SGD Paper 
Gelfond JA, et al. (2009) A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data. Biometrics 65(4):1087-95
Abstract: Summary. 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.
| Status: Published | Type: Journal Article | PubMed ID: 19210737 |
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| Topics | Topics not linked to Genes | Genes linked to topics |
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| RAP1 | ||
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| Computational analysis | | |
| Omics | 
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