Install

Setup miniconda and then update conda:

conda update -n base -c defaults conda

Add channels

conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda

Clone the course repository:

git clone https://github.com/geparada/RNA_seq_course2020

Create a conda enviroment with snakemake installed on it. We can call this virtual enviroment snakemake_env, but any other name would also work.

conda create -n snakemake_env snakemake   

Activate conda enviroment:

conda activate snakemake_env

Download Data

snakemake --resources get_data=1 --use-conda download_all

Run

In order to check that the full snakemake pipeline is working correctly, we can try with an dry-run (-n) while we print the commands of every step (-p):

snakemake --use-conda -np whippet_delta

To run the full you need to download D. melanogaster transcript annotation from UCSC Table Browser and save it as Gene_annotation/dm6.Ensembl.genes.gtf (replacing the empty file from this repository). Then select the number of processor you want to run snakemake with and run it as:

snakemake --use-conda whippet_delta

Run from a cluster

We provide a cluster.json where we provide the variables that lsf (current queueing system at Wellcome Sanger Institute cluster) needs to run. You can create a new cluster.jsonfile to addapt this Snakefile to any other queueing system.

snakemake --cluster-config cluster.json --cluster "bsub -n {cluster.nCPUs} -R {cluster.resources} -c {cluster.tCPU} -G {cluster.Group} -q {cluster.queue} -o {cluster.output} -e {cluster.error} -M {cluster.memory}" --use-conda -k -j 100