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

Klosterman PS, Uzilov AV, Bendaña YR, Bradley RK, Chao S, Kosiol C, Goldman N, Holmes I - BMC Bioinformatics (2006)

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

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioengineering, University of California, Berkeley CA, USA. petek@accesscom.com

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.

Show MeSH
Estimated codon mutation rate matrix for the codon model benchmark (see Results and Discussion). These rates were estimated by xrate from simulated data, generated using a mechanistic rate model (see figure 8).
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Figure 9: Estimated codon mutation rate matrix for the codon model benchmark (see Results and Discussion). These rates were estimated by xrate from simulated data, generated using a mechanistic rate model (see figure 8).

Mentions: xrate is able to recover M0 well from this 'artifical' Pandit database. The true rates used in the simulation are shown (see figure 8). These may be compared with the recovered rates (see figure 9).


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

Klosterman PS, Uzilov AV, Bendaña YR, Bradley RK, Chao S, Kosiol C, Goldman N, Holmes I - BMC Bioinformatics (2006)

Estimated codon mutation rate matrix for the codon model benchmark (see Results and Discussion). These rates were estimated by xrate from simulated data, generated using a mechanistic rate model (see figure 8).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1622757&req=5

Figure 9: Estimated codon mutation rate matrix for the codon model benchmark (see Results and Discussion). These rates were estimated by xrate from simulated data, generated using a mechanistic rate model (see figure 8).
Mentions: xrate is able to recover M0 well from this 'artifical' Pandit database. The true rates used in the simulation are shown (see figure 8). These may be compared with the recovered rates (see figure 9).

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

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioengineering, University of California, Berkeley CA, USA. petek@accesscom.com

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.

Show MeSH