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Bayesian Estimation of Past Population Dynamics in BEAST 1.10 Using the Skygrid Coalescent Model
Author(s) -
Verity Hill,
Guy Baele
Publication year - 2019
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/msz172
Subject(s) - coalescent theory , markov chain monte carlo , bayesian probability , biology , bayesian inference , population , inference , markov chain , hidden markov model , evolutionary biology , computer science , phylogenetic tree , statistics , machine learning , artificial intelligence , mathematics , genetics , demography , sociology , gene
Inferring past population dynamics over time from heterochronous molecular sequence data is often achieved using the Bayesian Skygrid model, a nonparametric 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/; last accessed July 29, 2019), LogCombiner as well as Tracer in a complete workflow.

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