MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space.
MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space.
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DOI：
10.1093/sysbio/sys029
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发表时间：
2012-05
影响因子：
6.5
通讯作者：
Huelsenbeck JP
中科院分区：
文献类型：
--
作者：
Ronquist F;Teslenko M;van der Mark P;Ayres DL;Darling A;Höhna S;Larget B;Liu L;Suchard MA;Huelsenbeck JP
自从2001年推出以来，MrBayes作为一个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而越来越受欢迎。在此声明中，我们宣布3.2版本的发布，这是2003年发布的最新官方版本的主要升级。新版本提供了收敛诊断，并允许多个分析并行运行，并实时监控收敛进度。新方案的引入和自动优化的调谐参数提高了许多问题的收敛性。新版本还通过流式单指令多数据扩展（SSE）和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）。对于密码子问题，SSE代码的加速因子约为2，BEAGLE代码的加速因子超过50。跨越所有模型的检查点允许在分析过早终止的情况下完成长期运行。新模型包括放松时钟、定年、跨时间可逆替代模型的模型平均，以及对硬、负和部分（主干）树约束的支持。充分结合贝叶斯物种树估计（BEST）算法，支持从基因树推断物种树。使用垫脚石法可以在整个模型空间中准确地估计贝叶斯因子检验的边际模型似然。新版本提供了比以前更多的输出选项，包括祖先状态、站点速率、站点dN/dS比率、分支速率和节点日期的样本。还可以在FigTree和兼容软件中输出对树参数的广泛统计数据进行可视化。
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 dN/dS 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.