VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.

ABSTRACT: A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are se

SUBMITTER: Jia P 

PROVIDER: S-EPMC3916227 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature