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      Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10


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          The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.

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          Most cited references14

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          Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

          Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
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            Simulating normalizing constants: from importance sampling to bridge sampling to path sampling

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              SpreaD3: Interactive Visualization of Spatiotemporal History and Trait Evolutionary Processes.

              Model-based phylogenetic reconstructions increasingly consider spatial or phenotypic traits in conjunction with sequence data to study evolutionary processes. Alongside parameter estimation, visualization of ancestral reconstructions represents an integral part of these analyses. Here, we present a complete overhaul of the spatial phylogenetic reconstruction of evolutionary dynamics software, now called SpreaD3 to emphasize the use of data-driven documents, as an analysis and visualization package that primarily complements Bayesian inference in BEAST (http://beast.bio.ed.ac.uk, last accessed 9 May 2016). The integration of JavaScript D3 libraries (www.d3.org, last accessed 9 May 2016) offers novel interactive web-based visualization capacities that are not restricted to spatial traits and extend to any discrete or continuously valued trait for any organism of interest.

                Author and article information

                Virus Evol
                Virus Evol
                Virus Evolution
                Oxford University Press
                January 2018
                08 June 2018
                08 June 2018
                : 4
                : 1
                [1 ]Department of Biomathematics, David Geffen School of MedicineUniversity of California, Los Angeles, 621 Charles E. Young Dr., South, Los Angeles, CA, 90095 USA
                [2 ]Department of Biostatistics, Fielding School of Public HealthUniversity of California, Los Angeles, 650 Charles E, Young Dr., South, Los Angeles, CA, 90095 USA
                [3 ]Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Dr., South, Los Angeles, CA, 90095 USA
                [4 ]Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
                [5 ]Center for Bioinformatics and Computational Biology, University of Maryland, College Park, 125 Biomolecular Science Bldg #296, College Park, MD 20742 USA
                [6 ]Department of Computer Science, University of Auckland, 303/38 Princes St., Auckland, 1010 NZ
                [7 ]Centre for Computational Evolution, University of Auckland, 303/38 Princes St., Auckland, 1010 NZ
                [8 ]Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, EH9 3FL UK
                Author notes
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 5
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 206298/Z/17/Z
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: DMS 1264153
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01 HG006139
                Award ID: R01 AI107034
                Award ID: U19 AI135995
                Funded by: Bijzonder Onderzoeksfonds 10.13039/501100007229
                Award ID: OT/14/115

                phylogenetics,phylodynamics,bayesian inference,markov chain monte carlo


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