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A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
Open accessAbstract
The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/.
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Adrenal Mitochondria and Steroidogenesis: From Individual Proteins to Functional Protein Assemblies
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“Nucleotide sequences were aligned using MUSCLE (42), and phylogenetic trees constructed using PHyML (43).”
MethodGenomic insights into Wnt signaling in an early diverging metazoan, the ctenophore Mnemiopsis leidyi
Pang, K., Ryan, J. F., Program, N. C. S., Mullikin, J. C., Baxevanis, A. D., & Martindale, M. Q. (2010) · EvoDevo · doi:10.1186/2041-9139-1-10 · Cited by 115
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Lysosomal Degradation Is Required for Sustained Phagocytosis of Bacteria by Macrophages
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High Frequency and Diversity of Species C Enteroviruses in Cameroon and Neighboring Countries
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Chimeric plastid proteome in the Florida “red tide” dinoflagellate Karenia brevis
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Origin of animal epithelia: insights from the sponge genome
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Genomic support for a moa-tinamou clade and adaptive morphological convergence in flightless ratites
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Metadata
- Authors
- Guindon, S., & Gascuel, O.
- Venue
- Systematic Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-10-01
- Volume
- 52
- Issue
- 5
- Pages
- 696-704
- ISSN
- 1063-5157 (ISSN-L)
- 1076-836X
- OpenAlex
- W2103546861
- Cited by
- 16,806
- References
- 4
- FWCI
- 76.13
- Type
- journal article
- Language
- en
- Open access
- bronze
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
