Does the choice of nucleotide substitution models matter topologically?

Michael Hoff,Diego Darriba,Stefan Orf,Alexandros Stamatakis,Benedikt Riehm

doi:10.1186/s12859-016-0985-x

Abstract

BackgroundIn the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian information criteria. We address the question if model selection matters topologically, that is, if conducting ML inferences under the optimal, instead of a standard General Time Reversible model, yields different tree topologies. We also assess, to which degree models selected and trees inferred under the three standard criteria (AIC, AICc, BIC) differ. Finally, we assess if the definition of the sample size (#sites versus #sites × #taxa) yields different models and, as a consequence, different tree topologies.ResultsWe find that, all three factors (by order of impact: nucleotide model selection, information criterion used, sample size definition) can yield topologically substantially different final tree topologies (topological difference exceeding 10 %) for approximately 5 % of the tree inferences conducted on the 39 empirical datasets used in our study.ConclusionsWe find that, using the best-fit nucleotide substitution model may change the final ML tree topology compared to an inference under a default GTR model. The effect is less pronounced when comparing distinct information criteria. Nonetheless, in some cases we did obtain substantial topological differences.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0985-x) contains supplementary material, which is available to authorized users.

Highlights

In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian information criteria
We present a set of experiments on empirical datasets to answer the following question: Does model selection really matter with respect to its impact on the shape of the final tree topology? Posada and Buckley discussed the potential impact of the sample size on AICc and BIC criteria [15]
We assess the magnitude of topological differences between trees inferred under GTR+ and trees inferred under the best-fit model according to the respective information criterion

Summary

Introduction

We address the question if model selection matters topologically, that is, if conducting ML inferences under the optimal, instead of a standard General Time Reversible model, yields different tree topologies. To which degree models selected and trees inferred under the three standard criteria (AIC, AICc, BIC) differ. Statistical models of DNA evolution as used in Bayesian inference (BI) and Maximum Likelihood (ML) methods for phylogenetic reconstruction are typically required to be time-reversible. A nucleotide substitution matrix is timereversible, it must exhibit a certain symmetry. This symmetry requirement is depicted in the following example ACGT ⎛ ⎞ A.

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Conclusion