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MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

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      Abstract

      Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site dN/dS rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.

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      Most cited references 34

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      MrBayes 3: Bayesian phylogenetic inference under mixed models.

      MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types-e.g. morphological, nucleotide, and protein-and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters.
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        jModelTest: phylogenetic model averaging.

        jModelTest is a new program for the statistical selection of models of nucleotide substitution based on "Phyml" (Guindon and Gascuel 2003. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 52:696-704.). It implements 5 different selection strategies, including "hierarchical and dynamical likelihood ratio tests," the "Akaike information criterion," the "Bayesian information criterion," and a "decision-theoretic performance-based" approach. This program also calculates the relative importance and model-averaged estimates of substitution parameters, including a model-averaged estimate of the phylogeny. jModelTest is written in Java and runs under Mac OSX, Windows, and Unix systems with a Java Runtime Environment installed. The program, including documentation, can be freely downloaded from the software section at http://darwin.uvigo.es.
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          MRBAYES: Bayesian inference of phylogenetic trees.

          The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html.
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            Author and article information

            Affiliations
            [1 ]Department of Biodiversity Informatics, Swedish Museum of Natural History, SE-10405 Stockholm, Sweden
            [2 ]Department of Scientific Computing, Florida State University, FL 32306, USA
            [3 ]Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
            [4 ]Genome Center, University of California, Davis, CA 95616, USA
            [5 ]Department of Mathematics, Stockholm University, SE-10691 Stockholm, Sweden
            [6 ]Departments of Statistics and Botany, University of Wisconsin, Madison, WI 53706, USA
            [7 ]Departments of Agriculture and Natural Resources, Delaware State University, Dover, DE 19901, USA
            [8 ]Departments of Biomathematics, Biostatistics and Human Genetics, University of California, Los Angeles, CA 90095, USA
            [9 ]Department of Integrative Biology, University of California, Berkeley, CA 94720, USA
            Author notes
            [* ]Correspondence to be sent to: Department of Biodiversity Informatics, Swedish Museum of Natural History, SE-10405 Stockholm, Sweden; E-mail: fredrik.ronquist@ 123456nrm.se .

            Associate Editor: David Posada

            Journal
            Syst Biol
            Syst. Biol
            sysbio
            sysbio
            Systematic Biology
            Oxford University Press
            1063-5157
            1076-836X
            May 2012
            22 February 2012
            22 February 2012
            : 61
            : 3
            : 539-542
            3329765
            22357727
            10.1093/sysbio/sys029
            © The Author(s) 2012. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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            Pages: 4
            Categories
            Software for Systematics and Evolution

            Animal science & Zoology

            model averaging, bayesian inference, bayes factor, model choice, mcmc

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