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      RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference

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          Abstract

          Motivation

          Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets.

          Results

          We present RAxML-NG, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability compared with RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and the recently introduced transfer bootstrap support metric.

          Availability and implementation

          The code is available under GNU GPL at https://github.com/amkozlov/raxml-ng . RAxML-NG web service (maintained by Vital-IT) is available at https://raxml-ng.vital-it.ch/ .

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Renewing Felsenstein’s Phylogenetic Bootstrap in the Era of Big Data

          F. Lemoine, J.-B. Domelevo Entfellner, E. Wilkinson … (2018)
          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.
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            EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences

            Pierre Barbera, Alexey M. Kozlov, Lucas Czech … (2018)
            Abstract Next generation sequencing (NGS) technologies have led to a ubiquity of molecular sequence data. This data avalanche is particularly challenging in metagenetics, which focuses on taxonomic identification of sequences obtained from diverse microbial environments. Phylogenetic placement methods determine how these sequences fit into an evolutionary context. Previous implementations of phylogenetic placement algorithms, such as the evolutionary placement algorithm (EPA) included in RAxML, or PPLACER, are being increasingly used for this purpose. However, due to the steady progress in NGS technologies, the current implementations face substantial scalability limitations. Herein, we present EPA-NG, a complete reimplementation of the EPA that is substantially faster, offers a distributed memory parallelization, and integrates concepts from both, RAxML-EPA and PPLACER. EPA-NG can be executed on standard shared memory, as well as on distributed memory systems (e.g., computing clusters). To demonstrate the scalability of EPA-NG, we placed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$1$\end{document} billion metagenetic reads from the Tara Oceans Project onto a reference tree with 3748 taxa in just under \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$7$\end{document} h, using 2048 cores. Our performance assessment shows that EPA-NG outperforms RAxML-EPA and PPLACER by up to a factor of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$30$\end{document} in sequential execution mode, while attaining comparable parallel efficiency on shared memory systems. We further show that the distributed memory parallelization of EPA-NG scales well up to 2048 cores. EPA-NG is available under the AGPLv3 license: https://github.com/Pbdas/epa-ng .
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              Empirical profile mixture models for phylogenetic reconstruction.

              Le Quang, Nicolas Lartillot, Olivier Gascuel (2008)
              Previous studies have shown that accounting for site-specific amino acid replacement patterns using mixtures of stationary probability profiles offers a promising approach for improving the robustness of phylogenetic reconstructions in the presence of saturation. However, such profile mixture models were introduced only in a Bayesian context, and are not yet available in a maximum likelihood (ML) framework. In addition, these mixture models only perform well on large alignments, from which they can reliably learn the shapes of profiles, and their associated weights. In this work, we introduce an expectation-maximization algorithm for estimating amino acid profile mixtures from alignment databases. We apply it, learning on the HSSP database, and observe that a set of 20 profiles is enough to provide a better statistical fit than currently available empirical matrices (WAG, JTT), in particular on saturated data.
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                Author and article information

                Contributors
                Jonathan Wren: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 01 November 2019
                Publication date (Electronic): 09 May 2019
                Publication date PMC-release: 09 May 2019
                Volume: 35
                Issue: 21
                Pages: 4453-4455
                Affiliations
                [1 ] Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies , Heidelberg, Germany
                [2 ] Institute of Theoretical Informatics, Karlsruhe Institute of Technology , Karlsruhe, Germany
                Author notes
                To whom correspondence should be addressed. E-mail: alexey.kozlov@ 123456h-its.org
                Author information
                http://orcid.org/0000-0001-7394-2718
                http://orcid.org/0000-0001-5482-3932
                Article
                Publisher ID: btz305
                DOI: 10.1093/bioinformatics/btz305
                PMC ID: 6821337
                PubMed ID: 31070718
                SO-VID: dfa6c112-aa29-45be-b4d4-4bef057db91c
                Copyright © © The Author(s) 2019. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Date received : 22 October 2018
                Date revision received : 16 April 2019
                Date accepted : 24 April 2019
                Page count
                Pages: 3
                Funding
                Funded by: Klaus Tschira Foundation 10.13039/501100007316
                Categories
                Subject: Applications Notes
                Subject: Phylogenetics

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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