This workflow performs a differential expression analysis with STAR and edgeR - strongly inspired by https://github.com/snakemake-workflows/rna-seq-star-deseq2

• Dean Pettinga @deanpettinga

NOTE this workflow is optimized for HPC3 @ Van Andel Institute.

• Functional installation of conda in your $PATH.
  • Snakemake is invoked from within a conda environment, so you must have your shell initialized for conda.

The following recipe provides established best practices for running and extending this workflow in a reproducible way.

1. Fork the repo to a project directory on /secondary
2. Clone the fork to the desired working directory for your project.
3. Create a new branch (the project-branch) within the clone and switch to it. The branch will contain any project-specific modifications (e.g. to configuration, but also to code).

• Modify the config, and any necessary sheets, e.g.:
  • src/units.tsv
    • sample - ID of biological sample
    • group - comparison group for DE contrast
    • genotype - description of genotype
    • condition - treatment description
    • unit - description of sequencing unit ("lane1" or "flowcell1", etc.)
    • fq1 - name of read1 fastq
    • fq2 - name of read2 fastq
    • strandedness - strandedness of library prep
      • typically reverse for Illumina
      • used to identify column to count reads from STAR output.
  • src/contrasts.tsv
    • contrast - name for edgeR contrast
    • groupRelative - "group" defined in units.tsv
    • groupBaseline - other "group" defined in units.tsv
  • src/config.yaml
    • PE_or_SE - designates the sequencing protocol as paired-end or single-end. (e.g. "PE")
    • ref
      • index - absolute path to your STAR index directory
      • annotation - absolute path to your genome annotation.gtf
    • species
      • short - e.g. "hsapiens"
      • long - e.g. "Homo sapiens"
    • annotation - Bioconductor annotation package (e.g. "org.Hs.eg.db")
• Move your sequencing reads to raw_reads/

Test your configuration by performing a dry-run via

snakemake --use-conda -np

Execute from within your project directory as a PBS job using BBC nodes via

qsub -q bbc src/run_snake.sh

This job script will produce DAG (.txt & .png) and .html with run stats for the workflow to be executed in logs/runs/bulk_rnaseq-workflow_(TIME)

Review your results including the run stats:

• logs/runs/rnaseq-workflow_(TIME).html

and the differential expression results:

• deliverables/edgeR_longReport.html
• deliverables/edgeR_shortReport.html

Now that you've successfully run your analysis, it's time to do some housekeeping.

• Commit any changes you've made to the repo and push the project-branch to your fork on github.
  • Optional: Merge back any valuable and generalizable changes to the [upstream repo] via a pull request. This would be greatly appreciated.
  • Optional: Push results (plots/tables) to the remote branch on your fork.
  • Optional: Create a self-contained workflow archive for publication along with the paper (snakemake --archive).
  • Optional: Delete the local clone/workdir to free space.