Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data | Zendy

Runjun D. Kumar | Zendy; Adam C. Searleman | Zendy; S. Joshua Swamidass | Zendy; Obi L. Griffith | Zendy; Ron Bose | Zendy

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Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data

Author(s) -

Runjun D. Kumar,

Adam C. Searleman,

S. Joshua Swamidass,

Obi L. Griffith,

Ron Bose

Publication year - 2015

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/btv430

Subject(s) - gene , cancer , biology , genome , suppressor , genetics , computational biology , function (biology) , mutation

Several tools exist to identify cancer driver genes based on somatic mutation data. However, these tools do not account for subclasses of cancer genes: oncogenes, which undergo gain-of-function events, and tumor suppressor genes (TSGs) which undergo loss-of-function. A method which accounts for these subclasses could improve performance while also suggesting a mechanism of action for new putative cancer genes.

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