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bModelTest: Bayesian phylogenetic site model averaging and model comparison

BACKGROUND: Reconstructing phylogenies through Bayesian methods has many benefits, which include providing a mathematically sound framework, providing realistic estimates of uncertainty and being able to incorporate different sources of information based on formal principles. Bayesian phylogenetic a...

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Autores principales: Bouckaert, Remco R., Drummond, Alexei J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5294809/
https://www.ncbi.nlm.nih.gov/pubmed/28166715
http://dx.doi.org/10.1186/s12862-017-0890-6
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author Bouckaert, Remco R.
Drummond, Alexei J.
author_facet Bouckaert, Remco R.
Drummond, Alexei J.
author_sort Bouckaert, Remco R.
collection PubMed
description BACKGROUND: Reconstructing phylogenies through Bayesian methods has many benefits, which include providing a mathematically sound framework, providing realistic estimates of uncertainty and being able to incorporate different sources of information based on formal principles. Bayesian phylogenetic analyses are popular for interpreting nucleotide sequence data, however for such studies one needs to specify a site model and associated substitution model. Often, the parameters of the site model is of no interest and an ad-hoc or additional likelihood based analysis is used to select a single site model. RESULTS: bModelTest allows for a Bayesian approach to inferring and marginalizing site models in a phylogenetic analysis. It is based on trans-dimensional Markov chain Monte Carlo (MCMC) proposals that allow switching between substitution models as well as estimating the posterior probability for gamma-distributed rate heterogeneity, a proportion of invariable sites and unequal base frequencies. The model can be used with the full set of time-reversible models on nucleotides, but we also introduce and demonstrate the use of two subsets of time-reversible substitution models. CONCLUSION: With the new method the site model can be inferred (and marginalized) during the MCMC analysis and does not need to be pre-determined, as is now often the case in practice, by likelihood-based methods. The method is implemented in the bModelTest package of the popular BEAST 2 software, which is open source, licensed under the GNU Lesser General Public License and allows joint site model and tree inference under a wide range of models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12862-017-0890-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-52948092017-02-09 bModelTest: Bayesian phylogenetic site model averaging and model comparison Bouckaert, Remco R. Drummond, Alexei J. BMC Evol Biol Software BACKGROUND: Reconstructing phylogenies through Bayesian methods has many benefits, which include providing a mathematically sound framework, providing realistic estimates of uncertainty and being able to incorporate different sources of information based on formal principles. Bayesian phylogenetic analyses are popular for interpreting nucleotide sequence data, however for such studies one needs to specify a site model and associated substitution model. Often, the parameters of the site model is of no interest and an ad-hoc or additional likelihood based analysis is used to select a single site model. RESULTS: bModelTest allows for a Bayesian approach to inferring and marginalizing site models in a phylogenetic analysis. It is based on trans-dimensional Markov chain Monte Carlo (MCMC) proposals that allow switching between substitution models as well as estimating the posterior probability for gamma-distributed rate heterogeneity, a proportion of invariable sites and unequal base frequencies. The model can be used with the full set of time-reversible models on nucleotides, but we also introduce and demonstrate the use of two subsets of time-reversible substitution models. CONCLUSION: With the new method the site model can be inferred (and marginalized) during the MCMC analysis and does not need to be pre-determined, as is now often the case in practice, by likelihood-based methods. The method is implemented in the bModelTest package of the popular BEAST 2 software, which is open source, licensed under the GNU Lesser General Public License and allows joint site model and tree inference under a wide range of models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12862-017-0890-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-02-06 /pmc/articles/PMC5294809/ /pubmed/28166715 http://dx.doi.org/10.1186/s12862-017-0890-6 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Bouckaert, Remco R.
Drummond, Alexei J.
bModelTest: Bayesian phylogenetic site model averaging and model comparison
title bModelTest: Bayesian phylogenetic site model averaging and model comparison
title_full bModelTest: Bayesian phylogenetic site model averaging and model comparison
title_fullStr bModelTest: Bayesian phylogenetic site model averaging and model comparison
title_full_unstemmed bModelTest: Bayesian phylogenetic site model averaging and model comparison
title_short bModelTest: Bayesian phylogenetic site model averaging and model comparison
title_sort bmodeltest: bayesian phylogenetic site model averaging and model comparison
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5294809/
https://www.ncbi.nlm.nih.gov/pubmed/28166715
http://dx.doi.org/10.1186/s12862-017-0890-6
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