Abstract
Phylogenetic analyses under the Maximum Likelihood 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 Maximum Likelihood 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 analyses, 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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We included a new subsection entitled New Approach in the manuscript to highlight distinctions between previous approaches and Pythia. We further added a section entitled Use and Misuse of Pythia to state more clearly the use cases of Pythia and draw attention to potential misuse cases. We updated the supplementary material and included justification for training Pythia on DNA and AA data conjointly. In general, we made a substantial attempt to further streamline the manuscript through restructuring and rewriting of unclear paragraphs.