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      BEAST 2: A Software Platform for Bayesian Evolutionary Analysis


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          We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. 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. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.

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          Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.

          A new statistical method for estimating divergence dates of species from DNA sequence data by a molecular clock approach is developed. This method takes into account effectively the information contained in a set of DNA sequence data. The molecular clock of mitochondrial DNA (mtDNA) was calibrated by setting the date of divergence between primates and ungulates at the Cretaceous-Tertiary boundary (65 million years ago), when the extinction of dinosaurs occurred. A generalized least-squares method was applied in fitting a model to mtDNA sequence data, and the clock gave dates of 92.3 +/- 11.7, 13.3 +/- 1.5, 10.9 +/- 1.2, 3.7 +/- 0.6, and 2.7 +/- 0.6 million years ago (where the second of each pair of numbers is the standard deviation) for the separation of mouse, gibbon, orangutan, gorilla, and chimpanzee, respectively, from the line leading to humans. Although there is some uncertainty in the clock, this dating may pose a problem for the widely believed hypothesis that the pipedal creature Australopithecus afarensis, which lived some 3.7 million years ago at Laetoli in Tanzania and at Hadar in Ethiopia, was ancestral to man and evolved after the human-ape splitting. Another likelier possibility is that mtDNA was transferred through hybridization between a proto-human and a proto-chimpanzee after the former had developed bipedalism.
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            Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach.

            A maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes is developed. The method uses a Markov chain Monte Carlo approach to investigate possible genealogies with branch lengths and with migration events. Properties of the new method are shown by using simulated data from a four-population n-island model and a source-sink population model. Our estimation method as coded in migrate is tested against genetree; both programs deliver a very similar likelihood surface. The algorithm converges to the estimates fairly quickly, even when the Markov chain is started from unfavorable parameters. The method was used to estimate gene flow in the Nile valley by using mtDNA data from three human populations.
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              Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics.

              Kingman's coalescent process opens the door for estimation of population genetics model parameters from molecular sequences. One paramount parameter of interest is the effective population size. Temporal variation of this quantity characterizes the demographic history of a population. Because researchers are rarely able to choose a priori a deterministic model describing effective population size dynamics for data at hand, nonparametric curve-fitting methods based on multiple change-point (MCP) models have been developed. We propose an alternative to change-point modeling that exploits Gaussian Markov random fields to achieve temporal smoothing of the effective population size in a Bayesian framework. The main advantage of our approach is that, in contrast to MCP models, the explicit temporal smoothing does not require strong prior decisions. To approximate the posterior distribution of the population dynamics, we use efficient, fast mixing Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. In a simulation study, we demonstrate that the proposed temporal smoothing method, named Bayesian skyride, successfully recovers "true" population size trajectories in all simulation scenarios and competes well with the MCP approaches without evoking strong prior assumptions. We apply our Bayesian skyride method to 2 real data sets. We analyze sequences of hepatitis C virus contemporaneously sampled in Egypt, reproducing all key known aspects of the viral population dynamics. Next, we estimate the demographic histories of human influenza A hemagglutinin sequences, serially sampled throughout 3 flu seasons.

                Author and article information

                Role: Editor
                PLoS Comput Biol
                PLoS Comput. Biol
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                April 2014
                10 April 2014
                : 10
                : 4
                : e1003537
                [1 ]Computational Evolution Group, Department of Computer Science, University of Auckland, Auckland, New Zealand
                [2 ]Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
                [3 ]Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand
                [4 ]Departments of Biomathematics and Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America
                [5 ]Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, California, United States of America
                [6 ]Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom
                [7 ]Allan Wilson Centre for Molecular Ecology and Evolution, University of Auckland, Auckland, New Zealand
                UCSD, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Analyzed the data: DK TV CHW. Wrote the paper: RB JH DK TV CHW MAS AR AJD. Developed software: RB JH DK TV CHW DX MAS AR AJD.

                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                : 2 December 2013
                : 20 January 2014
                Page count
                Pages: 6
                AJD, RB and JH were supported by a Rutherford Discovery Fellowship ( http://www.royalsociety.org.nz/programmes/funds/rutherford-discovery/) from the Royal Society of New Zealand awarded to AJD. DK and CHW were supported by Marsden grant UOA0809 from the Royal Society of New Zealand ( http://www.royalsociety.org.nz/programmes/funds/marsden/awards/award-2008-2/). MAS was supported by National Institutes of Health ( http://www.nih.gov) grant R01 HG006139 and National Science Foundation ( http://www.nsf.gov) grants DMS-0856099 and DMS-1264153. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Biology and Life Sciences
                Computational Biology
                Evolutionary Modeling
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Sequence Analysis

                Quantitative & Systems biology
                Quantitative & Systems biology


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