New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0
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PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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FastTree was found to be much faster than RAxML and PhyML but reported lower likelihood scores for data sets with both small and large numbers of sequences.
“On the other hand, FastTree was found to be much faster than RAxML and PhyML but reported lower likelihood scores for data sets with both small and large numbers of sequences (Guindon et al. 2010; Price et al. 2010; Liu et al. 2011).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Guindon et al. 2010
In prior studies, RAxML and PhyML showed largely similar performance in identifying trees of higher likelihood scores, whereas IQ-TREE exhibited improved efficiency compared with both RAxML and PhyML.
“In these studies, RAxML and PhyML showed largely similar performance in identifying trees of higher likelihood scores (Guindon et al. 2010; Money and Whelan 2012), whereas IQ-TREE exhibited improved efficiency compared with both RAxML and PhyML (Nguyen et al. 2015; Chernomor et al. 2016).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Guindon et al. 2010
RAxML consistently outperformed PhyML in all data sets.
“However, in contrast to previous observations (Guindon et al. 2010), we found that RAxML consistently outperformed PhyML in all data sets.”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Guindon et al. 2010
Prior to this study, the four programs had only been evaluated using simulated data and empirical data sets with wide ranges of taxon numbers but relatively small numbers of genes, which might not well approximate today's state-of-the-art phylogenomic data matrices.
“So far, these four programs have only been evaluated using simulated data (Guindon et al. 2010; Price et al. 2010; Liu et al. 2011), and empirical data sets with wide ranges of taxon numbers (e.g., up to 237,882 taxa in Price et al. [2010]) but relatively small numbers of genes (from ∼10 [Price et al. 2010; Liu et al. 2011; Chernomor et al. 2016] to ∼200 [Guindon et al. 2010; Money and Whelan 2012”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Guindon et al. 2010
The difference between our finding that RAxML outperformed PhyML and previous observations might be due to the small number of alignments examined in the previous study and the numerous updates of both programs since then.
“This difference might be due to the small number of alignments examined in the previous study (Guindon et al. 2010) and the numerous updates of both programs since then.”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Guindon et al. 2010
Our single-gene tree inference results are consistent with previous smaller-scale studies on the better efficiency of IQ-TREE relative to RAxML and PhyML and the inferior performance of FastTree in likelihood score maximization.
“Overall, our results at the level of single-gene tree inference are consistent with previous, smaller-scale studies on the better efficiency of IQ-TREE relative to RAxML and PhyML (all using one search per alignment) (Nguyen et al. 2015), and the inferior performance of FastTree in likelihood score maximization when compared with other programs (Guindon et al. 2010; Liu et al. 2011).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Guindon et al. 2010
The differential time efficiency of the programs on protein versus DNA data may be attributed to their distinct algorithmic designs and/or software implementations.
“Such differential behavior may be attributed to the distinct algorithmic designs and/or software implementations of the programs on protein and DNA data (Guindon et al. 2010).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Guindon et al. 2010
Some of the examined programs displayed different time efficiency on protein and DNA data sets.
“in agreement with previous studies (Guindon et al. 2010; Nguyen et al. 2015), we found that some programs displayed different time efficiency on protein and DNA data sets”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Guindon et al. 2010
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 Guindon et al. 2010
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- Authors
- Guindon, S., Dufayard, J.-F., Lefort, V., Anisimova, M., Hordijk, W., & Gascuel, O.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Gascuel, Olivier
- ORCID
- Venue
- Systematic Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-03-29
- Volume
- 59
- Issue
- 3
- Pages
- 307-321
- ISSN
- 1063-5157 (ISSN-L)
- 1076-836X
- OpenAlex
- W2111211467
- Cited by
- 18,376
- References
- 41
- Citations by year
- 2026: 1054 · 2025: 1310 · 2024: 1284 · 2023: 1304 · 2022: 1541 · 2021: 1787 · 2020: 1614 · 2019: 1428 · 2018: 1301 · 2017: 1148 · 2016: 1113 · 2015: 1006 · 2014: 1002 · 2013: 748 · 2012: 507
- FWCI
- 190.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Systematic Biology (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- HAL (submittedVersion, other-oa)
- reroDoc Digital Library (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ResearchSpace (University of Auckland) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Agence Nationale de la Recherche
- 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
- Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
- Keywords
- PhyML, maximum likelihood phylogenetic inference, nonparametric bootstrap
- MeSH terms
- Algorithms, Classification, Phylogeny, Software, Likelihood Functions
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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