Höhna Sebastian, Landis Michael J, Heath Tracy A
Department of Integrative Biology, University of California, Berkeley, California.
Department of Statistics, University of California, Berkeley, California.
Curr Protoc Bioinformatics. 2017 May 2;57:6.16.1-6.16.34. doi: 10.1002/cpbi.22.
Bayesian phylogenetic inference aims to estimate the evolutionary relationships among different lineages (species, populations, gene families, viral strains, etc.) in a model-based statistical framework that uses the likelihood function for parameter estimates. In recent years, evolutionary models for Bayesian analysis have grown in number and complexity. RevBayes uses a probabilistic-graphical model framework and an interactive scripting language for model specification to accommodate and exploit model diversity and complexity within a single software package. In this unit we describe how to specify standard phylogenetic models and perform Bayesian phylogenetic analyses in RevBayes. The protocols focus on the basic analysis of inferring a phylogeny from single and multiple loci, describe a hypothesis-testing approach, and point to advanced topics. Thus, this unit is a starting point to illustrate the power and potential of Bayesian inference under complex phylogenetic models in RevBayes. © 2017 by John Wiley & Sons, Inc.
贝叶斯系统发育推断旨在在基于模型的统计框架中估计不同谱系（物种、种群、基因家族、病毒株等）之间的进化关系，该框架使用似然函数进行参数估计。近年来，用于贝叶斯分析的进化模型在数量和复杂性上都有所增加。RevBayes使用概率图模型框架和交互式脚本语言进行模型规范，以在单个软件包中适应和利用模型的多样性和复杂性。在本单元中，我们将描述如何在RevBayes中指定标准的系统发育模型并进行贝叶斯系统发育分析。这些方案侧重于从单个和多个基因座推断系统发育的基本分析，描述一种假设检验方法，并指出一些高级主题。因此，本单元是一个起点，用于说明在RevBayes中复杂系统发育模型下贝叶斯推断的能力和潜力。© 2017约翰威立父子公司。
