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Probabilistic programming: a powerful new approach to statistical phylogenetics

View ORCID ProfileFredrik Ronquist, View ORCID ProfileJan Kudlicka, View ORCID ProfileViktor Senderov, View ORCID ProfileJohannes Borgström, View ORCID ProfileNicolas Lartillot, View ORCID ProfileDaniel Lundén, View ORCID ProfileLawrence Murray, View ORCID ProfileThomas B. Schön, View ORCID ProfileDavid Broman
doi: https://doi.org/10.1101/2020.06.16.154443
Fredrik Ronquist
1Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Box 50007, SE-104 05 Stockholm, Sweden
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  • For correspondence: fredrik.ronquist@nrm.se
Jan Kudlicka
2Department of Information Technology, Uppsala University, Box 337, SE-751 05 Uppsala, Sweden
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Viktor Senderov
1Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Box 50007, SE-104 05 Stockholm, Sweden
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Johannes Borgström
2Department of Information Technology, Uppsala University, Box 337, SE-751 05 Uppsala, Sweden
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Nicolas Lartillot
3Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Claude Bernard Lyon 1, FR-69622 Villeurbanne Cedex, France
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Daniel Lundén
4Department of Computer Science, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
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Lawrence Murray
5Uber AI, San Francisco CA 94105, United States
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Thomas B. Schön
2Department of Information Technology, Uppsala University, Box 337, SE-751 05 Uppsala, Sweden
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David Broman
4Department of Computer Science, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
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Abstract

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 model expression problem, while still supporting automated generation of efficient inference algorithms. To illustrate the power of the approach, we use it to generate sequential Monte Carlo (SMC) algorithms for recent biological diversification models that have been difficult to tackle using traditional approaches. This is the first time that SMC algorithms have been available for these models, and the first time it has been possible to compare them using model testing. Leveraging these advances, we re-examine previous claims about the performance of the models. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before PPLs can be effectively applied to the full range of phylogenetic models.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/phyppl/probabilistic-programming/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 18, 2020.
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Probabilistic programming: a powerful new approach to statistical phylogenetics
Fredrik Ronquist, Jan Kudlicka, Viktor Senderov, Johannes Borgström, Nicolas Lartillot, Daniel Lundén, Lawrence Murray, Thomas B. Schön, David Broman
bioRxiv 2020.06.16.154443; doi: https://doi.org/10.1101/2020.06.16.154443
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Probabilistic programming: a powerful new approach to statistical phylogenetics
Fredrik Ronquist, Jan Kudlicka, Viktor Senderov, Johannes Borgström, Nicolas Lartillot, Daniel Lundén, Lawrence Murray, Thomas B. Schön, David Broman
bioRxiv 2020.06.16.154443; doi: https://doi.org/10.1101/2020.06.16.154443

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