Universal probabilistic programming offers a powerful approach to statistical phylogenetics
Universal probabilistic programming offers a powerful approach to statistical phylogenetics
Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here we show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and efficient estimation of Bayes factors that are used in model testing. We take advantage of this in automatically generating SMC algorithms for several recent diversification models that have been difficult or impossible to tackle previously. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.
Kudlicka Jan、Lartillot Nicolas、Lund¨|n Daniel、Sch?n Thomas B.、Broman David、Senderov Viktor、Ronquist Fredrik、Borgstr?m Johannes、Murray Lawrence
Department of Information Technology, Uppsala UniversityLaboratoire de Biom¨|trie et Biologie Evolutive, UMR CNRS 5558, Universit¨| Claude Bernard Lyon 1Department of Computer Science, KTH Royal Institute of TechnologyDepartment of Information Technology, Uppsala UniversityDepartment of Computer Science, KTH Royal Institute of TechnologyDepartment of Bioinformatics and Genetics, Swedish Museum of Natural HistoryDepartment of Bioinformatics and Genetics, Swedish Museum of Natural HistoryDepartment of Information Technology, Uppsala UniversityUber AI
生物科学现状、生物科学发展生物科学研究方法、生物科学研究技术计算技术、计算机技术
Kudlicka Jan,Lartillot Nicolas,Lund¨|n Daniel,Sch?n Thomas B.,Broman David,Senderov Viktor,Ronquist Fredrik,Borgstr?m Johannes,Murray Lawrence.Universal probabilistic programming offers a powerful approach to statistical phylogenetics[EB/OL].(2025-03-28)[2025-06-21].https://www.biorxiv.org/content/10.1101/2020.06.16.154443.点此复制
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