FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods.

Authors: Becker, Timothy and Lee, Wan-Ping and Leone, Joseph and Zhu, Qihui and Zhang, Chengsheng and Liu, Silvia and Sargent, Jack and Shanker, Kritika and Mil-Homens, Adam and Cerveira, Eliza and Ryan, Mallory and Cha, Jane and Navarro, Fabio C P and Galeev, Timur and Gerstein, Mark and Mills, Ryan E and Shin, Dong-Guk and Lee, Charles and Malhotra, Ankit

Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE .

Journal: Genome biology
DOI: 10.1186/s13059-018-1404-6
Year: 2018

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