Bayesian Phylogenetics with BEAUti and the BEAST 1.7

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Abstract

Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.uk

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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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SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics

Filip Bielejec, Andrew Rambaut, Marc Suchard … (2011)

Summary: SPREAD is a user-friendly, cross-platform application to analyze and visualize Bayesian phylogeographic reconstructions incorporating spatial–temporal diffusion. The software maps phylogenies annotated with both discrete and continuous spatial information and can export high-dimensional posterior summaries to keyhole markup language (KML) for animation of the spatial diffusion through time in virtual globe software. In addition, SPREAD implements Bayes factor calculation to evaluate the support for hypotheses of historical diffusion among pairs of discrete locations based on Bayesian stochastic search variable selection estimates. SPREAD takes advantage of multicore architectures to process large joint posterior distributions of phylogenies and their spatial diffusion and produces visualizations as compelling and interpretable statistical summaries for the different spatial projections. Availability: SPREAD is licensed under the GNU Lesser GPL and its source code is freely available as a GitHub repository: https://github.com/phylogeography/SPREAD Contact: filip.bielejec@rega.kuleuven.be

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Sampling-through-time in birth-death trees.

Tanja Stadler (2010)

I consider the constant rate birth-death process with incomplete sampling. I calculate the density of a given tree with sampled extant and extinct individuals. This density is essential for analyzing datasets which are sampled through time. Such datasets are common in virus epidemiology as viruses in infected individuals are sampled through time. Further, such datasets appear in phylogenetics when extant and extinct species data is available. I show how the derived tree density can be used (i) as a tree prior in a Bayesian method to reconstruct the evolutionary past of the sequence data on a calender-timescale, (ii) to infer the birth- and death-rates for a reconstructed evolutionary tree, and (iii) for simulating trees with a given number of sampled extant and extinct individuals which is essential for testing evolutionary hypotheses for the considered datasets. Copyright © 2010 Elsevier Ltd. All rights reserved.

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Author and article information

Journal

Journal ID (nlm-ta): Mol Biol Evol

Journal ID (iso-abbrev): Mol. Biol. Evol

Journal ID (publisher-id): molbev

Journal ID (hwp): molbiolevol

Title: Molecular Biology and Evolution

Publisher: Oxford University Press

ISSN (Print): 0737-4038

ISSN (Electronic): 1537-1719

Publication date (Print): August 2012

Publication date (Electronic): 25 February 2012

Publication date PMC-release: 25 February 2012

Volume: 29

Issue: 8

Pages: 1969-1973

Affiliations

[ ¹ ]Department of Computer Science, University of Auckland, Auckland, New Zealand

[ ² ]Allan Wilson Centre for Molecular Ecology and Evolution, University of Auckland, Auckland, New Zealand

[ ³ ]Departments of Biomathematics and Human Genetics, David Geffen School of Medicine, University of California, Los Angeles

[ ⁴ ]Department of Biostatistics, School of Public Health, University of California, Los Angeles

[ ⁵ ]Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom

Author notes

[* ]Corresponding author: E-mail: alexei@ 123456cs.auckland.ac.nz ; msuchard@ 123456ucla.edu ; a.rambaut@ 123456ed.ac.uk .

Associate editor: Sudhir Kumar

Article

Publisher ID: mss075

DOI: 10.1093/molbev/mss075

PMC ID: 3408070

PubMed ID: 22367748

SO-VID: 26e5670d-2f4e-40b5-8322-fd491a009de9

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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ScienceOpen disciplines: Molecular biology

Keywords: evolution,bayesian phylogenetics,coalescent theory,phylogenetics,molecular evolution

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ScienceOpen disciplines: Molecular biology

Keywords: evolution, bayesian phylogenetics, coalescent theory, phylogenetics, molecular evolution

Bayesian Phylogenetics with BEAUti and the BEAST 1.7

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Most cited references 17

Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics.

SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics

Sampling-through-time in birth-death trees.

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