title: RNA modeling using Gibbs sampling and stochastic context free grammars.
abstract: A new method of discovering the common secondary structure of a family of homologous RNA sequences using Gibbs sampling and stochastic context-free grammars is proposed. Given an unaligned set of sequences, a Gibbs sampling step simultaneously estimates the secondary structure of each sequence and a set of statistical parameters describing the common secondary structure of the set as a whole. These parameters describe a statistical model of the family. After the Gibbs sampling has produced a crude statistical model for the family, this model is translated into a stochastic context-free grammar, which is then refined by an Expectation Maximization (EM) procedure to produce a more complete model. A prototype implementation of the method is tested on tRNA, pieces of 16S rRNA and on U5 snRNA with good results.
authors.affiliation: Baskin Center for Computer Engineering and Computer and Information Sciences University of California, Santa Cruz 95064, USA.
authors.forename: L
authors.initials: L
authors.lastname: Grate
authors.forename: M
authors.initials: M
authors.lastname: Herbster
authors.forename: R
authors.initials: R
authors.lastname: Hughey
authors.forename: D
authors.initials: D
authors.lastname: Haussler
authors.forename: I S
authors.initials: IS
authors.lastname: Mian
authors.forename: H
authors.initials: H
authors.lastname: Noller
mesh_terms: D000818:Animals; D001483:Base Sequence; D006801:Humans; D008962:Models, Theoretical; D008969:Molecular Sequence Data; D015394:Molecular Structure; D012313:RNA / Q000032:analysis*; D013269:Stochastic Processes
chemical_list: D012313:RNA