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Genome Biol. 2018 Mar 20;19(1):38. doi: 10.1186/s13059-018-1404-6.

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

Author information

1
The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
2
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
3
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
4
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
5
Department of Computer Science, Yale University, New Haven, CT, USA.
6
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
7
Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
8
The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA. Charles.Lee@jax.org.
9
The Department of Life Sciences, Ewha Womans University, Seoul, Korea. Charles.Lee@jax.org.
10
The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA. Ankit.Malhotra@jax.org.

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 .

KEYWORDS:

Copy number variation; Genome rearrangements; Next generation sequencing; Structural variation

PMID:
29559002
PMCID:
PMC5859555
DOI:
10.1186/s13059-018-1404-6
[Indexed for MEDLINE]
Free PMC Article

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