RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference

Abstract Motivation Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable...

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Veröffentlicht in:	Bioinformatics Jg. 35; H. 21; S. 4453 - 4455
Hauptverfasser:	Kozlov, Alexey M, Darriba, Diego, Flouri, Tomáš, Morel, Benoit, Stamatakis, Alexandros
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Oxford University Press 01.11.2019
Schlagworte:	Applications Notes
ISSN:	1367-4803, 1367-4811, 1460-2059, 1367-4811
Online-Zugang:	Volltext
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Zusammenfassung:	Abstract Motivation Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets. Results We present RAxML-NG, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability compared with RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and the recently introduced transfer bootstrap support metric. Availability and implementation The code is available under GNU GPL at https://github.com/amkozlov/raxml-ng. RAxML-NG web service (maintained by Vital-IT) is available at https://raxml-ng.vital-it.ch/. Supplementary information Supplementary data are available at Bioinformatics online.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1367-4803 1367-4811 1460-2059 1367-4811
DOI:	10.1093/bioinformatics/btz305