A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data.

ABSTRACT: We propose a unified framework for the analysis of chromatin (Ch) immunoprecipitation (IP) microarray (ChIP-chip) data for detecting transcription factor binding sites (TFBSs) or motifs. ChIP-chip assays are used to focus the genome-wide search for TFBSs by isolating a sample of DNA fragments with TFBSs and applying this sample to a microarray with probes corresponding to tiled segments across the genome. Present analytical methods use a two-step approach: (i) analyze array data to estimate IP-enrichment peaks then (ii) analyze the corresponding sequences independently of intensity information. The proposed model integrates peak finding and motif discovery through a unified Bayesian hidden Markov model (HMM) framework that accommodates the inherent uncertainty in both measurements. A Marko

SUBMITTER: Gelfond JA 

PROVIDER: S-EPMC2794970 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature