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, 36 (11), 2620-2628
[Online ahead of print]

Bayesian Estimation of Past Population Dynamics in BEAST 1.10 Using the Skygrid Coalescent Model

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Bayesian Estimation of Past Population Dynamics in BEAST 1.10 Using the Skygrid Coalescent Model

Verity Hill et al. Mol Biol Evol.

Abstract

Inferring past population dynamics over time from heterochronous molecular sequence data is often achieved using the Bayesian Skygrid model, a non-parametric coalescent model that estimates the effective population size over time. Available in BEAST, a cross-platform program for Bayesian analysis of molecular sequences using Markov chain Monte Carlo, this coalescent model is often estimated in conjunction with a molecular clock model to produce time-stamped phylogenetic trees. We here provide a practical guide to using BEAST and its accompanying applications for the purpose of drawing inference under these models. We focus on best practices, potential pitfalls and recommendations that can be generalized to other software packages for Bayesian inference. This protocol shows how to use TempEst, BEAUti and BEAST 1.10 (http://beast.community/), LogCombiner as well as Tracer in a complete workflow.

Keywords: BEAST; Bayesian phylogenetics; Skygrid; TempEst; Tracer; coalescent; pathogen phylodynamics.

Figures

<sc>Fig</sc>. 1.
Fig. 1.
Conceptual representation of various nonparametric coalescent models on a phylogeny of n =7 heterochronous sequences. The classic skyline (Pybus et al. 2000) and its extension, the generalized skyline (Strimmer and Pybus 2001), were the first among a still increasing collection of nonparametric coalescent models. Initially estimated using maximum likelihood inference on a fixed phylogeny, these models have been extended for use in Bayesian framework while accommodating phylogenetic uncertainty (Drummond et al. 2005). Recent developments include the Skyride (Minin et al. 2008), the Skygrid (Gill et al. 2013), and its extension to incorporate covariates (Gill et al. 2016), which all employ smoothing priors.
<sc>Fig</sc>. 2.
Fig. 2.
Using TempEst to determine whether our data set has sufficiently strong temporal signal and to identify outliers. (a) Dialog box showing how to extract sampling times from the sequence labels, (b) root-to-tip plot showing regression of genetic distance against time, and (c) residuals plot. In (b) and (c), four outliers can be identified and are indicated by the red box (for illustration purposes only, i.e. not a feature of TempEst).
<sc>Fig</sc>. 3.
Fig. 3.
Setting up the Skygrid coalescent model in BEAUti. (a) Shows the data partitions we have imported using two different FASTA files for coding and intergenic regions. (b) Shows the “Trees” panel for setting up a Skygrid coalescent model to infer past population dynamics.
<sc>Fig</sc>. 4.
Fig. 4.
(a) The Tracer panel on the left shows the parameters logged during the run. Note that both runs are selected and as such, the panel on the right shows both traces in different colors. (b) Shows the options for the Skygrid reconstruction based on this analysis. (c) Shows past population dynamics visualized using the Skygrid model. The shaded portion is the 95% Bayesian credibility interval (obtained by clicking the “Solid interval” checkbox in the lower left-hand corner of the visualization window), and the solid line is the posterior median. The vertical lines represent the best estimate for the time of the root of the tree, and the upper highest posterior density, respectively.

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