Genome Biol 2018 Mar 20; 19(1):38
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 . Genome Biol 2018 Mar 20; 19(1):38.
Becker, Timothy; Lee, Wan-Ping; Leone, Joseph; Zhu, Qihui; Zhang, Chengsheng; Liu, Silvia; Sargent, Jack; Shanker, Kritika; Mil-Homens, Adam; Cerveira, Eliza; Ryan, Mallory; Cha, Jane; Navarro, Fabio C P; Galeev, Timur; Gerstein, Mark; Mills, Ryan E; Shin, Dong-Guk; Lee, Charles; and Malhotra, Ankit, "FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods." (2018). Faculty Research 2018. 66.