Highly portable parallel workflow to detect structural variants in cancer genomes.
* Enables comprehensive detection of structural variants (SVs) by combining multiple tools * Supports both single-sample (germline) and paired-sample (somatic) SV analyses * Makes the analyses scalable and easily portable across HPC clusters (e.g., using GridEngine, Slurm etc.) or compute clouds * It's easy to use, deploy and extend with new tools This [Snakemake](https://snakemake.readthedocs.io/)-based workflow combines several state-of-the-art tools (i.e. [Manta](https://github.com/Illumina/manta), [DELLY](https://github.com/dellytools/delly), [LUMPY](https://github.com/arq5x/lumpy-sv) and [GRIDSS](https://github.com/PapenfussLab/gridss)) for detecting structural variants (SVs) in whole genome sequencing data. The workflow is easy to use and to deploy on any Linux-based machine. In particular, the workflow supports automated software deployment, easy configuration and addition of new analysis tools as well as enables to scale from a single computer to different HPC clusters with minimal effort.
sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data
A portable and scalable workflow for detecting structural variants in whole-genome sequencing data
sv-channels: filtering genomic deletions using one-dimensional convolutional neural networks