Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation

The estimation of phylogenetic trees for individual genes or multi-locus datasets is a basic part of considerable biological research. In order to enable large trees to be computed, Disjoint Tree Mergers (DTMs) have been developed; these methods operate by dividing the input sequence dataset into disjoint sets, constructing trees on each subset, and then combining the subset trees (using auxiliary information) into a tree on the full dataset. DTMs have been used to advantage for multi-locus species tree estimation, enabling highly accurate species trees at reduced computational effort, compared to leading species tree estimation methods. Here, we evaluate the feasibility of using DTMs to improve the scalability of maximum likelihood (ML) gene tree estimation to large numbers of input sequences. Our study shows distinct differences between the three selected ML codes-RAxML-NG, IQ-TREE 2, and FastTree 2-and shows that good DTM pipeline design can provide advantages over these ML codes on large datasets.

Automated summary

The use of Disjoint Tree Mergers (DTMs) for multi-locus species tree estimation has been successful in reducing computational effort while producing highly accurate species trees. In this study, the feasibility of applying DTMs to improve maximum likelihood (ML) gene tree estimation scalability for large numbers of input sequences was evaluated. The study showed that a well-designed DTM pipeline can provide advantages over selected ML codes on large datasets.