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SV2: Accurate Structural Variation Genotyping and De Novo Mutation Detection from Whole Genomes

Overview
Journal Bioinformatics
Publisher Oxford University Press
Specialty Biology
Date 2018 Jan 5
PMID 29300834
Citations 21
Authors
Danny Antaki
William M Brandler
Jonathan Sebat
Affiliations
Soon will be listed here.
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.

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