title: The Gibbs Centroid Sampler.
abstract: The Gibbs Centroid Sampler is a software package designed for locating conserved elements in biopolymer sequences. The Gibbs Centroid Sampler reports a centroid alignment, i.e. an alignment that has the minimum total distance to the set of samples chosen from the a posteriori probability distribution of transcription factor binding-site alignments. In so doing, it garners information from the full ensemble of solutions, rather than only the single most probable point that is the target of many motif-finding algorithms, including its predecessor, the Gibbs Recursive Sampler. Centroid estimators have been shown to yield substantial improvements, in both sensitivity and positive predictive values, to the prediction of RNA secondary structure and motif finding. The Gibbs Centroid Sampler, along with interactive tutorials, an online user manual, and information on downloading the software, is available at: http://bayesweb.wadsworth.org/gibbs/gibbs.html.
authors.affiliation: Center for Computational Molecular Biology and the Division of Applied Mathematics, Brown University, Providence, RI 02912, USA. william_thompson_l@brown.edu
authors.forename: William A
authors.initials: WA
authors.lastname: Thompson
authors.forename: Lee A
authors.initials: LA
authors.lastname: Newberg
authors.forename: Sean
authors.initials: S
authors.lastname: Conlan
authors.forename: Lee Ann
authors.initials: LA
authors.lastname: McCue
authors.forename: Charles E
authors.initials: CE
authors.lastname: Lawrence
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D014644:Genetic Variation; D020407:Internet; D008390:Markov Chains; D009010:Monte Carlo Method; D012091:Repetitive Sequences, Nucleic Acid; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D018709:Statistics, Nonparametric; D014157:Transcription Factors / Q000378:metabolism*; D024363:Transcription Initiation Site; D014584:User-Computer Interface
chemical_list: D014157:Transcription Factors