ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.

ABSTRACT: Chromatin immunoprecipitation with massively parallel DNA sequencing (ChIP-seq) has greatly improved the reliability with which transcription factor binding sites (TFBSs) can be identified from genome-wide profiling studies. Many computational tools are developed to detect binding events or peaks, however the robust detection of weak binding events remains a challenge for current peak calling tools. We have developed a novel Bayesian approach (ChIP-BIT) to reliably detect TFBSs and their target genes by jointly modeling binding signal intensities and binding locations of TFBSs. Specifically, a Gaussian mixture model is used to capture both binding and background signals in sample data. As a unique feature of ChIP-BIT, background signals are modeled by a local Gaussian distribution that is

SUBMITTER: Chen X 

PROVIDER: S-EPMC4838354 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature