JSM 2005 Online Program

Abstract #303077

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	178
Type:	Topic Contributed
Date/Time:	Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303077
Title:	Bayesian Models for Regulatory Motif Discovery and Clustering
Author(s):	Shane Jensen*+
Companies:	University of Pennsylvania
Address:	The Wharton School, Department of Statistics, Philadelphia, PA, ,
Keywords:	Bayesian Models ; Motif Discovery ; Genetic Regulation ; Motif Clustering ; Dirichlet Process
Abstract:	Genes often are regulated in living cells by proteins called transcription factors that bind directly to short segments of DNA in close proximity to certain target genes. These segments share a conserved appearance called a motif. Many motif-finding programs exist, but none is clearly superior in all situations. Based on an extended Bayesian model, we derive a comprehensive scoring function that objectively compares discovered motifs, combining the strengths of existing programs. Our algorithm BioOptimizer optimizes our scoring function to reduce noise in the motif signal, which is superior to current programs in simulation studies and real-data applications. We also present a Bayesian hierarchical motif clustering model, based on a Dirichlet process prior, which is implemented using Gibbs sampling. Our motif discovery and clustering models are used in combination to predict coregulated genes in bacteria. Sequences from several related species are used to discover motifs conserved by evolution, which are used to cluster genes into putative coregulated groups validated using several external measures of cell regulation.

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