CaDrA: A Computational Framework for Performing Candidate Driver Analyses Using Genomic Features.

ABSTRACT: The identification of genetic alteration combinations as drivers of a given phenotypic outcome, such as drug sensitivity, gene or protein expression, and pathway activity, is a challenging task that is essential to gaining new biological insights and to discovering therapeutic targets. Existing methods designed to predict complementary drivers of such outcomes lack analytical flexibility, including the support for joint analyses of multiple genomic alteration types, such as somatic mutations and copy number alterations, multiple scoring functions, and rigorous significance and reproducibility testing procedures. To address these limitations, we developed Candidate Driver Analysis or CaDrA, an integrative framework that implements a step-wise heuristic search approach to identify functional

SUBMITTER: Kartha VK 

PROVIDER: S-EPMC6390206 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature