A probabilistic method to detect regulatory modules.

Saurabh Sinha, Erik van Nimwegen, Eric D. Siggia

Research output: Contribution to journalArticle

Abstract

MOTIVATION: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. RESULTS: We develop a computational method that uses Hidden Markov Models and an Expectation Maximization algorithm to detect such modules, given the weight matrices of a set of transcription factors known to work together. Two novel features of our probabilistic model are: (i) correlations between binding sites, known to be required for module activity, are exploited, and (ii) phylogenetic comparisons among sequences from multiple species are made to highlight a regulatory module. The novel features are shown to improve detection of modules, in experiments on synthetic as well as biological data.

Original languageEnglish (US)
Pages (from-to)i292-301
JournalBioinformatics (Oxford, England)
Volume19 Suppl 1
DOIs
StatePublished - 2003
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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