Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation

Minhyuk Park,Tandy Warnow,Paul Zaharias

doi:10.3390/a14050148

Abstract

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.

Highlights

Phylogenetic tree estimation is a basic part of many biological research studies, due to the centrality of the evolutionary perspective in biology
This study provides some insights into the conditions that impact the relative accuracy and running times of three leading maximum likelihood (ML) codes, and into the potential for Disjoint Tree Merger (DTM) pipelines to provide improved accuracy and/or scalability when compared to these ML codes
Our study showed that the best DTM pipelines we explored provide improved accuracy as compared to both FastTree and IQ-TREE, but they are not as reliably accurate as RAxML, a finding that should be contrasted with the trends reported in the prior study evaluating Constrained-INC pipelines for gene tree estimation [24]

Summary

Introduction

Phylogenetic tree estimation is a basic part of many biological research studies, due to the centrality of the evolutionary perspective in biology. These analyses are typically based on probabilistic models of evolution (e.g., the Generalized Time Reversible Model [1]), and maximum likelihood (ML) tree estimation under these models is a standard approach. FastTree 2 (more commonly referred to as “FastTree”) is the only ML heuristic that scales well to very large numbers of sequences (e.g., more than 10,000), but it is not as commonly used because of its relatively poor ML scores as compared to RAxML and other heuristics

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Discussion

Conclusion