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      Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

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          Abstract

          Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.

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          AWTY (are we there yet?): a system for graphical exploration of MCMC convergence in Bayesian phylogenetics.

          Johan Nylander, James C Wilgenbusch, Dan L Warren … (2008)
          A key element to a successful Markov chain Monte Carlo (MCMC) inference is the programming and run performance of the Markov chain. However, the explicit use of quality assessments of the MCMC simulations-convergence diagnostics-in phylogenetics is still uncommon. Here, we present a simple tool that uses the output from MCMC simulations and visualizes a number of properties of primary interest in a Bayesian phylogenetic analysis, such as convergence rates of posterior split probabilities and branch lengths. Graphical exploration of the output from phylogenetic MCMC simulations gives intuitive and often crucial information on the success and reliability of the analysis. The tool presented here complements convergence diagnostics already available in other software packages primarily designed for other applications of MCMC. Importantly, the common practice of using trace-plots of a single parameter or summary statistic, such as the likelihood score of sampled trees, can be misleading for assessing the success of a phylogenetic MCMC simulation. The program is available as source under the GNU General Public License and as a web application at http://ceb.scs.fsu.edu/awty.
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            Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics.

            Vladimir N. Minin, Erik W Bloomquist, Marc Suchard (2008)
            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.
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              Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data.

              Alexei J. Drummond, Geoff Nicholls, Allen Rodrigo … (2002)
              Molecular sequences obtained at different sampling times from populations of rapidly evolving pathogens and from ancient subfossil and fossil sources are increasingly available with modern sequencing technology. Here, we present a Bayesian statistical inference approach to the joint estimation of mutation rate and population size that incorporates the uncertainty in the genealogy of such temporally spaced sequences by using Markov chain Monte Carlo (MCMC) integration. The Kingman coalescent model is used to describe the time structure of the ancestral tree. We recover information about the unknown true ancestral coalescent tree, population size, and the overall mutation rate from temporally spaced data, that is, from nucleotide sequences gathered at different times, from different individuals, in an evolving haploid population. We briefly discuss the methodological implications and show what can be inferred, in various practically relevant states of prior knowledge. We develop extensions for exponentially growing population size and joint estimation of substitution model parameters. We illustrate some of the important features of this approach on a genealogy of HIV-1 envelope (env) partial sequences.
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                Author and article information

                Contributors
                Edward Susko: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Syst Biol
                Journal ID (iso-abbrev): Syst. Biol
                Journal ID (publisher-id): sysbio
                Title: Systematic Biology
                Publisher: Oxford University Press
                ISSN (Print): 1063-5157
                ISSN (Electronic): 1076-836X
                Publication date (Print): September 2018
                Publication date (Electronic): 27 April 2018
                Publication date PMC-release: 27 April 2018
                Volume: 67
                Issue: 5
                Pages: 901-904
                Affiliations
                [1 ]Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, King’s Buildings, Edinburgh, EH9 3FL, UK
                [2 ]Department of Computer Science, University of Auckland, 303/38 Princes St, Auckland, 1010, NZ
                [3 ]Centre for Computational Evolution, University of Auckland, 303/38 Princes St, Auckland, 1010, NZ
                [4 ]Department of Microbiology and Immunology, Rega Institute, KU Leuven - University of Leuven, Herestraat 49, 3000 Leuven, Belgium
                [5 ]Department of Human Genetics, University of California, Los Angeles, 695 Charles E. Young Dr., Los Angeles, CA 90095, USA
                [6 ]Department of Biostatistics, University of California, Los Angeles, 650 Charles E. Young Dr., Los Angeles, CA 90095, USA
                Author notes
                Correspondence to be sent to: Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, King’s Buildings, Edinburgh, EH9 3FL, UK; Email: a.rambaut@ 123456ed.ac.uk .
                Article
                Publisher ID: syy032
                DOI: 10.1093/sysbio/syy032
                PMC ID: 6101584
                PubMed ID: 29718447
                SO-VID: 58834aef-a682-4dbf-896f-0680f4e295c4
                Copyright © © The Author(s) 2018. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.
                License:

                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. For Permissions, please email: journals.permissions@oup.com

                History
                Date received : 13 February 2018
                Date revision received : 19 April 2018
                Date accepted : 20 April 2018
                Page count
                Pages: 4
                Funding
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: 1264153
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01 AI107034
                Award ID: U19 AI135995
                Categories
                Subject: Software for Systematics and Evolution

                ScienceOpen disciplines: Animal science & Zoology
                Keywords: bayesian inference,markov chain monte carlo,phylogenetics,visualization
                Data availability:
                ScienceOpen disciplines: Animal science & Zoology
                Keywords: bayesian inference, markov chain monte carlo, phylogenetics, visualization

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