Renewing Felsenstein’s phylogenetic bootstrap in the era of big data

Felsenstein’s application of the bootstrap method to evolutionary trees is one of the most cited scientific papers of all time. The bootstrap method, which is based on resampling and replications, is used extensively to assess the robustness of phylogenetic inferences. However, increasing numbers of...

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Bibliographic Details
Published in:Nature (London) Vol. 556; no. 7702; pp. 452 - 456
Main Authors: Lemoine, F., Domelevo Entfellner, J.-B., Wilkinson, E., Correia, D., Dávila Felipe, M., De Oliveira, T., Gascuel, O.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.04.2018
Nature Publishing Group
Subjects:
631/114/2415
631/114/739
631/114/794
631/181/2480
631/181/757
Algorithms
Big data
Bioinformatics
Bootstrap method
Bootstrapping (Statistics)
Branches
Computer Science
Data management
Data processing
Datasets
Epidemiology
HIV
Human immunodeficiency virus
Humanities and Social Sciences
Hypothesis testing
multidisciplinary
Phylogenetics
Phylogeny
Resampling
Science
Science (multidisciplinary)
Scientific papers
Statistical methods
Statistical models
ISSN:0028-0836, 1476-4687, 1476-4687
Online Access:Get full text
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Summary:Felsenstein’s application of the bootstrap method to evolutionary trees is one of the most cited scientific papers of all time. The bootstrap method, which is based on resampling and replications, is used extensively to assess the robustness of phylogenetic inferences. However, increasing numbers of sequences are now available for a wide variety of species, and phylogenies based on hundreds or thousands of taxa are becoming routine. With phylogenies of this size Felsenstein’s bootstrap tends to yield very low supports, especially on deep branches. Here we propose a new version of the phylogenetic bootstrap in which the presence of inferred branches in replications is measured using a gradual ‘transfer’ distance rather than the binary presence or absence index used in Felsenstein’s original version. The resulting supports are higher and do not induce falsely supported branches. The application of our method to large mammal, HIV and simulated datasets reveals their phylogenetic signals, whereas Felsenstein’s bootstrap fails to do so. A new version of the phylogenetic bootstrap method enables assessment of the robustness of phylogenies that are based on large datasets of hundreds or thousands of taxa.
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ISSN:0028-0836
1476-4687
1476-4687
DOI:10.1038/s41586-018-0043-0