Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis.

ABSTRACT: The emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators - such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others - as predictive features of cancer outcome. However, identification of "driver genes" associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively. iEDGE first identifies the cis and trans gene expression signatures ass

SUBMITTER: Li A 

PROVIDER: S-EPMC6858347 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature