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


ABSTRACT: Motivation:Structural variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease. Results: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. Availability and implementation:SV2 is freely available on GitHub (https://github.com/dantaki/SV2). Contact:jsebat@ucsd.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Antaki D 

PROVIDER: S-EPMC5946924 | biostudies-literature |

REPOSITORIES: biostudies-literature

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