RNA_seq_Snakemake

A snakemake pipeline for a quick analysis of RNA-seq data using salmon

Aim

Count, normalize and get differential expressions of paired-end Illumina RNA-seq data, saving time and disk space by using Salmon that preduces counts table without first producing alignment files (bam/sam).

Description

Content

• Snakefile containing the targeted output and the rules to generate them from the input files.
• config/ , folder containing the configuration files making the Snakefile adaptable to any input files, genome and parameter for the rules.
• data/, folder containing samples.txt (sample descriptions) and subsetted paired-end fastq files used to test locally the pipeline. Generated using Seqtk: seqtk sample -s100 {inputfile(can be gzipped)} 250000 > {output(always gunzipped)}
• envs/, folder containing the environments needed for the Snakefile to run. Need to make one specifically for MACS2 as MACS2 uses python 2.7 following the information found here.

Usage

Conda environment

First, you need to install all softwares and packages needed with the Conda package manager.

1. Create a virtual environment named "chipseq" from the environment.yaml file with the following command: conda env create --name RNA-Seq --file ~/envs/DCM.yaml
2. Then, activate this virtual environment with: source activate RNA-Seq
   Now, all the basic softwares and packages versions in use are the one listed in the DCM.yaml file. The other environments (hisat2, subRead etc) will automatically be created and activated when requested by a rule.

Configuration file

The ~/configs/config.yaml file specifies the sample data file, the genomic and transcriptomic reference fasta files to use, the parameters for the rules to use, etc. This file is used so the Snakefile does not need to be changed when locations or parameters need to be changed.

Snakemake execution

The Snakemake pipeline/workflow management system reads a master file (often called Snakefile) to list the steps to be executed and defining their order. It has many rich features. Read more here.

Dry run

Use the command snakemake --use-conda -np to perform a dry run that prints out the rules and commands.

Real run

Simply type Snakemake --use-conda and provide the number of cores with --cores 10 for ten cores for instance.
For cluster execution, please refer to the Snakemake reference.

Main outputs

the alignment files (*.bam), fastqc report files, raw countsfile (counts.txt), results file containing normalized differential expressions (results.tsv)