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Adaptive RAxML-NG: Accelerating Phylogenetic Inference under Maximum Likelihood using Dataset Difficulty

Togkousidis, Anastasis; Kozlov, Oleksiy M.; Haag, Julia; Höhler, Dimitri; Stamatakis, Alexandros ORCID iD icon 1; Bonatto, Sandro [Hrsg.]
1 Institut für Theoretische Informatik (ITI), Karlsruher Institut für Technologie (KIT)

Abstract:

Phylogenetic inferences under the maximum likelihood criterion deploy heuristic tree search strategies to explore the vast search space. Depending on the input dataset, searches from different starting trees might all converge to a single tree topology. Often, though, distinct searches infer multiple topologies with large log-likelihood score differences or yield topologically highly distinct, yet almost equally likely, trees. Recently, Haag et al. introduced an approach to quantify, and implemented machine learning methods to predict, the dataset difficulty with respect to phylogenetic inference. Easy multiple sequence alignments (MSAs) exhibit a single likelihood peak on their likelihood surface, associated with a single tree topology to which most, if not all, independent searches rapidly converge. As difficulty increases, multiple locally optimal likelihood peaks emerge, yet from highly distinct topologies. To make use of this information, we introduce and implement an adaptive tree search heuristic in RAxML-NG, which modifies the thoroughness of the tree search strategy as a function of the predicted difficulty.


Verlagsausgabe §
DOI: 10.5445/IR/1000163922
Veröffentlicht am 16.11.2023
Originalveröffentlichung
DOI: 10.1093/molbev/msad227
Scopus
Zitationen: 2
Dimensions
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 06.10.2023
Sprache Englisch
Identifikator ISSN: 0737-4038, 1537-1719
KITopen-ID: 1000163922
Erschienen in Molecular Biology and Evolution
Verlag Oxford University Press (OUP)
Band 40
Heft 10
Nachgewiesen in Scopus
Dimensions
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