下载PDF
{"title":"Phylogenetic Inference Using RevBayes.","authors":"Sebastian Höhna, Michael J Landis, Tracy A Heath","doi":"10.1002/cpbi.22","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"57 ","pages":"6.16.1-6.16.34"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.22","citationCount":"26","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current protocols in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/cpbi.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 26
引用
批量引用
Abstract
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.
基于RevBayes的系统发育推断。
贝叶斯系统发育推断的目的是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方面都有所增长。RevBayes使用概率图形模型框架和交互式脚本语言进行模型规范,以适应和利用单个软件包中的模型多样性和复杂性。在本单元中,我们描述了如何在RevBayes中指定标准系统发育模型并执行贝叶斯系统发育分析。该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,描述了一种假设检验方法,并指出了高级主题。因此,本单元是一个起点,说明在RevBayes复杂系统发育模型下贝叶斯推理的力量和潜力。©2017 by John Wiley & Sons, Inc。
本文章由计算机程序翻译,如有差异,请以英文原文为准。