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Biometrics. 2009 Dec;65(4):1087-95. doi: 10.1111/j.1541-0420.2008.01180.x.

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

Author information

  • 1Department of Epidemiology and Biostatistics, Mail Code 7933, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229-3900, USA. gelfondjal@uthscsa.edu

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 Markov chain Monte Carlo algorithm is formulated for parameter estimation, adapting recursive techniques used for HMMs. In simulations and applications to a yeast RAP1 dataset, the proposed method has favorable TFBS discovery performance compared to currently available two-stage procedures in terms of both sensitivity and specificity.

PMID:
19210737
[PubMed - indexed for MEDLINE]
PMCID:
PMC2794970
Free PMC Article
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