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A total-evidence approach to dating with fossils, applied to the early radiation of the hymenoptera.基于化石的全证据年代测定方法及其在膜翅目早期辐射中的应用
Syst Biol. 2012 Dec 1;61(6):973-99. doi: 10.1093/sysbio/sys058. Epub 2012 Jun 20.
2
BEAGLE: an application programming interface and high-performance computing library for statistical phylogenetics.BEAGLE:一个用于统计系统发生学的应用程序编程接口和高性能计算库。
Syst Biol. 2012 Jan;61(1):170-3. doi: 10.1093/sysbio/syr100. Epub 2011 Oct 1.
3
Guided tree topology proposals for Bayesian phylogenetic inference.贝叶斯系统发育推断的引导树拓扑提议。
Syst Biol. 2012 Jan;61(1):1-11. doi: 10.1093/sysbio/syr074. Epub 2011 Aug 9.
4
Inferring speciation and extinction rates under different sampling schemes.推断不同采样方案下的物种形成和灭绝速率。
Mol Biol Evol. 2011 Sep;28(9):2577-89. doi: 10.1093/molbev/msr095. Epub 2011 Apr 11.
5
Improving marginal likelihood estimation for Bayesian phylogenetic model selection.改进贝叶斯系统发育模型选择的边缘似然估计。
Syst Biol. 2011 Mar;60(2):150-60. doi: 10.1093/sysbio/syq085. Epub 2010 Dec 27.
6
Bayesian phylogenetics and its influence on insect systematics.贝叶斯系统发生学及其对昆虫系统分类学的影响。
Annu Rev Entomol. 2010;55:189-206. doi: 10.1146/annurev.ento.54.110807.090529.
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On incomplete sampling under birth-death models and connections to the sampling-based coalescent.不完全抽样下的birth-death 模型与基于抽样的合并的联系。
J Theor Biol. 2009 Nov 7;261(1):58-66. doi: 10.1016/j.jtbi.2009.07.018. Epub 2009 Jul 23.
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Phylogenetic inference using whole genomes.使用全基因组进行系统发育推断。
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The conditioned reconstructed process.条件重建过程。
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10
jModelTest: phylogenetic model averaging.jModelTest:系统发育模型平均法。
Mol Biol Evol. 2008 Jul;25(7):1253-6. doi: 10.1093/molbev/msn083. Epub 2008 Apr 8.

MrBayes 3.2:在大型模型空间中进行高效的贝叶斯系统发育推断和模型选择。

MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space.

机构信息

Department of Biodiversity Informatics, Swedish Museum of Natural History, SE-10405 Stockholm, Sweden.

出版信息

Syst Biol. 2012 May;61(3):539-42. doi: 10.1093/sysbio/sys029. Epub 2012 Feb 22.

DOI:10.1093/sysbio/sys029
PMID:22357727
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3329765/
Abstract

Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d(N)/d(S) rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.

摘要

自 2001 年推出以来,MrBayes 作为使用马尔可夫链蒙特卡罗(MCMC)方法进行贝叶斯系统发育推断的软件包,其受欢迎程度不断提高。在此说明中,我们宣布发布 3.2 版,这是 2003 年发布的最新官方版本的重大升级。新版本提供了收敛诊断,并允许在并行运行的同时实时监测多个分析的收敛进度。新提议的引入和调整参数的自动优化,改善了许多问题的收敛性。新版本还通过流单指令多数据扩展(SSE)和对 BEAGLE 库的支持,显著加快了似然计算速度,允许将似然计算委托给兼容硬件上的图形处理单元(GPU)。SSE 代码的加速因子约为 2,而 BEAGLE 的加速因子则超过 50,适用于密码子问题。所有模型的检查点功能允许在分析过早终止时完成长时间运行。新模型包括放松的时钟、日期、时间可逆替代模型的模型平均以及对硬、负和部分(骨干)树约束的支持。通过充分纳入贝叶斯物种树估计(BEST)算法,支持从基因树推断物种树。使用步石法可以在整个模型空间中准确估计贝叶斯因子检验的边缘模型似然。新版本提供了比以前更多的输出选项,包括祖先状态、位点速率、位点 d(N)/d(S) 比、分支速率和节点日期的样本。还可以输出树参数的广泛统计信息,以便在 FigTree 和兼容软件中进行可视化。