title: A probabilistic method to detect regulatory modules.
abstract: BACKGROUND
  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.
authors.affiliation: Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 0021, USA. saurabh@lonnrot.rockefeller.edu
authors.forename: Saurabh
authors.initials: S
authors.lastname: Sinha
authors.forename: Erik
authors.initials: E
authors.lastname: van Nimwegen
authors.forename: Eric D
authors.initials: ED
authors.lastname: Siggia
mesh_terms: D000465:Algorithms; D029721:Drosophila Proteins / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D010802:Phylogeny; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D012689:Sequence Homology, Nucleic Acid; D012984:Software; D013045:Species Specificity
chemical_list: D029721:Drosophila Proteins