Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference

doi:10.1093/sysbio/syaa002

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	Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference
	Zhang, Chi 1,2; Huelsenbeck, John P.3; Ronquist, Fredrik 4
	2020-09-01
发表期刊	SYSTEMATIC BIOLOGY
ISSN	1063-5157
卷号	69 期号:5 页码:1016-1032
通讯作者	Ronquist, Fredrik(fredrik.ronquist@nrm.se)
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.
关键词	Bayesian phylogenetic inference MCMC parsimony tree proposal
DOI	10.1093/sysbio/syaa002
关键词[WOS]	DNA-SEQUENCES ; ALGORITHMS ; MRBAYES ; EVOLUTION ; RATES
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Evolutionary Biology
WOS类目	Evolutionary Biology
WOS记录号	WOS:000593200000015
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.205/handle/311034/18793
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.Chinese Acad Sci, Inst Vertebrate Paleontol & Paleoanthropol, Key Lab Vertebrate Evolut & Human Origins, 142 XizhimenWai St, Beijing 100044, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Life & Paleoenvironm, 142 XizhimenWai St, Beijing 100044, Peoples R China 3.Univ Calif Berkeley, Dept Integrat Biol, Berkeley, CA 94720 USA 4.Swedish Museum Nat Hist, Dept Bioinformat & Genet, Box 50007, SE-10405 Stockholm, Sweden
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.