FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods
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 fu...
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| Главные авторы: | , , , , , , , , , , , , , , , , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
BMC
2018-03-01
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| Серии: | Genome Biology |
| Предметы: | |
| Online-ссылка: | http://link.springer.com/article/10.1186/s13059-018-1404-6 |
| Метки: |
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