckV.

Installation

1. Clone repo from Github.

2. Set up the conda environment (contains Snakemake, Click, and other essentials) using configs/conda/virus-phage-detect.yaml.

3. Update the relevant parameters (if applicable- for example, location of external non-conda tools) in test_data/parameters.yaml.

4. Make sure the installed pipeline works correctly.

# Create and activate conda environment 
cd camp_virus-phage-detect
conda env create -f configs/conda/virus-phage-detect.yaml
conda activate camp_virus-phage-detect
# Run tests on the included sample dataset
python /path/to/camp_virus-phage-detect/workflow/virus-phage-detect.py test

Using the Module

Input: /path/to/samples.csv provided by the user.

Output: 1) An output config file summarizing 2) the module's outputs.

• /path/to/work/dir/virus-phage-detect/final_reports/{sample}_quality_summary.tsv .csv

Module Structure

└── workflow
    ├── Snakefile
    ├── virus-phage-detect.py
    ├── utils.py
    ├── __init__.py
    └── ext/
        └── scripts/

• workflow/virus-phage-detect.py: Click-based CLI that wraps the snakemake and other commands for clean management of parameters, resources, and environment variables.
• workflow/Snakefile: The snakemake pipeline.
• workflow/utils.py: Sample ingestion and work directory setup functions, and other utility functions used in the pipeline and the CLI.
• ext/: External programs, scripts, and small auxiliary files that are not conda-compatible but used in the workflow.

Running the Workflow

1. Make your own samples.csv based on the template in configs/samples.csv. Sample test data can be found in test_data/.

   • samples.csv requires either absolute paths or paths relative to the directory that the module is being run in

2. Update the relevant parameters in configs/parameters.yaml.

3. Update the computational resources available to the pipeline in configs/resources.yaml.

Command Line Deployment

To run CAMP on the command line, use the following, where /path/to/work/dir is replaced with the absolute path of your chosen working directory, and /path/to/samples.csv is replaced with your copy of samples.csv. - The default number of cores available to Snakemake is 1 which is enough for test data, but should probably be adjusted to 10+ for a real dataset. - Relative or absolute paths to the Snakefile and/or the working directory (if you're running elsewhere) are accepted! - The parameters and resource config YAMLs can also be customized.

python /path/to/camp_virus-phage-detect/workflow/mag_qc.py \
    (-c number_of_cores_allocated) \
    (-p /path/to/parameters.yaml) \
    (-r /path/to/resources.yaml) \
    -d /path/to/work/dir \
    -s /path/to/samples.csv

If for some reason the module keeps failing, CAMP can print a script called commands.sh containing all of the remaining commands that can be run manually.

python /path/to/virus-phage-detect/workflow/virus-phage-detect.py --dry_run \
    -d /path/to/work/dir \
    -s /path/to/samples.csv

Slurm Cluster Deployment

To run CAMP on a job submission cluster (for now, only Slurm is supported), use the following. - --slurm is an optional flag that submits all rules in the Snakemake pipeline as sbatch jobs. - In Slurm mode, the -c flag refers to the maximum number of sbatch jobs submitted in parallel, not the pool of cores available to run the jobs. Each job will request the number of cores specified by threads in configs/resources/slurm.yaml.

sbatch -J jobname -o jobname.log << "EOF"
#!/bin/bash
python /path/to/camp_virus-phage-detect/workflow/mag_qc.py --slurm \
    (-c max_number_of_parallel_jobs_submitted) \
    (-p /path/to/parameters.yaml) \
    (-r /path/to/resources.yaml) \
    -d /path/to/work/dir \
    -s /path/to/samples.csv
EOF

Test Dataset

The test dataset was simulated using ART with the following parameters:

• read length (-l): 150
• mean size of fragment (-m): 200
• standard deviation of fragment (-s): 10.

The following viral species and their corresponding European Nucleotide Archive (ENA) IDs were used as referenced and simulated at coverage (-f):10.

• Corona virus MN996532
• Dengue virus KF952702
• Influenza virus AB262394
• Lambda bacteriophage AB008394
• M13 bacteriophage AY389148
• Norovirus AB019423
• T4 bacteriophage JX183125

Additionally, two bacteria were spiked in with coverage (-f):11. Names and NCBI acession IDs below:

• Escherichi acoli GCF_008761535.2_ASM876153v3
• Streptococcus pneumoniae GCA_002076835.1_ASM207683

Credits

• This package was created with Cookiecutter as a simplified version of the project template.
• Free software: MIT License