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2021 (English)In: Communications Biology, E-ISSN 2399-3642, Vol. 4, article id 244Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
Springer Nature, 2021
National Category
Probability Theory and Statistics Computer Sciences Evolutionary Biology
Identifiers
urn:nbn:se:uu:diva-432405 (URN)10.1101/2020.06.16.154443 (DOI)000623853500002 ()33627766 (PubMedID)
Funder
Swedish Research Council, 2013-4853Swedish Research Council, 2018-04620Swedish Foundation for Strategic Research, RIT15-0012EU, Horizon 2020, 898120
2021-01-192021-01-192024-01-15Bibliographically approved