Abstract
Incremental tree building (INC) is a new phylogeny estimation method that has been proven to be absolute fast converging under standard sequence evolution models. A variant of INC, called Constrained-INC, is designed for use in divide-and-conquer pipelines for phylogeny estimation where a set of species is divided into disjoint subsets, trees are computed on the subsets using a selected base method, and then the subset trees are combined together. We evaluate the accuracy of INC and Constrained-INC for gene tree and species tree estimation on simulated datasets, and compare it to similar pipelines using NJMerge (another method that merges disjoint trees). For gene tree estimation, we find that INC has very poor accuracy in comparison to standard methods, and even Constrained-INC(using maximum likelihood methods to compute constraint trees) does not match the accuracy of the better maximum likelihood methods. Results for species trees are somewhat different, with Constrained-INC coming close to the accuracy of the best species tree estimation methods, while being much faster; furthermore, using Constrained-INC allows species tree estimation methods to scale to large datasets within limited computational resources. Overall, this study exposes the benefits and limitations of divide-and-conquer strategies for large-scale phylogenetic tree estimation.
Highlights
THE estimation of gene trees and species trees is a basic part of many biological analysis pipelines; gene trees have implications for trait evolution and the prediction of protein function and structure, while species trees are needed to understand how species adapt to their environments, to date speciation events, etc
We examine the impact of Constrained-Incremental tree building (INC) for use in species tree estimation from multi-locus datasets, where gene trees can differ from the species tree due to incomplete lineage sorting (ILS)
Under low/moderate ILS conditions, both NJMerge and Constrained-INC were similar in accuracy to RAxML, but both were much faster than RAxML
Summary
THE estimation of gene trees and species trees is a basic part of many biological analysis pipelines; gene trees have implications for trait evolution and the prediction of protein function and structure (as well as other applications), while species trees are needed to understand how species adapt to their environments, to date speciation events, etc. The estimation of both gene trees and species trees are based on statistical models of evolution, with gene trees based on a single locus within the genome of the different species, and species trees based on multiple loci.
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