title: Motif discovery using expectation maximization and Gibbs' sampling.
abstract: Expectation maximization and Gibbs' sampling are two statistical approaches used to identify transcription factor binding sites and the motif that represents them. Both take as input unaligned sequences and search for a statistically significant alignment of putative binding sites. Expectation maximization is deterministic so that starting with the same initial parameters will always converge to the same solution, making it wise to start it multiple times from different initial parameters. Gibbs' sampling is stochastic so that it may arrive at different solutions from the same initial parameters. In both cases multiple runs are advised because comparisons of the solutions after each run can indicate whether a global, optimum solution is likely to have been achieved.
authors.affiliation: Department of Genetics, School of Medicine, Washington University, St. Louis, MO, USA. stormo@wustl.edu
authors.forename: Gary D
authors.initials: GD
authors.lastname: Stormo
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics / Q000378:metabolism; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D013269:Stochastic Processes; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors; D004247:DNA