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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 mode...
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| Publicado en: | Genome Biol |
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
BioMed Central
2018
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5859555/ https://ncbi.nlm.nih.gov/pubmed/29559002 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s13059-018-1404-6 |
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