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

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

Leçons de physique experimentale. Tome 2 / . Par M. Sigaud de La Fond,...

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Additional file 6: of FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods

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Additional file 1: of FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods

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