GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding.

ABSTRACT:

Motivation

The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies.

Results

We present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence d

SUBMITTER: Zeng H 

PROVIDER: S-EPMC5860000 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature