From Easy to Hopeless—Predicting the Difficulty of Phylogenetic Analyses

        Phylogenetic analyzes under the Maximum-Likelihood (ML) model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating ML-based tree inferences. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyzes, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard-to-analyze datasets.


DOI: 10.1093/molbev/msac254

Research Groups: Computational Molecular Evolution

Publication type: Journal

Journal: Molecular Biology and Evolution

Editors: Naruya Saitou

Citation: Molecular Biology and Evolution 39(12),msac254

Date Published: 1st Dec 2022

Registered Mode: by DOI

Haag, J., Höhler, D., Bettisworth, B., & Stamatakis, A. (2022). From Easy to Hopeless—Predicting the Difficulty of Phylogenetic Analyses. In N. Saitou (Ed.), Molecular Biology and Evolution (Vol. 39, Issue 12). Oxford University Press (OUP).

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Created: 2nd Jan 2024 at 17:57

Last updated: 5th Mar 2024 at 21:25

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