Gelfond, J.A.L., Gupta, M. and Ibrahim, J.G. (2009) A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data. Biometrics, 65(4), pp. 1087-1095. (doi: 10.1111/j.1541-0420.2008.01180.x)
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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.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Gupta, Professor Mayetri |
Authors: | Gelfond, J.A.L., Gupta, M., and Ibrahim, J.G. |
College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics |
Journal Name: | Biometrics |
ISSN: | 0006-341X |
ISSN (Online): | 1541-0420 |
Published Online: | 05 February 2009 |
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