87
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe 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.

          Author summary

          Bayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

          Related collections

          Most cited references106

          • Record: found
          • Abstract: not found
          • Article: not found

          A Contribution to the Mathematical Theory of Epidemics

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            BEAST 2: A Software Platform for Bayesian Evolutionary Analysis

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10

              Abstract 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.
                Bookmark

                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Software
                Role: Software
                Role: Software
                Role: InvestigationRole: SoftwareRole: ValidationRole: Writing – review & editing
                Role: Software
                Role: SoftwareRole: Writing – review & editing
                Role: Software
                Role: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: SoftwareRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: VisualizationRole: Writing – original draft
                Role: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: Software
                Role: Conceptualization
                Role: Software
                Role: Software
                Role: Conceptualization
                Role: Software
                Role: Software
                Role: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                April 2019
                8 April 2019
                : 15
                : 4
                : e1006650
                Affiliations
                [1 ] Centre of Computational Evolution, University of Auckland, Auckland, New Zealand
                [2 ] Max Planck Institute for the Science of Human History, Jena, Germany
                [3 ] ETH Zürich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland
                [4 ] Swiss Institute of Bioinformatics, Lausanne, Switzerland
                [5 ] Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Victoria, Australia
                [6 ] ithree institute, University of Technology Sydney, Sydney, Australia
                [7 ] Department of Biochemistry, University of Otago, Dunedin 9016, New Zealand
                [8 ] Independent researcher, Auckland, New Zealand
                [9 ] Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, SE 405 30 Göteborg, Sweden
                [10 ] European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridgeshire, UK
                [11 ] Department of Environmental Sciences, University of Basel, 4051 Basel, Switzerland
                [12 ] Department of Computer Science, Rice University, Houston, TX 77005-1892, USA
                [13 ] Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK
                [14 ] Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, EH9 3FL UK
                [15 ] Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA
                [16 ] Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, W2 1PG, UK
                [17 ] Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
                [18 ] Department of Statistics, University of Oxford, OX1 3LB, UK
                [19 ] Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, China
                Johns Hopkins University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-6765-3813
                http://orcid.org/0000-0002-5220-5468
                http://orcid.org/0000-0002-2863-0907
                http://orcid.org/0000-0001-8153-9822
                http://orcid.org/0000-0002-2403-7997
                http://orcid.org/0000-0002-9686-5871
                http://orcid.org/0000-0002-5657-018X
                http://orcid.org/0000-0002-1776-8564
                http://orcid.org/0000-0001-6204-7208
                http://orcid.org/0000-0003-1589-6885
                http://orcid.org/0000-0003-2595-3062
                http://orcid.org/0000-0001-9818-479X
                http://orcid.org/0000-0001-6009-5273
                http://orcid.org/0000-0001-6431-535X
                http://orcid.org/0000-0003-4454-2576
                Article
                PCOMPBIOL-D-18-01929
                10.1371/journal.pcbi.1006650
                6472827
                30958812
                babe0dc6-7199-43cd-8f55-0fa4f90d915b
                © 2019 Bouckaert et al

                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.

                History
                : 14 November 2018
                : 4 February 2019
                Page count
                Figures: 6, Tables: 1, Pages: 28
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100009193, Marsden Fund;
                Award ID: 16-UOA-277
                Award Recipient :
                AJD would like to acknowledge support from a Royal Society of New Zealand Marsden award (#UOA1611; 16-UOA-277). LdP would like to acknowledge support from the European Research Council under the Seventh Framework Programme of the European Commission (PATHPHYLODYN: grant agreement number 614725). IS would like to acknowledge support from the NIH MIDAS U01 GM110749 grant. NFM and TS are funded in part by the Swiss National Science foundation (SNF; grant number CR32I3 166258). TS, JB-S, LdP, TGV, and CZ were supported in part by the European Research Council under the Seventh Framework Programme of the European Commission (PhyPD: grant agreement number 335529). DK would like to acknowledge support from the Max Planck Society. NDM was supported by EMBL. MM acknowledges support from the Swiss National Science Foundation (SNP; grant number PBBSP3-138680). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-04-18
                The XML file and log files used for the bModelTest analyses shown in Fig 2 are available from http://www.doi.org/10.5281/zenodo.1475369. The XML file, log file, MCC tree and post-processing scripts for the bdsky analyses shown in Fig 3 are available from http://www.doi.org/10.5281/zenodo.1476124. The alignments, XML files, log files and post processing scripts for the AIM analysis shown in Fig 5 can be found at https://github.com/nicfel/Neolamprologus. The XML files and a script to generate the TreeModelAdequacy analyses shown in Fig 6 are available from http://doi.org/10.5281/zenodo.1473852.

                Quantitative & Systems biology
                Quantitative & Systems biology

                Comments

                Comment on this article