Abstract
BackgroundPhylogeny estimation is an important part of much biological research, but large-scale tree estimation is infeasible using standard methods due to computational issues. Recently, an approach to large-scale phylogeny has been proposed that divides a set of species into disjoint subsets, computes trees on the subsets, and then merges the trees together using a computed matrix of pairwise distances between the species. The novel component of these approaches is the last step: Disjoint Tree Merger (DTM) methods.ResultsWe present GTM (Guide Tree Merger), a polynomial time DTM method that adds edges to connect the subset trees, so as to provably minimize the topological distance to a computed guide tree. Thus, GTM performs unblended mergers, unlike the previous DTM methods. Yet, despite the potential limitation, our study shows that GTM has excellent accuracy, generally matching or improving on two previous DTMs, and is much faster than both.ConclusionsThe proposed GTM approach to the DTM problem is a useful new tool for large-scale phylogenomic analysis, and shows the surprising potential for unblended DTM methods.
Highlights
Phylogeny estimation is an important part of much biological research, but large-scale tree estimation is infeasible using standard methods due to computational issues
Experiment 1: designing the Disjoint Tree Merger (DTM) pipelines We consider the accuracy of pipelines for each DTM method, selecting either RAxML or ASTRAL for constraint trees and NJst, ASTRAL, or FastTree for the starting tree
Results for TreeMerge and Guide Tree Merger (GTM) on the high incomplete lineage sorting (ILS) datasets with 10 introns are shown in Fig. 3; NJMerge is not shown for these data, as it failed for all these replicates
Summary
Phylogeny estimation is an important part of much biological research, but large-scale tree estimation is infeasible using standard methods due to computational issues. An approach to large-scale phylogeny has been proposed that divides a set of species into disjoint subsets, computes trees on the subsets, and merges the trees together using a computed matrix of pairwise distances between the species. The novel component of these approaches is the last step: Disjoint Tree Merger (DTM) methods
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