Cho, AraShim, JungKim, EiruSupek, FranLehner, Ben, 1978-Lee, Insuk2016-11-282016-11-282016Cho A, Shim J, Kim E, Supek F, Lehner B, Lee I. MUFFINN: cancer gene discovery via network analysis of somatic mutation data. Genome Biology. 2016; 17:129. DOI: 10.1186/s13059-016-0989-x1474-760Xhttp://hdl.handle.net/10230/27623A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations.application/pdfeng© 2016 The Author(s). Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s13059-016-0989-xCancer gene predictionCancer somatic mutationCancer genomesMutation frequencyFunctional gene networkPathway-centric analysisinfo:eu-repo/semantics/openAccess