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PubMed · 12855472

A probabilistic method to detect regulatory modules.

Saurabh Sinha·Erik van Nimwegen·Eric D Siggia
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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.

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AlgorithmsDrosophila ProteinsGene Expression ProfilingGene Expression RegulationLikelihood FunctionsMarkov ChainsModels, GeneticModels, StatisticalPhylogenyRegulatory Sequences, Nucleic AcidSequence AlignmentSequence Analysis, DNASequence Homology, Nucleic AcidSoftwareSpecies SpecificityComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Saurabh Sinha, Erik van Nimwegen, Eric D Siggia. 2003. A probabilistic method to detect regulatory modules.. https://doi.org/10.1093/bioinformatics%2Fbtg1040

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