About

Thanks for your interest in phyddle. The phyddle project emerged from a phylogenetic deep learning study led by Ammon Thompson (paper). The goal of phyddle is to provide its users with a generalizable pipeline workflow for phylogenetic modeling and deep learning. This hopefully will make it easier for phylogenetic model enthusiasts and developers to explore and apply models that do not have tractable likelihood functions. It’s also intended for use by methods developers who want to characterize how deep learning methods perform under different conditions for standard phylogenetic estimation tasks.

The phyddle project is developed by Michael Landis and Ammon Thompson.

Issues & Feedback

Please use Issues to report bugs or request features that require modifying phyddle source code. Please contact Michael Landis to request troubleshooting support using phyddle.

References

EE Goldberg, LT Lancaster, RH Ree. 2011. Phylogenetic inference of reciprocal effects between geographic range evolution and diversification. Systematic Biology 60:451-465. doi: https://doi.org/10.1093/sysbio/syr046

S Lambert, J Voznica, H Morlon. 2022. Deep learning from phylogenies for diversification analyses. bioRxiv. 2022.09.27.509667. doi: https://doi.org/10.1101/2022.09.27.509667

A Thompson, B Liebeskind, EJ Scully, MJ Landis. 2023. Deep learning approaches to viral phylogeography are fast and as robust as likelihood methods to model misspecification. bioRxiv 2023.02.08.527714; doi: https://doi.org/10.1101/2023.02.08.527714

TG Vaughan, AJ Drummond. 2013. A stochastic simulator of birth–death master equations with application to phylodynamics. Mol Biol Evol 30:1480–1493. doi: https://doi.org/10.1093/molbev/mst057

J Voznica, A Zhukova, V Boskova, E Saulnier, F Lemoine, M Moslonka-Lefebvre, O Gascuel. 2022. Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks. Nat Commun 13:3896. doi: https://doi.org/10.1038/s41467-022-31511-0