Composite Motif Prediction Using the Regulatory Module Sampler

• Activity Number: 374
• Type: Contributed
• Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Mayetri Gupta*+ and Jun S. Liu
• Companies: Harvard University and Harvard University
• Keywords: gene regulation ; regulatory module ; evolutionary Monte Carlo ; hidden Markov models

Abstract

A better understanding of gene regulatory networks could lead to momentous advances in medical research and drug discovery. Accurate discovery of regulatory binding sites (motifs), conserved patterns in DNA sequences, is a first necessary step. In eukaryotic genomes, motif detection is a challenging problem as motifs tend to be short, variable, and occur in multi-pattern clusters (regulatory modules). In this paper we introduce a Bayesian methodology for state-space model selection and parameter estimation in hidden Markov models, and apply it to the location of regulatory modules. Given a potentially large and diverse starting set of motif profile matrices we need to find motif classes comprising the module and the location of sites. The module framework assumes an underlying Markov structure for the position of sites on the sequence and also the order of pattern types. A fast algorithm based on evolutionary Monte Carlo (Liang and Wong 2000) enables us to detect likely clusters comprising the module and obtain improved parameter estimates. The performance of the new method will be demonstrated by both simulation studies and applications to bacterial and human genomes.