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Confirmatory Results

Does the choice of nucleotide substitution models matter topologically?

Michael Hoff, Stefan Orf, Benedikt Riehm, Diego Darriba, Alexandros Stamatakis
doi: https://doi.org/10.1101/041566
Michael Hoff
1Karlsruhe Institute of Technology, Department of Informatics, Kaiserstraße 12, 76131, Karlsruhe, Germany
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Stefan Orf
1Karlsruhe Institute of Technology, Department of Informatics, Kaiserstraße 12, 76131, Karlsruhe, Germany
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Benedikt Riehm
1Karlsruhe Institute of Technology, Department of Informatics, Kaiserstraße 12, 76131, Karlsruhe, Germany
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Diego Darriba
2The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
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Alexandros Stamatakis
1Karlsruhe Institute of Technology, Department of Informatics, Kaiserstraße 12, 76131, Karlsruhe, Germany
2The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
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  • For correspondence: Alexandros.Stamatakis@h-its.org
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Abstract

Background 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 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.

Results We 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.

Conclusions We 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.

Footnotes

  • ↵† Equal contributor

  • michael.hoff{at}student.kit.edu, steffan.orf{at}student.kit.edu, benedikt.riehm{at}student.kit.edu

  • diego.darriba{at}h-its.org, alexandros.stamatakis{at}h-its.org

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted February 26, 2016.
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Does the choice of nucleotide substitution models matter topologically?
Michael Hoff, Stefan Orf, Benedikt Riehm, Diego Darriba, Alexandros Stamatakis
bioRxiv 041566; doi: https://doi.org/10.1101/041566
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Does the choice of nucleotide substitution models matter topologically?
Michael Hoff, Stefan Orf, Benedikt Riehm, Diego Darriba, Alexandros Stamatakis
bioRxiv 041566; doi: https://doi.org/10.1101/041566

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