Mako: A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

Mako: A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

Complex structural variants (CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants. However, detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy. As...

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Bibliographic Details
Main Authors: Jiadong Lin, Xiaofei Yang, Walter Kosters, Tun Xu, Yanyan Jia, Songbo Wang, Qihui Zhu, Mallory Ryan, Li Guo, Chengsheng Zhang, Charles Lee, Scott E. Devine, Evan E. Eichler, Kai Ye, Mark B. Gerstein, Ashley D. Sanders, Micheal C. Zody, Michael E. Talkowski, Ryan E. Mills, Jan O. Korbel, Tobias Marschall, Peter Ebert, Peter A. Audano, Bernardo Rodriguez-Martin, David Porubsky, Marc Jan Bonder, Arvis Sulovari, Jana Ebler, Weichen Zhou, Rebecca Serra Mari, Feyza Yilmaz, Xuefang Zhao, PingHsun Hsieh, Joyce Lee, Sushant Kumar, Tobias Rausch, Yu Chen, Zechen Chong, Katherine M. Munson, Mark J.P. Chaisson, Junjie Chen, Xinghua Shi, Aaron M. Wenger, William T. Harvey, Patrick Hansenfeld, Allison Regier, Ira M. Hall, Paul Flicek, Alex R. Hastie, Susan Fairely
Format: Article
Language:English
Published: Oxford University Press 2022-02-01
Series:Genomics, Proteomics & Bioinformatics
Subjects:
Next-generation sequencing
Complex structural variant
Pattern growth
Graph mining
Formation mechanism
Online Access:http://www.sciencedirect.com/science/article/pii/S1672022921001431

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http://www.sciencedirect.com/science/article/pii/S1672022921001431

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