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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Detalles Bibliográficos
Autores principales: Becker, Timothy, Lee, Wan-Ping, Leone, Joseph, Zhu, Qihui, Zhang, Chengsheng, Liu, Silvia, Sargent, Jack, Shanker, Kritika, Mil-homens, Adam, Cerveira, Eliza, Ryan, Mallory, Cha, Jane, Navarro, Fabio C. P., Galeev, Timur, Gerstein, Mark, Mills, Ryan E., Shin, Dong-Guk, Lee, Charles, Malhotra, Ankit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5859555/
https://www.ncbi.nlm.nih.gov/pubmed/29559002
http://dx.doi.org/10.1186/s13059-018-1404-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5859555/
https://www.ncbi.nlm.nih.gov/pubmed/29559002
http://dx.doi.org/10.1186/s13059-018-1404-6

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