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XRate: a fast prototyping, training and annotation tool for phylo-grammars.


ABSTRACT:

Background

Recent years have seen the emergence of genome annotation methods based on the phylo-grammar, a probabilistic model combining continuous-time Markov chains and stochastic grammars. Previously, phylo-grammars have required considerable effort to implement, limiting their adoption by computational biologists.

Results

We have developed an open source software tool, xrate, for working with reversible, irreversible or parametric substitution models combined with stochastic context-free grammars. xrate efficiently estimates maximum-likelihood parameters and phylogenetic trees using a novel "phylo-EM" algorithm that we describe. The grammar is specified in an external configuration file, allowing users to design new grammars, estimate rate parameters from training data and annotate multiple sequence alignments without the need to recompile code from source. We have used xrate to measure codon substitution rates and predict protein and RNA secondary structures.

Conclusion

Our results demonstrate that xrate estimates biologically meaningful rates and makes predictions whose accuracy is comparable to that of more specialized tools.

SUBMITTER: Klosterman PS 

PROVIDER: S-EPMC1622757 | biostudies-literature | 2006 Oct

REPOSITORIES: biostudies-literature

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XRate: a fast prototyping, training and annotation tool for phylo-grammars.

Klosterman Peter S PS   Uzilov Andrew V AV   Bendaña Yuri R YR   Bradley Robert K RK   Chao Sharon S   Kosiol Carolin C   Goldman Nick N   Holmes Ian I  

BMC bioinformatics 20061003


<h4>Background</h4>Recent years have seen the emergence of genome annotation methods based on the phylo-grammar, a probabilistic model combining continuous-time Markov chains and stochastic grammars. Previously, phylo-grammars have required considerable effort to implement, limiting their adoption by computational biologists.<h4>Results</h4>We have developed an open source software tool, xrate, for working with reversible, irreversible or parametric substitution models combined with stochastic c  ...[more]

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