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

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


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Genome, Human*
  • Genomic Structural Variation*
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Sequence Analysis, DNA
  • Software