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- Title
A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data.
- Authors
Gelfond, Jonathan A L; Gupta, Mayetri; Ibrahim, Joseph G
- 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.
- Publication
Biometrics, 2009, Vol 65, Issue 4, p1087
- ISSN
1541-0420
- Publication type
Journal Article
- DOI
10.1111/j.1541-0420.2008.01180.x