RNA modeling using Gibbs sampling and stochastic context free grammars.
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.
Grate
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Baskin Center for Computer Engineering and Computer and Information Sciences University of California, Santa Cruz 95064, USA.
Herbster
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Hughey
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Haussler
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Mian
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Noller
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D000818:Animals; D001483:Base Sequence; D006801:Humans; D008962:Models, Theoretical; D008969:Molecular Sequence Data; D015394:Molecular Structure; D012313:RNA / Q000032:analysis*; D013269:Stochastic Processes
D012313:RNA