MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations | Zendy

Sisheng Liu | Zendy; Jinpeng Liu | Zendy; Yanqi Xie | Zendy; Tingting Zhai | Zendy; Eugene W. Hinderer | Zendy; Arnold J. Stromberg | Zendy; Nathan L. Vanderford | Zendy; Jill Kolesar | Zendy; Hunter Moseley | Zendy; Li Chen | Zendy; Chunming Liu | Zendy; Chi Wang | Zendy

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MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations

Author(s) -

Sisheng Liu,

Jinpeng Liu,

Yanqi Xie,

Tingting Zhai,

Eugene W. Hinderer,

Arnold J. Stromberg,

Nathan L. Vanderford,

Jill Kolesar,

Hunter Moseley,

Li Chen,

Chunming Liu,

Chi Wang

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa957

Subject(s) - genome , computational biology , false discovery rate , spurious relationship , mutation , mutation rate , genetics , gene , biology , r package , computer science , data mining , machine learning , computational science

Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency.

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