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Snakemake workflow for TitanCNA_SV_WGS

Modules to load

  • ml snakemake/5.19.2-foss-2019b-Python-3.7.4
  • ml R/3.6.2-foss-2019b-fh1
  • ml Python/3.7.4-foss-2019b-fh1
  • ml BCFtools/1.9-GCC-8.3.0
  • ml Pysam/0.15.4-GCC-8.3.0-Python-3.7.4
  • ml PyYAML/5.1.2-GCCcore-8.3.0-Python-3.7.4

Set-up

config/samples.yaml

Please specify the samples to be analyzed in config/samples.yaml, following the format explained therein.

config/config.yaml

There are a number of parameters to adjust in config/config.yaml. Filepaths to where your TitanCNA and ichorCNA repository as well as the filepath to tools (samTools, bcfTools, svaba) and readCounterScript.

Running the snakemake workflows on slurm cluster

This workflow will run TitanCNA (CN) anlaysis for a set of tumor-normal pairs, starting from the aligned BAM files.

snakemake -s TitanCNA.snakefile --latency-wait 60 --restart-times 3 --keep-going --cluster-config config/cluster_slurm.yaml --cluster "sbatch -p {cluster.partition} --mem={cluster.mem} -t {cluster.time} -c {cluster.ncpus} -n {cluster.ntasks} -o {cluster.output}" -j 30

This workflow will run SvABA structural variation (SV) analysis for a set of tumor-normal pairs, starting from the aligned BAM files.

snakemake -s svaba.snakefile --latency-wait 60 --cluster-config config/cluster_slurm.yaml --cluster "sbatch -p {cluster.partition} --mem={cluster.mem} -t {cluster.time} -c {cluster.ncpus} -n {cluster.ntasks} -o {cluster.output}" -j 30

SV classes are predicted by combining SV results with copy number results previously generated by TitanCNA to determine SV classes.

snakemake -s combineSvabaTitan.snakefile --latency-wait 60 --keep-going --restart-times 3 --cluster-config config/cluster_slurm.yaml --cluster "sbatch -p {cluster.partition} --mem={cluster.mem} -t {cluster.time} -c {cluster.ncpus} -n {cluster.ntasks} -o {cluster.output}" -j 30

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