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A representative Performance Assessment of Maximum Likelihood based Phylogenetic Inference Tools

Dimitri Höhler, Julia Haag, View ORCID ProfileAlexey M. Kozlov, Alexandros Stamatakis
doi: https://doi.org/10.1101/2022.10.31.514545
Dimitri Höhler
1Computational Molecular Evolution group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
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  • For correspondence: dimitri.hoehler@h-its.org
Julia Haag
1Computational Molecular Evolution group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
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Alexey M. Kozlov
1Computational Molecular Evolution group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
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Alexandros Stamatakis
1Computational Molecular Evolution group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
2Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Abstract

Summary The evaluation of phylogenetic inference tools is commonly conducted on simulated and empirical sequence data alignments. An open question is how representative these alignments are with respect to those, commonly analyzed by users. Based upon the RAxMLGrove database, it is now possible to simulate DNA sequences based on more than 70, 000 representative RAxML and RAxML-NG tree inferences on empirical datasets conducted on the RAxML web servers. This allows to assess the phylogenetic tree inference accuracy of various inference tools based on realistic and representative simulated DNA alignments. We simulated 20, 000 MSAs based on representative datasets (in terms of signal strength) from RAxMLGrove, and used 5, 000 datasets from the TreeBASE database, to assess the inference accuracy of FastTree2, IQ-TREE2, and RAxML-NG. We find that on quantifiably difficult-to-analyze MSAs all of the analysed tools perform poorly, such that the quicker FastTree2, can constitute a viable alternative to infer trees. We also find, that there are substantial differences between accuracy results on simulated and empirical data, despite the fact that a substantial effort was undertaken to simulate sequences under as realistic as possible settings.

Contact Dimitri Höhler, dimitri.hoehler{at}h-its.org

Competing Interest Statement

The authors have declared no competing interest.

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-NC-ND 4.0 International license.
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Posted November 01, 2022.
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A representative Performance Assessment of Maximum Likelihood based Phylogenetic Inference Tools
Dimitri Höhler, Julia Haag, Alexey M. Kozlov, Alexandros Stamatakis
bioRxiv 2022.10.31.514545; doi: https://doi.org/10.1101/2022.10.31.514545
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A representative Performance Assessment of Maximum Likelihood based Phylogenetic Inference Tools
Dimitri Höhler, Julia Haag, Alexey M. Kozlov, Alexandros Stamatakis
bioRxiv 2022.10.31.514545; doi: https://doi.org/10.1101/2022.10.31.514545

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