From Easy to Hopeless—Predicting the Difficulty of Phylogenetic Analyses. (17th November 2022)

• Record Type: Journal Article
• Authors: Haag, Julia; Höhler, Dimitri; Bettisworth, Ben; Stamatakis, Alexandros
• Editors: Saitou, Naruya
• Is Part Of: Molecular biology and evolution. Volume 39:Number 12(2022)
• Publication Date: 2022-11-17
• Subjects: phylogenetics -- maximum likelihood -- machine learning -- random forest regression; Molecular biology -- Periodicals; Molecular evolution -- Periodicals; Evolution, Molecular -- Periodicals; Molecular Biology -- Periodicals; 572.8
• DOI: 10.1093/molbev/msac254
• Languages: English
• ISSNs: 0737-4038

Abstract

Abstract: 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.