SV2: accurate structural variation genotyping andde novomutation detection from whole genomes

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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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