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Activity Number:
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116
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #306262 |
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Title:
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Context-Dependent Models for Discovery of Transcription Factor Binding Sites
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Author(s):
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Chuancai Wang*+ and Jun Xie and Bruce A. Craig
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Companies:
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The Pennsylvania State University and Purdue University and Purdue University
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Address:
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600 Centerview Drive, Hershey, PA, 17033,
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Keywords:
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conditional position dependent motif model ; Gibbs sampler ; Markov model ; Markov order determination ; transcription factor binding site
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Abstract:
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Transcription factors play a crucial role in gene regulation, and the identification of transcription factor binding sites helps gain insight into gene regulatory mechanisms. The overall goal of this work is to describe a new method of binding site detection called Motif Discovery via Context Dependent Models (MDCDM). We characterize the motif (i.e., binding sites) by a series of position-dependent first-order Markov models. In addition, a ``step-up'' testing procedure is used to automatically determine the best-fitting Markov model for the background (i.e., nonsite regions). We compare our approach with the existing methods using both real and simulated data sets. The results show that the detection of binding sites can be greatly improved by accounting for dependence across positions in a motif and appropriately modeling the background dependence.
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