RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation
Open accessAbstract
Statistical methods for phylogeny estimation, especially maximum likelihood (ML), offer high accuracy with excellent theoretical properties. However, RAxML, the current leading method for large-scale ML estimation, can require weeks or longer when used on datasets with thousands of molecular sequences. Faster methods for ML estimation, among them FastTree, have also been developed, but their relative performance to RAxML is not yet fully understood. In this study, we explore the performance with respect to ML score, running time, and topological accuracy, of FastTree and RAxML on thousands of alignments (based on both simulated and biological nucleotide datasets) with up to 27,634 sequences. We find that when RAxML and FastTree are constrained to the same running time, FastTree produces topologically much more accurate trees in almost all cases. We also find that when RAxML is allowed to run to completion, it provides an advantage over FastTree in terms of the ML score, but does not produce substantially more accurate tree topologies. Interestingly, the relative accuracy of trees computed using FastTree and RAxML depends in part on the accuracy of the sequence alignment and dataset size, so that FastTree can be more accurate than RAxML on large datasets with relatively inaccurate alignments. Finally, the running times of RAxML and FastTree are dramatically different, so that when run to completion, RAxML can take several orders of magnitude longer than FastTree to complete. Thus, our study shows that very large phylogenies can be estimated very quickly using FastTree, with little (and in some cases no) degradation in tree accuracy, as compared to RAxML.
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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 Liu et al. 2011
Liu et al. (2011) examined data sets with highly reliable curated phylogenies as references, which are not available in most empirical studies, and much greater numbers of taxa than the ones examined in our study (up to 200).
“Importantly, Liu et al. (2011) examined data sets with highly reliable curated phylogenies as references, which are not available in most empirical studies, and also much greater numbers of taxa (between 263 and 27,643 in most cases) than the ones examined in our study (up to 200).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Liu et al. 2011
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 Liu et al. 2011
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 Liu et al. 2011
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- Authors
- Liu, K., Linder, C. R., & Warnow, T.
- Affiliations
- The University of Texas at Austin (US)
- Corresponding author
- Warnow, Tandy
- ORCID
- Warnow, Tandy
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-11-21
- Volume
- 6
- Issue
- 11
- Pages
- e27731
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3221724
- OpenAlex
- W2152179349
- Cited by
- 207
- References
- 18
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 11 · 2023: 16 · 2022: 15 · 2021: 18 · 2020: 30 · 2019: 13 · 2018: 14 · 2017: 17 · 2016: 13 · 2015: 12 · 2014: 18 · 2013: 12 · 2012: 4
- FWCI
- 4.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Texas ScholarWorks (Texas Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation (0733029)
- John Simon Guggenheim Memorial Foundation
- University of Texas at Austin
- Division of Environmental Biology
- 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 › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- RAxML, FastTree, running time comparison, DNA sequence alignment, method comparison
- MeSH terms
- Base Sequence, Computer Simulation, Humans, Phylogeny, Software, Likelihood Functions, Sequence Alignment, Computational Biology, Databases, Genetic
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