| Literature DB >> 29559002 |
Timothy Becker1,2, Wan-Ping Lee1, Joseph Leone1, Qihui Zhu1, Chengsheng Zhang1, Silvia Liu1, Jack Sargent1, Kritika Shanker1, Adam Mil-Homens1, Eliza Cerveira1, Mallory Ryan1, Jane Cha1, Fabio C P Navarro3,4, Timur Galeev3,4, Mark Gerstein3,4,5, Ryan E Mills6,7, Dong-Guk Shin2, Charles Lee8,9, Ankit Malhotra10.
Abstract
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 .Entities:
Keywords: Copy number variation; Genome rearrangements; Next generation sequencing; Structural variation
Mesh：
Year: 2018 PMID： 29559002 PMCID： PMC5859555 DOI： 10.1186/s13059-018-1404-6
Source DB: PubMed Journal: Genome Biol ISSN： 1474-7596 Impact factor: 13.583