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ervx 🔬

Endogenous Retrovirus Expression Pipeline for Human and Mouse

tests docs GitHub issues GitHub license

This is the home of the pipeline, ervx. Its long-term goals: to accurately characterize endogenous retrovirus expression like no pipeline before!

Overview

Welcome to ervx! Before getting started, we highly recommend reading through ervx's documentation.

The ./ervx pipeline is composed several inter-related sub commands to setup and run the pipeline across different systems. Each of the available sub commands perform different functions:

  • ervx run: Run the ervx pipeline with your input files.
  • ervx unlock: Unlocks a previous runs output directory.
  • ervx cache: Cache remote resources locally, coming soon!

ervx is a comprehensive pipeline to characterize endogenous retrovirus expression in human and mouse samples. It relies on technologies like Singularity1 to maintain the highest-level of reproducibility. The pipeline consists of a series of data processing and quality-control steps orchestrated by Snakemake2, a flexible and scalable workflow management system, to submit jobs to a cluster.

The pipeline is compatible with data generated from Illumina short-read sequencing technologies. As input, it accepts a set of FastQ files and can be run locally on a compute instance or on-premise using a cluster. A user can define the method or mode of execution. The pipeline can submit jobs to a cluster using a job scheduler like SLURM and UGE. A hybrid approach ensures the pipeline is accessible to all users.

Before getting started, we highly recommend reading through the usage section of each available sub command.

For more information about issues or trouble-shooting a problem, please checkout our FAQ prior to opening an issue on Github.

Dependencies

Requires: singularity>=3.5 snakemake>=6.0

At the current moment, the pipeline uses docker images for every step in the pipeline! With that being said, snakemake and singularity must be installed on the target system. Snakemake orchestrates the execution of each step in the pipeline. To guarantee the highest level of reproducibility, each step of the pipeline will rely on versioned images from DockerHub. Snakemake uses singularity to pull these images onto the local filesystem prior to job execution, and as so, snakemake and singularity will be the only two dependencies in the future.

Installation

Biowulf

Please clone this repository using the following commands:

# Clone Repository from Github
git clone https://github.com/OpenOmics/ervx.git
# Change your working directory
cd ervx/
# Add dependencies to $PATH
# Biowulf users should run
module load snakemake singularity
# Get usage information
./ervx -h

Locus

Please clone this repository using the following commands:

# Clone Repository from Github
git clone https://github.com/OpenOmics/ervx.git
# Add dependencies to $PATH
# LOCUS users should run
qrsh -l h_vmem=4G -pe threaded 4
module load snakemake
# Change your working directory
cd ervx/
# Get usage information
./ervx -h

Run

Biowulf

module load snakemake singularity
./ervx run \
    --input /data/NCBR/*.fastq.gz \
    --output /data/NCBR/project/results \
    --genome mm10_70 \
    --mode slurm \
    --star-2-pass-basic \
    --sif-cache /data/NCBR/dev/SIFs/ \
    --dry-run

Locus

qrsh -l h_vmem=4G -pe threaded 4
module load snakemake
# Change your working directory
cd ervx/
# On Locus --mode --tmp-dir --shared-resources --sif-cache are required options.
./ervx run \
    --input /hpcdata/dir/NCBR/*.fastq.gz \
    --output /hpcdata/NCBR/project/results/ \
    --genome mm10_70 \
    --mode uge \
    --star-2-pass-basic \
    --sif-cache /hpcdata/openomics/SIFs/ \
    --tmp-dir /hpcdata/scratch/ \
    --shared-resources /hpcdata/openomics/references/ervx/SHARED_RESOURCES/ \
    --dry-run

Contribute

This site is a living document, created for and by members like you. ervx is maintained by the members of OpenOmics and is improved by continous feedback! We encourage you to contribute new content and make improvements to existing content via pull request to our GitHub repository.

References

1. Kurtzer GM, Sochat V, Bauer MW (2017). Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459.
2. Koster, J. and S. Rahmann (2018). "Snakemake-a scalable bioinformatics workflow engine." Bioinformatics 34(20): 3600.