FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods

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...

Full description

Bibliographic Details
Main Authors: Timothy Becker, Wan-Ping Lee, Joseph Leone, Qihui Zhu, Chengsheng Zhang, Silvia Liu, Jack Sargent, Kritika Shanker, Adam Mil-homens, Eliza Cerveira, Mallory Ryan, Jane Cha, Fabio C. P. Navarro, Timur Galeev, Mark Gerstein, Ryan E. Mills, Dong-Guk Shin, Charles Lee, Ankit Malhotra
Format: Article
Language:English
Published: BMC 2018-03-01
Series:Genome Biology
Subjects:
Structural variation
Copy number variation
Next generation sequencing
Genome rearrangements
Online Access:http://link.springer.com/article/10.1186/s13059-018-1404-6

Internet

http://link.springer.com/article/10.1186/s13059-018-1404-6

Similar Items