FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods
| Autor: | Adam Mil-homens, Timothy James Becker, Jane Cha, Ankit Malhotra, Wan-Ping Lee, Kritika Shanker, Chengsheng Zhang, Silvia Liu, Fabio C. P. Navarro, Jack Sargent, Charles Lee, Eliza Cerveira, Ryan E. Mills, Mallory Ryan, Dong-Guk Shin, Mark Gerstein, Qihui Zhu, Timur R. Galeev, Joseph Leone |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
lcsh:QH426-470 Genome rearrangements Method Biology DNA sequencing Structural variation 03 medical and health sciences 0302 clinical medicine Next generation sequencing Humans Copy-number variation 1000 Genomes Project lcsh:QH301-705.5 Genome Human Copy number variation High-Throughput Nucleotide Sequencing Sequence Analysis DNA Experimental validation lcsh:Genetics 030104 developmental biology lcsh:Biology (General) Genomic Structural Variation Human genome Algorithm Merge (version control) Algorithms Software 030217 neurology & neurosurgery |
| Zdroj: | Genome Biology, Vol 19, Iss 1, Pp 1-14 (2018) Genome Biology |
| DOI: | 10.1186/s13059-018-1404-6 |
| Popis: | 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. Electronic supplementary material The online version of this article (10.1186/s13059-018-1404-6) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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