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BEAST: Bayesian evolutionary analysis by sampling trees.

Drummond AJ, Rambaut A - BMC Evol. Biol. (2007)

Bottom Line: Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree.A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented.BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Institute, University of Auckland, Auckland, New Zealand. alexei@cs.auckland.ac.nz

ABSTRACT

Background: The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented.

Results: BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license.

Conclusion: BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.

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Related in: MedlinePlus

Consensus tree of 17 dengue 4 env sequences The consensus tree for the example analysis of Dengue 4 sequences under the strict clock analysis with a GTR + CP substitution model. Each internal node is labeled with the posterior probability of monophyly of the corresponding clade. The gray bars illustrated the extent of the 95% highest posterior density intervals for each divergence time. The scale is in years.
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Figure 1: Consensus tree of 17 dengue 4 env sequences The consensus tree for the example analysis of Dengue 4 sequences under the strict clock analysis with a GTR + CP substitution model. Each internal node is labeled with the posterior probability of monophyly of the corresponding clade. The gray bars illustrated the extent of the 95% highest posterior density intervals for each divergence time. The scale is in years.

Mentions: As has been previously suggested to be generally the case for protein-coding sequences [39], we found that the codon-position-specific model of rate heterogeneity among sites has a substantially superior fit to the data than the GTR + Γ + I model (see Table 1), and also supports a different consensus tree topology (see Figure 1). However we find little difference (log BF = 0.8) between the two models of rate variation among branches, indicating that this particular data can be treated as clock-like, as has been previously suggested [30]. Under the strict clock model with codon-position rate heterogeneity and a constant-size coalescent tree prior the estimated date of the root of the phylogeny is 1924 (95% highest posterior density (HPD): 1911 – 1936) and the estimated rate of substitution for this serotype was estimated to be 8.38 × 10-4 (95% HPD: 6.40 × 10-4 – 1.05 × 10-3).


BEAST: Bayesian evolutionary analysis by sampling trees.

Drummond AJ, Rambaut A - BMC Evol. Biol. (2007)

Consensus tree of 17 dengue 4 env sequences The consensus tree for the example analysis of Dengue 4 sequences under the strict clock analysis with a GTR + CP substitution model. Each internal node is labeled with the posterior probability of monophyly of the corresponding clade. The gray bars illustrated the extent of the 95% highest posterior density intervals for each divergence time. The scale is in years.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2247476&req=5

Figure 1: Consensus tree of 17 dengue 4 env sequences The consensus tree for the example analysis of Dengue 4 sequences under the strict clock analysis with a GTR + CP substitution model. Each internal node is labeled with the posterior probability of monophyly of the corresponding clade. The gray bars illustrated the extent of the 95% highest posterior density intervals for each divergence time. The scale is in years.
Mentions: As has been previously suggested to be generally the case for protein-coding sequences [39], we found that the codon-position-specific model of rate heterogeneity among sites has a substantially superior fit to the data than the GTR + Γ + I model (see Table 1), and also supports a different consensus tree topology (see Figure 1). However we find little difference (log BF = 0.8) between the two models of rate variation among branches, indicating that this particular data can be treated as clock-like, as has been previously suggested [30]. Under the strict clock model with codon-position rate heterogeneity and a constant-size coalescent tree prior the estimated date of the root of the phylogeny is 1924 (95% highest posterior density (HPD): 1911 – 1936) and the estimated rate of substitution for this serotype was estimated to be 8.38 × 10-4 (95% HPD: 6.40 × 10-4 – 1.05 × 10-3).

Bottom Line: Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree.A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented.BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Institute, University of Auckland, Auckland, New Zealand. alexei@cs.auckland.ac.nz

ABSTRACT

Background: The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented.

Results: BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license.

Conclusion: BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.

Show MeSH
Related in: MedlinePlus