#Automated pipeline for alignment and SV calling in long-read datasets

Ensure that snakemake is installed. Clone the repository via git clone https://github.com/aganezov/EnsemblePLInternal.git into a general location (e.g., /path/to/pipelines) on a computing cluster.

Determine and cd into experiment dedicated folder (e.g., experiment). Create symlinks to all snakefile and py files in the EnsemblePIInteral master folder:

ln -s /path/to/pipelines/EnsemblePIInteral/*snakefile .
ln -s /path/to/pipelines/EnsemblePIInteral/*py .

Copy configuration yaml files:

cp /path/to/pipelines/EnsemblePIInteral/*yaml .

Set up targeted dataset inside data.yaml file.

Run snakemake (dry run) to see automated pipeline:

snakemake -s pipeline.snakefile -npr

If the dry run produces satisfactory results, run snakemake with production settings, including, possible cluster setup, multithreading, etc.

snakemake -s pipeline.snakefile --latency-wait 200 -pr -j 20 --rerun-incomplete --cluster "sbatch --account={cluster.account} --partition={cluster.partition} --job-name={cluster.name} --nodes={cluster.nodes} --cpus-per-task={cluster.nCPUs} --time={cluster.time} --out={cluster.out} --err={cluster.err} --mem={cluster.mem_mb}M"

which ensures that

• no more than 20 jobs (-j) are submitted at a time
• any incomplete results from possible previous failed runs are regenerated (--rerun-incomplete)
• sets up a SLURM submission setup, which in turn:
  • requests a single node of parallel partition per job,
  • with a 3 day time limit,
  • with 24G of RAM per node,
  • and with