RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

Stamatakis, Alexandros

doi:10.1093/bioinformatics/btl446

Cited by 15,582Genomics and Phylogenetic StudiesRNA and protein synthesis mechanismsPlant Diversity and Evolution

RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

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Stamatakis, Alexandros·2006·Bioinformatics

DOI 10.1093/bioinformatics/btl446

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Abstract

UNLABELLED: RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. AVAILABILITY: icwww.epfl.ch/~stamatak

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IQ-TREE uses NNI as its topological rearrangement mechanism, whereas RAxML and PhyML are both based on SPR.
“A potential explanation could be that IQ-TREE uses NNI as its topological rearrangement mechanism (Nguyen et al. 2015), whereas RAxML and PhyML are both based on SPR (Stamatakis 2006; Guindon et al. 2010).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Stamatakis 2006

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Ley,R. et al. (2005) Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA, 102, 11070-11075.
Stamatakis,A. (2006) Phylogenetic models of rate heterogeneity: a high per- formance computing perspective. In Proceedings of the IPDPS2006, Rhodos, Greece.
Zwickl,D. (2006) Genetic algorithm approaches for the phylogenetic analysis of large biologiical sequence datasets under the maximum likelihood criterion. PhD thesis, University of Texas at Austin, TX.

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Authors: Stamatakis, A.
Affiliations: École Polytechnique Fédérale de Lausanne (CH)
Corresponding author: Stamatakis, Alexandros
ORCID: Stamatakis, Alexandros
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2006-08-23
Volume: 22
Issue: 21
Pages: 2688-2690
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/btl446
OpenAlex: W2168696662
Cited by: 15,582
References: 0
Citations by year: 2026: 198 · 2025: 337 · 2024: 418 · 2023: 480 · 2022: 628 · 2021: 841 · 2020: 918 · 2019: 993 · 2018: 1019 · 2017: 1078 · 2016: 1245 · 2015: 1338 · 2014: 1714 · 2013: 1292 · 2012: 1075
FWCI: 82.83
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
Locations: Bioinformatics (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
reroDoc Digital Library (submittedVersion)
Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Plant Diversity and Evolution
Keywords: maximum likelihood phylogenetic inference, large phylogenies, parallel computing, MPI, bootstrapping, software performance comparison, phylogenetics software
MeSH terms: Algorithms, Models, Genetic, Phylogeny, Sequence Homology, Nucleic Acid, Software, Species Specificity, Models, Statistical, Sequence Alignment, Conserved Sequence, Sequence Analysis, DNA, Evolution, Molecular

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