沉闷科学的掘墓人分享 http://blog.sciencenet.cn/u/Bearjazz

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每日翻译20190712

已有 275 次阅读 2019-7-12 07:06 |个人分类:翻译作品|系统分类:科研笔记| BEAST, 贝叶斯, 程序, 功能

#编者信息

熊荣川

明湖实验室

xiongrongchuan@126.com

http://blog.sciencenet.cn/u/Bearjazz


Here, we present a major new version of   the molecular evolutionary software package Bayesian Evolutionary Analysis by   Sampling Trees (BEAST), updated to version 1.7, and representing a signifcant   software advance over that previously described (Drummond and Rambaut 2007).   Alongside the primary analysis engine in BEAST, this package also includes a   suite of utilities for specifying the analysis design, processing output files,   and summarizing and visualizing the results. Taken together, these programs   enable Bayesian inference of molecular sequences with an emphasis on time-structured   evolutionary models including phylodynamic models, divergence time estimates,   multiloci demographic models, gene–/species–tree inference, a range of spatial   phylogeographic analyses, and discrete and continuous trait evolution.   Implementing Markov chain Monte Carlo (MCMC) algorithms to perform these   inferences, the package is intended and used for rigorous statistical   inference and hypothesis testing of evolutionary models with joint inference   of phylogeny. It is also possible to constrain portions of the phylogenetic   model space to known values, including the tree topology, and perform   conditional inference if required.

 

在这里,我们提出了BEAST一个重要更新版本(1.7版),相较先前介绍的版本(Drummond and Rambaut 2007)它代表了一个显著的软件进步。除了BEAST中的主要分析核心要件外,此软件包还包括一套用于指定分析设置、处理输出文件以及汇总和可视化结果的实用程序。综合起来,这些程序使分子序列的贝叶斯推断成为可能,重点是时间结构进化模型,包括系统动力学模型、分化时间估计、多基因座种群模型、基因/物种树推断、空间分布范围系统地理分析,以及离散和连续的特征进化。利用马尔可夫蒙特卡罗(MCMC)算法实现这些推断,并将其应用于系统发育联合推论的进化模型的严格统计推断和假设检验。还可以将系统发育模型空间的一部分限制为已知值,包括树拓扑结构,并在需要时执行条件推断。

Drummond A J , Suchard M A , Xie D , et   al. Bayesian Phylogenetics with BEAUti and the BEAST 1.7[J]. Molecular   Biology and Evolution, 2012, 29(8):1969-1973.




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