Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models.

ABSTRACT: Cancer genomes contain vast amounts of somatic mutations, many of which are passenger mutations not involved in oncogenesis. Whereas driver mutations in protein-coding genes can be distinguished from passenger mutations based on their recurrence, non-coding mutations are usually not recurrent at the same position. Therefore, it is still unclear how to identify cis-regulatory driver mutations, particularly when chromatin data from the same patient is not available, thus relying only on sequence and expression information. Here we use machine-learning methods to predict functional regulatory regions using sequence information alone, and compare the predicted activity of the mutated region with the reference sequence. This way we define the Predicted Regulatory Impact of a Mutation in an Enha

SUBMITTER: Svetlichnyy D 

PROVIDER: S-EPMC4642938 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature