bbglab/deepCSA is a bioinformatics pipeline that can be used for analyzing the clonal structure information from targeted DNA sequencing data. It was designed for duplex sequencing data of normal tissues.
First, prepare a samplesheet with your input data that looks as follows:
samplesheet.csv:
sample,vcf,bam
sample1,sample1.high.filtered.vcf,sample1.sorted.bam
sample2,sample2.high.filtered.vcf,sample2.sorted.bam
Each row represents a single sample with a single-sample VCF containing the mutations called in that sample and the BAM file that was used for getting those variant calls. The mutations will be obtained from the VCF and the BAM file will be used for computing the sequencing depth at each position and using this for the downstream analysis.
Make sure that you do not use any '.' in your sample names, and also use text-like names for the samples, try to avoid having only numbers. This second case should be handled properly but using string-like names will ensure consistency.
There are specific datasets that need to be prepared before running deepCSA. You can find a list of those, and instructions for downloading them in the documentation section of the repo.
After making sure that these files are ready, you can now run the pipeline using:
git clone https://github.com/bbglab/deepCSA.git
cd deepCSA
nextflow run main.nf --outdir <OUTDIR> -profile singularity,<DESIRED PROFILE> -params-file pipeline_params.ymlThe input can be provided by the --input option but it is more recommended to define this and all the other parameters in a parameter file (i.e. pipeline_params.yml), that can be provided to the pipeline for running the analysis with the specified configuration. This will also allow the definition of the remaining required parameters.
Please provide pipeline parameters via the Nextflow -params-file option or CLI. Custom config files including those
provided by the -c Nextflow option can be used to provide any configuration except for parameters_;
see docs.
bbglab/deepCSA was originally written by Ferriol Calvet.
We thank the following people for their extensive assistance in the development of this pipeline:
- @rblancomi
- @FedericaBrando
- @koszulordie
- @St3451
- @AxelRosendahlHuber
- @andrianovam
- @migrau
An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.
This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.
The nf-core framework for community-curated bioinformatics pipelines.
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Find the documentation (link to docs).