title: 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 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.
authors.affiliation: Department 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
authors.forename: Jonathan A L
authors.initials: JA
authors.lastname: Gelfond
authors.forename: Mayetri
authors.initials: M
authors.lastname: Gupta
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001499:Bayes Theorem; D001665:Binding Sites / Q000235:genetics; D001699:Biometry / Q000379:methods*; D047369:Chromatin Immunoprecipitation / Q000706:statistics & numerical data*; D004271:DNA, Fungal / Q000235:genetics / Q000378:metabolism; D023281:Genomics / Q000706:statistics & numerical data*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D000089804:Shelterin Complex; D034501:Telomere-Binding Proteins / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D004271:DNA, Fungal; C440829:RAP1 protein, S cerevisiae; D029701:Saccharomyces cerevisiae Proteins; D000089804:Shelterin Complex; D034501:Telomere-Binding Proteins; D014157:Transcription Factors