Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference
文献类型：期刊论文
| 作者 | Zhang, Chi3,4; Huelsenbeck, John P.2; Ronquist, Fredrik1 |
| 刊名 | SYSTEMATIC BIOLOGY
 |
| 出版日期 | 2020-09-01 |
| 卷号 | 69期号:5页码:1016-1032 |
| 关键词 | Bayesian phylogenetic inference MCMC parsimony tree proposal |
| ISSN号 | 1063-5157 |
| DOI | 10.1093/sysbio/syaa002 |
| 通讯作者 | Ronquist, Fredrik(fredrik.ronquist@nrm.se) |
| 英文摘要 | Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as "convergence") and in estimating the correct proportions of the different types of them (known as "mixing"). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical data sets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these data sets, ranging in size from 357 to 934 taxa and from 1740 to 5681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account. |
| WOS关键词 | DNA-SEQUENCES ; ALGORITHMS ; MRBAYES ; EVOLUTION ; RATES |
| 资助项目 | Swedish Research Council (VR)[2014-05901] ; 100 Young Talents Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB26000000] ; Swedish National Infrastructure for Computing[SNIC 2014/1-323] ; Swedish National Infrastructure for Computing[SNIC 2015/1-394] |
| WOS研究方向 | Evolutionary Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:000593200000015 |
| 出版者 | OXFORD UNIV PRESS |
| 资助机构 | Swedish Research Council (VR) ; 100 Young Talents Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Swedish National Infrastructure for Computing |
| 源URL | [http://119.78.100.205/handle/311034/18792]  |
| 专题 | 中国科学院古脊椎动物与古人类研究所 |
| 通讯作者 | Ronquist, Fredrik |
| 作者单位 | 1.Swedish Museum Nat Hist, Dept Bioinformat & Genet, Box 50007, SE-10405 Stockholm, Sweden 2.Univ Calif Berkeley, Dept Integrat Biol, Berkeley, CA 94720 USA 3.Chinese Acad Sci, Ctr Excellence Life & Paleoenvironm, 142 XizhimenWai St, Beijing 100044, Peoples R China 4.Chinese Acad Sci, Inst Vertebrate Paleontol & Paleoanthropol, Key Lab Vertebrate Evolut & Human Origins, 142 XizhimenWai St, Beijing 100044, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Chi,Huelsenbeck, John P.,Ronquist, Fredrik. Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference[J]. SYSTEMATIC BIOLOGY,2020,69(5):1016-1032. |
| APA | Zhang, Chi,Huelsenbeck, John P.,&Ronquist, Fredrik.(2020).Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference.SYSTEMATIC BIOLOGY,69(5),1016-1032. |
| MLA | Zhang, Chi,et al."Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference".SYSTEMATIC BIOLOGY 69.5(2020):1016-1032. |
入库方式： OAI收割
来源：古脊椎动物与古人类研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。