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FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods
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DOI：10.1186/s13059-018-1404-6.png)
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Abstract
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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:
Structural variation
Copy number variation
Next generation sequencing
Genome rearrangements
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