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Detection of complex structural variation from paired-end sequencing data

By Joseph G. Arthur, Xi Chen, Bo Zhou, Alexander E Urban, Wing Hung Wong

Posted 08 Oct 2017
bioRxiv DOI: 10.1101/200170

Detecting 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.

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