RT Journal Article
SR Electronic
T1 Universal probabilistic programming offers a powerful approach to statistical phylogenetics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.16.154443
DO 10.1101/2020.06.16.154443
A1 Fredrik Ronquist
A1 Jan Kudlicka
A1 Viktor Senderov
A1 Johannes Borgström
A1 Nicolas Lartillot
A1 Daniel Lundén
A1 Lawrence Murray
A1 Thomas B. Schön
A1 David Broman
YR 2020
UL http://biorxiv.org/content/early/2020/12/10/2020.06.16.154443.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.