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GeneRax: A tool for species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss

View ORCID ProfileBenoit Morel, View ORCID ProfileAlexey M. Kozlov, View ORCID ProfileAlexandros Stamatakis, View ORCID ProfileGergely J. Szöllősi
doi: https://doi.org/10.1101/779066
Benoit Morel
1Computational Molecular Evolution group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
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  • For correspondence: benoit.morel@h-its.org
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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Gergely J. Szöllősi
3ELTE-MTA Lendület Evolutionary Genomics Research Group, Pázmány P. stny. 1A., H-1117 Budapest, Hungary
4Dept. Biological Physics, Etvs University, Pázmány P. stny. 1A., H-1117 Budapest, Hungary
5Evolutionary Systems Research Group, Centre for Ecological Research, Hungarian Academy of Sciences, 8237 Tihany, Klebelsberg Kuno str. 3. Hungary
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Abstract

Inferring gene trees is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges species tree-aware methods seek to use information from a putative species tree. However, there are few methods available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data pre-processing (e.g., computing bootstrap trees), and rely on approximations and heuristics that limit the exploration of tree space. Here we present GeneRax, the first maximum likelihood species tree-aware gene tree inference software. It simultaneously accounts for substitutions at the sequence level and gene level events, such as duplication, transfer and loss and uses established maximum likelihood optimization algorithms. GeneRax can infer rooted gene trees for an arbitrary number of gene families, directly from the per-gene sequence alignments and a rooted, but undated, species tree. We show that compared to competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms relative Robinson-Foulds distance. While, on empirical datasets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and that it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1099 Cyanobacteria families in eight minutes on 512 CPU cores. Thus, its advanced parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax.

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  • https://cme.h-its.org/exelixis/material/generax_data.tar.gz

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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 September 26, 2019.
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GeneRax: A tool for species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss
Benoit Morel, Alexey M. Kozlov, Alexandros Stamatakis, Gergely J. Szöllősi
bioRxiv 779066; doi: https://doi.org/10.1101/779066
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GeneRax: A tool for species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss
Benoit Morel, Alexey M. Kozlov, Alexandros Stamatakis, Gergely J. Szöllősi
bioRxiv 779066; doi: https://doi.org/10.1101/779066

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