JSM 2005 - Toronto

Abstract #302557

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 471
Type: Invited
Date/Time: Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #302557
Title: Statistical Analysis for Transcription Regulatory Modules
Author(s): Mayetri Gupta*+ and Jun S. Liu
Companies: University of North Carolina, Chapel Hill and Harvard University
Address: 3107C McGv Bldg CB #7420, Chapel Hill, NC, 27599-7420,
Keywords: transcription regulation ; cis-regulatory modules ; evolutionary Monte Carlo ; motif discovery ; hidden Markov models
Abstract:

Transcription regulation often is controlled by the coordinated binding of one or more transcription factors (TFs) in the promoter regions of genes. In higher eukaryotes, TF binding sites (TFBS), or motifs, often are not as well conserved compared to prokaryotes, but tend to occur as homotypic or heterotypic clusters (cis-regulatory modules). The number of sites and distances between the sites, however, vary greatly in a module. The main statistical challenges posed by this problem are (1) determining the likely motifs comprising the module from an extremely large collection of (potentially inaccurate) TFBS matrices and (2) determining site locations of the motifs on the promoter sequences. In this paper, we propose a statistical model that utilizes the underlying cluster structure as well as individual motif conservation to discover novel regulatory modules in upstream sequences of genes. We also apply a novel Monte Carlo sampling method to find the optimal solution. This method has been successfully applied to examples ranging from bacterial to insect and human genomes.

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Revised March 2005