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Activity Number:
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59
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #308374 |
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Title:
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Hidden Markov Model for Jointly Modeling Probe Sequences and ChIP-Chip Microarray Data
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Author(s):
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Jonathan Gelfond*+ and Mayetri Gupta and Joseph G. Ibrahim
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
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Address:
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823 Hill Top Circle, Sanford, NC, 27332,
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Keywords:
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ChIP-chip ; Microarray ; Hidden Markov Model ; Sequence Analysis
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Abstract:
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We propose a unified framework for the analysis of Chromatin (Ch)Immunoprecipitation (IP)microarray (ChIP-chip) data and the detection of transcription factor binding sites (TFBSs). 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 the array data to discover peaks or regions of IP enrichment then analyze the sequences of these peaks in a separate procedure to discover the TFBS motifs. The proposed method will jointly model ChIP-chip intensity and DNA sequences through a Bayesian Hidden Markov model (HMM) which identifies TFBSs. The method is applied to simulated and yeast datasets and has favorable TFBS discovery performance compared to current methods.
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