Hill Verity, Baele Guy
Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.
Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
Mol Biol Evol. 2019 Nov 1;36(11):2620-2628. doi: 10.1093/molbev/msz172.
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.
从异时分子序列数据推断过去的种群动态随时间的变化,通常使用贝叶斯Skygrid模型来实现,这是一种非参数合并模型,可估计随时间变化的有效种群大小。该合并模型可在BEAST中使用,BEAST是一个用于通过马尔可夫链蒙特卡罗方法对分子序列进行贝叶斯分析的跨平台程序,它通常与分子钟模型结合估计,以生成带时间戳的系统发育树。我们在此提供一份实用指南,介绍如何使用BEAST及其配套应用程序,以便在这些模型下进行推断。我们关注的是最佳实践、潜在陷阱以及可推广到其他贝叶斯推断软件包的建议。本方案展示了如何在一个完整的工作流程中使用TempEst、BEAUti、BEAST 1.10(http://beast.community/;最后访问时间:2019年7月29日)、LogCombiner以及Tracer。