SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes.

Abstract

Structural variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease. Here, we describe SV2, a machine-learning algorithm for genotyping deletions and duplications from paired-end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations. SV2 is freely available on GitHub (https://github.com/dantaki/SV2). jsebat@ucsd.edu. Supplementary data are available at Bioinformatics online.