FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments
Open accessAbstract
BACKGROUND: We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. METHODOLOGY/PRINCIPAL FINDINGS: Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the "CAT" approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100-1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. CONCLUSIONS/SIGNIFICANCE: FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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The validation process for BLS methods is centered around comparison of phylogenetic trees estimated from the alignment with FastTree 2.1.
“Thus, the validation process for our methods is centered around the comparison of phylogenetic trees estimated from the alignment with FastTree 2.1 (ref. 34).”
From Improving microbial phylogeny with citizen science within a mass-market video game (2025) · cites this paper as [34]
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 Price 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 Price et al. 2010
FastTree generates less accurate tree estimates than SPR-based ML methods.
“As a tradeoff, FastTree generates less accurate tree estimates than SPR-based ML methods (Price et al. 2010).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Price et al. 2010
FastTree can be orders of magnitude faster than either PhyML or RAxML/ExaML.
“For example, the recently developed FastTree program can be orders of magnitude faster than either PhyML or RAxML/ExaML (Price et al. 2010).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Price et al. 2010
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- Authors
- Price, M. N., Dehal, P. S., & Arkin, A. P.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Price, Morgan N.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-03-10
- Volume
- 5
- Issue
- 3
- Pages
- e9490
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC2835736
- OpenAlex
- W2031611770
- Cited by
- 14,894
- References
- 30
- Citations by year
- 2026: 1298 · 2025: 1543 · 2024: 1657 · 2023: 1486 · 2022: 1676 · 2021: 1651 · 2020: 1363 · 2019: 1013 · 2018: 776 · 2017: 686 · 2016: 521 · 2015: 415 · 2014: 309 · 2013: 256 · 2012: 133
- FWCI
- 63.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Funders
- U.S. Department of Energy (-AC02-05CH11231, 05CH11231, DE AC02 05CH11231, DE-AC02-)
- 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
- phylogenetic inference, maximum likelihood phylogeny, nearest neighbor interchange, cat model, 16S rRNA, phylogenetic accuracy, scalability
- MeSH terms
- Algorithms, Animals, Computers, Data Interpretation, Statistical, Genetic Techniques, Humans, Models, Genetic, Phylogeny, RNA, Ribosomal, 16S, Software, Likelihood Functions, Sequence Alignment, Databases, Protein
About these checks
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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