![]() ![]() This is achieved by explicitly modeling the rate of molecular evolution on each branch in the tree. Possibly the most distinguishing feature of BEAST is its firm focus on calibrated phylogenies and genealogies, that is, rooted trees incorporating a time-scale. For our purposes the target distribution is the posterior distribution of a set of evolutionary parameters given a set of molecular sequences. MCMC is a stochastic algorithm that produces sample-based estimates of a target distribution of choice. Like these software packages, the core algorithm implemented in BEAST is Metropolis-Hastings MCMC. Taken together with progress in phylogenetics and coalescent-based population genetics, Bayesian MCMC has been applied to most of the evolutionary questions commonly asked of molecular data.īEAST can be compared to a number of other software packages with similar goals, such as MrBayes, which currently focuses on phylogenetic inference and Batwing which focuses predominantly on coalescent-based population genetics of microsatellites. Most recently, Bayesian MCMC has also been applied to a central problem in evolutionary bioinformatics: the co-estimation of phylogeny and sequence alignment. Like phylogenetic analysis, these also require a gene tree in the underlying model, although in this setting, the sequences represent different individuals from the same species, rather than from different species. In addition to phylogenetic inference, a number of researchers have recently developed Bayesian MCMC software for coalescent-based estimation of demographic parameters from genetic data. Secondly, there is an often erroneous perception that Bayesian estimation is "faster" than heuristic optimization based on a maximum likelihood criterion. Firstly, Bayesian methods allow the relatively straightforward implementation of extremely complex evolutionary models. This enthusiasm can be attributed to a number of factors. Bayesian Markov chain Monte Carlo (MCMC) has already been enthusiastically embraced as the state-of-the-art method for phylogenetic reconstruction, largely driven by the rapid and widespread adoption of MrBayes. BEAST is a Bayesian statistical framework and thus provides a role for prior knowledge in combination with the information provided by the data. ![]() The BEAST software package is an ambitious attempt to provide a general framework for parameter estimation and hypothesis testing of evolutionary models from molecular sequence data. Similarly it is evident that to produce models that accurately describe molecular sequence variation an evolutionary perspective is required. It is now widely accepted that most questions regarding molecular sequences are statistical in nature and should be framed in terms of parameter estimation and hypothesis testing. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.Įvolution and statistics are two common themes that pervade the modern analysis of molecular sequence variation. ConclusionīEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. BEAST source code is object-oriented, modular in design and freely available at under the GNU LGPL license. ![]() 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. ResultsīEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. 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. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. The evolutionary analysis of molecular sequence variation is a statistical enterprise.
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