This Snakemake workflow uses Fastp to QC the reads, Salmon for transcript quantification, Tximport to convert from transcript abundance to gene abundance, and DEseq2 for differential gene expression.

• Clone the repository:
  • git clone https://github.com/davidecarlson/Snakemake_diff_gene_expr.git
• Use conda to install create a new environment with all the necessary software from the provided yaml file:
  • conda env create -f environment.yml

• Edit the example_config.yaml file to provide information regarding your data and experiment:

  • path to the RNA-Seq reads
  • path to save the results
  • information regarding the experimental conditions and samples being used
  • number of threads to use
  • species species ("human", "mouse", or "neither")
  • path to your Salmon index, which must be built ahead of time
    • see here for instructions on building the index
  • path to gtf file of gene annotations, if "neither" was chosen as your species
  • path to tab-separated file with list of samples and associated experimental condtions (e.g., "samples.txt")
  • name of the condition from "samples.txt" that should act as the control
  • whether or not input reads are gzipped
  • whether or not input reads are paired

• Edit the samples.txt file to include a row for each sample, including sample name, run name (can be the same as sample name if there is only one run per sample), and experimental condition for that sample

• conda activate snakemake_DGE
• snakemake --cores <# of threads * # of processes to run currently> -s <path to snakefile>

All output files and logs will be saved in the directory specificed in your config file

• Add more automation so that fewer files need to be manually edited prior to usage