Document Type
Article
Publication Date
3-20-2018
JAX Source
Genome Biol 2018 Mar 20; 19(1):38
Volume
19
Issue
1
First Page
38
Last Page
38
ISSN
1474-760X
PMID
29559002
DOI
https://doi.org/10.1186/s13059-018-1404-6
Grant
CA034196, HG007497
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 . Genome Biol 2018 Mar 20; 19(1):38.
Recommended Citation
Becker T,
Lee W,
Leone J,
Zhu Q,
Zhang C,
Liu S,
Sargent J,
Shanker K,
Mil-Homens A,
Cerveira E,
Ryan M,
Cha J,
Navarro F,
Galeev T,
Gerstein M,
Mills R,
Shin D,
Lee C,
Malhotra A.
FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods. Genome Biol 2018 Mar 20; 19(1):38