Survey of Branch Support Methods Demonstrates Accuracy, Power, and Robustness of Fast Likelihood-based Approximation Schemes
Maria Anisimova,Manuel Gil,Manuel Gil,Jean-François Dufayard,Christophe Dessimoz,Christophe Dessimoz,Olivier Gascuel +6 more
TLDR
This work compares the performance of the three fast likelihood-based methods with the standard bootstrap (SBS), the Bayesian approach, and the recently introduced rapid bootstrap, and proposes an additional method: a Bayesian-like transformation of aLRT (aBayes).Abstract:
Phylogenetic inference and evaluating support for inferred relationships is at the core of many studies testing evolutionary hypotheses. Despite the popularity of nonparametric bootstrap frequencies and Bayesian posterior probabil- ities, the interpretation of these measures of tree branch support remains a source of discussion. Furthermore, both meth- ods are computationally expensive and become prohibitive for large data sets. Recent fast approximate likelihood-based measures of branch supports (approximate likelihood ratio test (aLRT) and Shimodaira-Hasegawa (SH)-aLRT) provide a compelling alternative to these slower conventional methods, offering not only speed advantages but also excellent levels of accuracy and power. Here we propose an additional method: a Bayesian-like transformation of aLRT (aBayes). Consider- ing both probabilistic and frequentist frameworks, we compare the performance of the three fast likelihood-based methods with the standard bootstrap (SBS), the Bayesian approach, and the recently introduced rapid bootstrap. Our simulations and real data analyses show that with moderate model violations, all tests are sufficiently accurate, but aLRT and aBayes offer the highest statistical power and are very fast. With severe model violations aLRT, aBayes and Bayesian posteriors can produce elevated false-positive rates. With data sets for which such violation can be detected, we recommend using SH-aLRT, the nonparametric version of aLRT based on a procedure similar to the Shimodaira-Hasegawa tree selection. In general, the SBS seems to be excessively conservative and is much slower than our approximate likelihood-based methods. (Accuracy; aLRT; branch support methods; evolution; model violation; phylogenetic inference; power; SH-aLRT.)read more
Citations
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Journal ArticleDOI
IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era.
Bui Quang Minh,Heiko A. Schmidt,Olga Chernomor,Dominik Schrempf,Dominik Schrempf,Michael D. Woodhams,Arndt von Haeseler,Arndt von Haeseler,Robert Lanfear +8 more
TL;DR: Some notable features of IQ-TREE version 2 are described and the key advantages over other software are highlighted.
Journal ArticleDOI
W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis.
TL;DR: W-IQ-TREE supports multiple sequence types in common alignment formats and a wide range of evolutionary models including mixture and partition models, performing fast model selection, partition scheme finding, efficient tree reconstruction, ultrafast bootstrapping, branch tests, and tree topology tests.
Journal ArticleDOI
Ultrafast Approximation for Phylogenetic Bootstrap
TL;DR: This work proposes an ultrafast bootstrap approximation approach (UFBoot) to compute the support of phylogenetic groups in maximum likelihood (ML) based trees and offers an efficient and easy-to-use software to perform the UFBoot analysis with ML tree inference.
Journal ArticleDOI
A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes
TL;DR: A new large-scale phylogeny of squamate reptiles is presented that includes new, resurrected, and modified subfamilies within gymnophthalmid and scincid lizards, and boid, colubrid, and lamprophiid snakes.
Journal ArticleDOI
The genesis and source of the H7N9 influenza viruses causing human infections in China
Tommy Tsan-Yuk Lam,Jia Wang,Jia Wang,Yongyi Shen,Yongyi Shen,Yongyi Shen,Boping Zhou,Lian Duan,CL Cheung,Chi Ma,Chi Ma,Samantha Lycett,Connie Y. H. Leung,Xinchun Chen,Lifeng Li,Lifeng Li,Wenshan Hong,Yujuan Chai,Linlin Zhou,Huyi Liang,Huyi Liang,Zhihua Ou,Zhihua Ou,Yongmei Liu,Yongmei Liu,Amber Farooqui,David J. Kelvin,Leo L.M. Poon,David K. Smith,David K. Smith,Oliver G. Pybus,Gabriel M. Leung,Gabriel M. Leung,Yuelong Shu,Robert G. Webster,Richard J. Webby,Joseph S. M. Peiris,Andrew Rambaut,Andrew Rambaut,Hongbo Zhu,Hongbo Zhu,Yi Guan,Yi Guan +42 more
TL;DR: It is shown that H7 viruses probably transferred from domestic duck to chicken populations in China on at least two independent occasions and subsequently reassorted with enzootic H9N2 viruses to generate the H7N9 outbreak lineage, and a related previously unrecognized H7n7 lineage.
References
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Journal ArticleDOI
Confidence limits on phylogenies: an approach using the bootstrap.
TL;DR: The recently‐developed statistical method known as the “bootstrap” can be used to place confidence intervals on phylogenies and shows significant evidence for a group if it is defined by three or more characters.
Journal ArticleDOI
MRBAYES: Bayesian inference of phylogenetic trees
TL;DR: The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo, and an executable is available at http://brahms.rochester.edu/software.html.
Proceedings Article
Information Theory and an Extention of the Maximum Likelihood Principle
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
Journal ArticleDOI
A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.
Stéphane Guindon,Olivier Gascuel +1 more
TL;DR: This work has 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.
Book ChapterDOI
Information Theory and an Extension of the Maximum Likelihood Principle
TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.