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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Veröffentlicht in:	Nature (London) Jg. 556; H. 7702; S. 452 - 456
Hauptverfasser:	Lemoine, F., Domelevo Entfellner, J.-B., Wilkinson, E., Correia, D., Dávila Felipe, M., De Oliveira, T., Gascuel, O.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 01.04.2018 Nature Publishing Group
Schlagworte:	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
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Abstract	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.
AbstractList	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. Felsenstein’s article describing the application of the bootstrap to evolutionary trees is one of the most cited papers of all time. The bootstrap method, 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 with hundreds or thousands of taxa are becoming routine. In that framework, Felsenstein’s bootstrap tends to yield very low supports, especially on deep branches. We propose a new version of phylogenetic bootstrap, in which the presence of inferred branches in replications is measured using a gradual “transfer” distance, as opposed to the original version using a binary presence/absence index. The resulting supports are higher, while not inducing falsely supported branches. Our method is applied to large mammal, HIV, and simulated datasets, for which it reveals the phylogenetic signal, while Felsenstein’s bootstrap fails to do so. 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.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. 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. 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.
Audience	Academic
Author	Correia, D. De Oliveira, T. Wilkinson, E. Gascuel, O. Lemoine, F. Domelevo Entfellner, J.-B. Dávila Felipe, M.
AuthorAffiliation	4 KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa 3 Department of Computer Science & South African National Bioinformatics Institute, University of the Western Cape, Bellville 7535, Cape Town, South Africa 6 Méthodes et Algorithmes pour la Bioinformatique, IBC - LIRMM UMR 5506, Université de Montpellier & CNRS, Montpellier, France 1 Unité Bioinformatique Evolutive, C3BI USR 3756, Institut Pasteur & CNRS, Paris, France 5 Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa 2 Hub Bioinformatique et Biostatistique, C3BI USR 3756, Institut Pasteur & CNRS, Paris, France
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Snippet	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... 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... Felsenstein’s article describing the application of the bootstrap to evolutionary trees is one of the most cited papers of all time. The bootstrap method,...
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SubjectTerms	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
Title	Renewing Felsenstein’s phylogenetic bootstrap in the era of big data
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