This Snakemake pipeline starts from a set of nanopore reads and an associated genome, simulate reads, map them to the genome with 2 different tools (minimap2 and ngmlr) and compute various statistics about the correctness of the mapping
- snakemake-minimal =5.2.4
- python =3.6.3
- samtools =1.9
- pysam =0.15.0
- nanostat =1.1.0
- seqtk =1.3
- minimap2 =2.11
- nanosim =2.2.0
- ngmlr =0.2.7
- drmaa =0.7.6
To use this workflow, first download it:
git clone https://github.com/SeqOccin-SV/seqoccinlr.git
This pipeline needs all the tools and versions indicated in the file environment.yaml. An easy way to achieve this is to create a conda environment. For this you need conda (or Miniconda3-4.4.10) and to execute the following commands:
cd seqoccinlr
conda env create --name [yourname] --file environment.yaml
conda activate [yourname]
Configure the workflow according to your needs via editing the file config.yaml.
Test your configuration by performing a dry-run via
snakemake -n
Execute the workflow locally via
snakemake --cores $N
using $N cores or run it in a cluster environment via
snakemake --cluster qsub --jobs 100
or
snakemake --drmaa --jobs 100