diff --git a/README.md b/README.md index 708b96765df981e9f8983a80d44ae02120f6edf0..7a3aede9108f6660722fe88fa5a013fbba1bbd5a 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,8 @@ ## What is RNAflow ? -RNAflow is a snakemake pipeline dedicated to standard transcriptomic analysis. +RNAflow is a snakemake pipeline dedicated to standard transcriptomic analysis [@Hub of Bioinformatics & Biostatistics](https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/). + Be careful, RNAflow was principally conceived to run on the HPC of Institut Pasteur, and not on other HPC. @@ -49,19 +50,25 @@ If you simply want to use this workflow, download and extract the latest release * Step 2: Configure workflow -Configure the workflow according to your needs via editing the config.yaml and multiqc_config.yaml files in the config/ directory. +Configure the workflow according to your needs via editing the [config.yaml](https://gitlab.pasteur.fr/hub/rnaflow/-/blob/master/config/config.yaml) and [multiqc_config.yaml](https://gitlab.pasteur.fr/hub/rnaflow/-/blob/master/config/multiqc_config.yaml) files in the `config/` directory. + + +* Step 3: Load your conda environment + +See [runme.sh](https://gitlab.pasteur.fr/hub/rnaflow/-/blob/master/runme.sh) to create the complete conda env. +`conda activate snakemake` -* Step 3: Execute workflow +* Step 4: Execute workflow Test your configuration by performing a dry-run via `snakemake -n ` -run it in a HPC using environment modules via +run it in a Maestro using environment modules via -`snakemake -s Snakefile --use-envmodules --cluster-config config/cluster_config.json --cluster "sbatch --mem={cluster.ram} --cpus-per-task={threads} " -j 200 --nolock` +`sbatch -q hubbioit -p hubbioit snakemake --cluster-config config/cluster_config.json --use-envmodules --cluster "sbatch --mem={cluster.ram} --cpus-per-task={threads} -q hubbioit -p hubbioit" -j 300 --nolock` Visualize how the rules are connected via