Detection of complex structural variation from paired-end sequencing data

Abstract

AbstractDetecting structural variants (SVs) from sequencing data is key to genome analysis, but methods using standard whole-genome sequencing (WGS) data are typically incapable of resolving complex SVs with multiple co-located breakpoints. We introduce the ARC-SV method, which uses a probabilistic model to detect arbitrary local rearrangements from WGS data. Our method performs well on simple SVs while surpassing state-of-the-art methods in complex SV detection.