BEAST: Bayesian evolutionary analysis by sampling trees
TLDR
BEAST is a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree that 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.Abstract:
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. 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. 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.read more
Citations
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Journal ArticleDOI
Bayesian Phylogenetics with BEAUti and the BEAST 1.7
TL;DR: The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7 is presented, which implements a family of Markov chain Monte Carlo algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses.
Journal ArticleDOI
BEAST 2: A Software Platform for Bayesian Evolutionary Analysis
Remco R. Bouckaert,Joseph Heled,Denise Kühnert,Timothy G. Vaughan,Chieh-Hsi Wu,Dong Xie,Marc A. Suchard,Andrew Rambaut,Alexei J. Drummond +8 more
TL;DR: BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform.
Journal ArticleDOI
PartitionFinder: Combined Selection of Partitioning Schemes and Substitution Models for Phylogenetic Analyses
TL;DR: Two new objective methods for the combined selection of best-fit partitioning schemes and nucleotide substitution models are described and implemented in an open-source program, PartitionFinder, which it is hoped will encourage the objective selection of partitions and thus lead to improvements in phylogenetic analyses.
Journal ArticleDOI
Bayesian Inference of Species Trees from Multilocus Data
Joseph Heled,Alexei J. Drummond +1 more
TL;DR: It is demonstrated that both BEST and the new Bayesian Markov chain Monte Carlo method for the multispecies coalescent have much better estimation accuracy for species tree topology than concatenation, and the method outperforms BEST in divergence time and population size estimation.
Journal ArticleDOI
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.
Remco R. Bouckaert,Remco R. Bouckaert,Timothy G. Vaughan,Timothy G. Vaughan,Joëlle Barido-Sottani,Joëlle Barido-Sottani,Sebastián Duchêne,Mathieu Fourment,Alexandra Gavryushkina,Joseph Heled,Graham Jones,Denise Kühnert,Nicola De Maio,Michael Matschiner,Fábio K. Mendes,Nicola F. Müller,Nicola F. Müller,Huw A. Ogilvie,Louis du Plessis,Alex Popinga,Andrew Rambaut,David A. Rasmussen,Igor Siveroni,Marc A. Suchard,Chieh-Hsi Wu,Dong Xie,Chi Zhang,Tanja Stadler,Tanja Stadler,Alexei J. Drummond +29 more
TL;DR: A series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release are described.
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