Bulk-RNAseq data processing for differential expression assessment

Gene-level differential expression assessment from bulk RNA-seq data with DESeq2, EdgeR, and Voom.

How to use this pipeline

Step 1: Configure workflow Set the input read file directory, the reference sequence, etc. in the config.yaml file.

Step 2: Provide sample annotation Provide annotation of which sample corresponds to which treatment in the samples.tsv file.

Step 3: Setup your shell envinroment Provide optional configuration of shell in config.sh (e.g. "module load <...>" or "export PATH=<...>" or "source activate ")

Step 4: Test setup Test configuration in dry-run: snakemake -n / snakemake --use-conda -n

Step 5: Execute the workflow locally via

snakemake --use-conda --cores $ncpu using $ncpu cores, or run it on a cluster via

runSnakemake.sh (with ressource requirement configuration in cluster.json)

Workflow

• Read data QC with fastqc
• Trimming with Trimmomatic
• Alignment to reference with STAR
• Mapping QC with qualimap and picard
• Differential expression assessment with DESeq2, EdgeR, Voom
• QC report generation with Multiqc

Test Dataset

• genome: ftp.ensembl.org/pub/release-90/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna_sm.chromosome.21.fa.gz
• annotation: ftp.ensembl.org/pub/release-90/gtf/homo_sapiens/Homo_sapiens.GRCh38.90.gtf.gz
• reads: sampled from https://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/HG00096.1.M_111124_6.bam

Dependencies

To install:

• star
• multiqc
• fastqc
• trimmomatic
• qualimap
• picard

Via anaconda with --use-conda:

• deseq2
• biocparallel
• edger
• limma