Integrative analysis of cancer genes in a functional interactome.

ABSTRACT: The post-genomic era has resulted in the accumulation of high-throughput cancer data from a vast array of genomic technologies including next-generation sequencing and microarray. As such, the large amounts of germline variant and somatic mutation data that have been generated from GWAS and sequencing projects, respectively, show great promise in providing a systems-level view of these genetic aberrations. In this study, we analyze publicly available GWAS, somatic mutation, and drug target data derived from large databanks using a network-based approach that incorporates directed edge information under a randomized network hypothesis testing procedure. We show that these three classes of disease-associated nodes exhibit non-random topological characteristics in the context of a functional

SUBMITTER: Ung MH 

PROVIDER: S-EPMC4928112 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature