Abstract
Structural Variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for analysis, de novo mutations in particular.Here we describe SV2, a machine-learning algorithm for genotyping deletions and tandem duplications from paired-end whole genome sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified callset with low rates of false discoveries and Mendelian errors with accurate de novo detection.
Copyright
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