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.

• TitanCNA repository : https://github.com/gavinha/TitanCNA

• ichorCNA repository : https://github.com/GavinHaLab/ichorCNA

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