SVPLEX: A Nextflow Pipeline for Cohort-level Structural Variant Calling
TL;DR
SVPLEX is a Nextflow pipeline for cohort-level structural variant detection from short-read whole-genome sequencing data and generates a merged consensus callset across the analysis cohort, which can be used to assess cohort-specific variation, remove technical artefacts, and serve as input for rare disease variant prioritisation workflows.
Abstract
SVPLEX is a Nextflow pipeline for cohort-level structural variant detection from short-read whole-genome sequencing data. The pipeline implements six different structural variant callers with different strengths and weaknesses, integrating different levels of evidence for SVs, and generates a merged consensus callset across the analysis cohort. Callset filtering is achieved by leveraging consensus among multiple individual callers and by ensuring that deletion and duplication calls are supported by observable changes in read depth. The output merged cohort SV callset can then be used to assess cohort-specific variation, remove technical artefacts, and serve as input for rare disease variant prioritisation workflows. SVPLEX is user-friendly, reproducible, scalable, and can be executed flexibly on either a local workstation, a high-performance compute (HPC) cluster, or deployed on cloud infrastructure. The required inputs are alignment files for the cohort of interest, and the output is a single merged cohort structural variant VCF. SVPLEX is available on GitHub (bahlolab/SVPLEX) and is licensed under the MIT open-source licence.