title: Modeling transcription factor binding sites with Gibbs Sampling and Minimum Description Length encoding.
abstract: Transcription factors, proteins required for the regulation of gene expression, recognize and bind short stretches of DNA on the order of 4 to 10 bases in length. In general, each factor recognizes a family of "similar" sequences rather than a single unique sequence. Ultimately, the transcriptional state of a gene is determined by the cooperative interaction of several bound factors. We have developed a method using Gibbs Sampling and the Minimum Description Length principle for automatically and reliably creating weight matrix models of binding sites from a database (TRANSFAC) of known binding site sequences. Determining the relationship between sequence and binding affinity for a particular factor is an important first step in predicting whether a given uncharacterized sequence is part of a promoter site or other control region. Here we describe the foundation for the methods we will use to develop weight matrix models for transcription factor binding sites.
authors.affiliation: jschug@cbil.humgen.upenn.edu
authors.forename: J
authors.initials: J
authors.lastname: Schug
authors.forename: G C
authors.initials: GC
authors.lastname: Overton
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites / Q000235:genetics; D004247:DNA / Q000235:genetics / Q000378:metabolism; D016208:Databases, Factual; D008390:Markov Chains; D008954:Models, Biological; D016415:Sequence Alignment / Q000379:methods / Q000706:statistics & numerical data; D013269:Stochastic Processes; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors; D004247:DNA