FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods.
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ABSTRACT:
SUBMITTER: Becker T
PROVIDER: S-EPMC5859555 | biostudies-literature | 2018 Mar
REPOSITORIES: biostudies-literature
Publications
FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods.
Becker Timothy T Lee Wan-Ping WP Leone Joseph J Zhu Qihui Q Zhang Chengsheng C Liu Silvia S Sargent Jack J Shanker Kritika K Mil-Homens Adam A Cerveira Eliza E Ryan Mallory M Cha Jane J Navarro Fabio C P FCP Galeev Timur T Gerstein Mark M Mills Ryan E RE Shin Dong-Guk DG Lee Charles C Malhotra Ankit A
Genome biology 20180320 1
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. Experime ...[more]
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