RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies.
TL;DR: This work presents some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees.
Abstract: Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting postanalyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU
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TL;DR: The phylogenetic analysis suggests that bats might be the original host of this virus, an animal sold at the seafood market in Wuhan might represent an intermediate host facilitating the emergence of the virus in humans.
9,474 citations
TL;DR: An integrated database, called EzBioCloud, that holds the taxonomic hierarchy of the Bacteria and Archaea, which is represented by quality-controlled 16S rRNA gene and genome sequences, with accompanying bioinformatics tools.
Abstract: The recent advent of DNA sequencing technologies facilitates the use of genome sequencing data that provide means for more informative and precise classification and identification of members of the Bacteria and Archaea. Because the current species definition is based on the comparison of genome sequences between type and other strains in a given species, building a genome database with correct taxonomic information is of paramount need to enhance our efforts in exploring prokaryotic diversity and discovering novel species as well as for routine identifications. Here we introduce an integrated database, called EzBioCloud, that holds the taxonomic hierarchy of the Bacteria and Archaea, which is represented by quality-controlled 16S rRNA gene and genome sequences. Whole-genome assemblies in the NCBI Assembly Database were screened for low quality and subjected to a composite identification bioinformatics pipeline that employs gene-based searches followed by the calculation of average nucleotide identity. As a result, the database is made of 61 700 species/phylotypes, including 13 132 with validly published names, and 62 362 whole-genome assemblies that were identified taxonomically at the genus, species and subspecies levels. Genomic properties, such as genome size and DNA G+C content, and the occurrence in human microbiome data were calculated for each genus or higher taxa. This united database of taxonomy, 16S rRNA gene and genome sequences, with accompanying bioinformatics tools, should accelerate genome-based classification and identification of members of the Bacteria and Archaea. The database and related search tools are available at www.ezbiocloud.net/.
5,027 citations
Cites methods from "RAxML version 8: a tool for phyloge..."
...Maximum-likelihood phylogenetic trees of each taxonomic group, such as phyla, classes, orders or families, were generated from manually aligned 16S rRNA gene sequences using RAxML software [14]....
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TL;DR: Some notable features of IQ-TREE version 2 are described and the key advantages over other software are highlighted.
Abstract: IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
4,337 citations
Cites result from "RAxML version 8: a tool for phyloge..."
...For example, optimization of the model parameters for the LG4X model on the AA-data set took 1.9 min in IQ-TREE 2, 3 min in RAxML-NG, and 17.6 min in PhyML-mixtures....
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...We compared our scheduling approach with the program ParGenes version 1.0.1 (Morel et al. 2019), which uses RAxMLNG for tree search and a more sophisticated scheduling algorithm....
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...In addition to implementing existing mixture models, IQ-TREE 2 goes beyond the mixture models employed in PhyML-mixtures (Le et al. 2008) and RAxML-NG software (Kozlov et al. 2019), by allowing for user-defined mixture models using the “MIXfmodel1,. ....
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...The speed of FastTree2 was accomplished at the cost of producing substantially worse trees than RAxML, PhyML, and IQ-TREE (Zhou et al. 2018)....
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...IQ-TREE 2 exploits partial terraces to improve tree search under partitioned models and achieves up to 4.5- and 8-fold speedups compared with IQ-TREE 1 and RAxML, respectively (Chernomor et al. 2016)....
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TL;DR: An r package, ggtree, which provides programmable visualization and annotation of phylogenetic trees, which can read more tree file formats than other softwares, and support visualization of phylo, multiphylo, phylo4, phyla4d, obkdata and phyloseq tree objects defined in other r packages.
Abstract: Summary
We present an r package, ggtree, which provides programmable visualization and annotation of phylogenetic trees.
ggtree can read more tree file formats than other softwares, including newick, nexus, NHX, phylip and jplace formats, and support visualization of phylo, multiphylo, phylo4, phylo4d, obkdata and phyloseq tree objects defined in other r packages. It can also extract the tree/branch/node-specific and other data from the analysis outputs of beast, epa, hyphy, paml, phylodog, pplacer, r8s, raxml and revbayes software, and allows using these data to annotate the tree.
The package allows colouring and annotation of a tree by numerical/categorical node attributes, manipulating a tree by rotating, collapsing and zooming out clades, highlighting user selected clades or operational taxonomic units and exploration of a large tree by zooming into a selected portion.
A two-dimensional tree can be drawn by scaling the tree width based on an attribute of the nodes. A tree can be annotated with an associated numerical matrix (as a heat map), multiple sequence alignment, subplots or silhouette images.
The package ggtree is released under the artistic-2.0 license. The source code and documents are freely available through bioconductor (http://www.bioconductor.org/packages/ggtree).
2,692 citations
Cites methods from "RAxML version 8: a tool for phyloge..."
...It can also extract the tree/branch/node-specific and other data from the analysis outputs of BEAST, EPA, HYPHY, PAML, PHYLODOG, PPLACER, R8S, RAXML and REVBAYES software, and allows using these data to annotate the tree....
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...Non-standard analysis output files from various evolutionary biology software packages including BEAST (Bouckaert et al. 2014), EPA (Berger, Krompass& Stamatakis 2011), HYPHY (Pond, Frost & Muse 2005), PAML (Yang 2007), PHYLODOG (Bastien et al. 2013), PPLACER (Matsen, Kodner & Armbrust 2010), RAXML (Stamatakis 2014), REVBAYES (Sebastian et al. 2014) and R8S (Sanderson 2003) (Table 1) can also be parsed into S4 objects using functions read.beast, read.codeml_mlc, read.codeml, read.hyphy, read.jplace, read.nhx, read.paml_rst, read.phylip, read.raxml and read.r8s (Fig....
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...Computer programs and R packages for molecular evolutionary analyses in which their specific data outputs can be directly parsed by GGTREE Programs Data that can be parsed APE (R package) Bootstrap values BEAST Any information (e.g. substitution rates, node ages, geographic states) stored in the node attributes in the nexus-formatted tree file PAML-BASEML Ancestral sequences (from rst file) PAML-CODEML Ancestral sequences (from rst file) dN, dS andx estimates (frommlc file) HYPHY Ancestral sequences (from the nexus-formatted tree file) PHANGORN (R package) Ancestral sequences RAXML Branch support values R8S Tree with branch in unit of time, rate and absolute substitution PPLACER Taxon placement information from jplace- formatted tree file EPA Taxon placement information from jplace- formatted tree file PHYLODOG Any information from theNHX-formatted tree file REVBAYES Any information from theNHX-formatted tree file © 2016 The Authors....
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...…2011), HYPHY (Pond, Frost & Muse 2005), PAML (Yang 2007), PHYLODOG (Bastien et al. 2013), PPLACER (Matsen, Kodner & Armbrust 2010), RAXML (Stamatakis 2014), REVBAYES (Sebastian et al. 2014) and R8S (Sanderson 2003) (Table 1) can also be parsed into S4 objects using functions read.beast,…...
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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.
Abstract: This article presents W-IQ-TREE, an intuitive and user-friendly web interface and server for IQ-TREE, an efficient phylogenetic software for maximum likelihood analysis. W-IQ-TREE supports multiple sequence types (DNA, protein, codon, binary and morphology) in common alignment formats and a wide range of evolutionary models including mixture and partition models. W-IQ-TREE performs fast model selection, partition scheme finding, efficient tree reconstruction, ultrafast bootstrapping, branch tests, and tree topology tests. All computations are conducted on a dedicated computer cluster and the users receive the results via URL or email. W-IQ-TREE is available at http://iqtree.cibiv.univie.ac.at It is free and open to all users and there is no login requirement.
2,488 citations
References
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TL;DR: UNLABELLED RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML) that has been used to compute ML trees on two of the largest alignments to date.
Abstract: Summary: RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Γ yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets ≥4000 taxa it also runs 2--3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25 057 (1463 bp) and 2182 (51 089 bp) taxa, respectively.
Availability: icwww.epfl.ch/~stamatak
Contact: Alexandros.Stamatakis@epfl.ch
Supplementary information: Supplementary data are available at Bioinformatics online.
14,847 citations
"RAxML version 8: a tool for phyloge..." refers background or methods in this paper
...Since the last RAxML paper (Stamatakis, 2006), it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community....
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...RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analysis of large datasets under maximum likelihood....
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...RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogen- etic analyses of large datasets under maximum likelihood....
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TL;DR: A new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves and a new test to assess the support of the data for internal branches of a phylogeny are introduced.
Abstract: PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
14,385 citations
"RAxML version 8: a tool for phyloge..." refers background in this paper
...Since the last RAxML paper (Stamatakis, 2006), it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community....
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TL;DR: This work developed, implemented, and thoroughly tested rapid bootstrap heuristics in RAxML (Randomized Axelerated Maximum Likelihood) that are more than an order of magnitude faster than current algorithms and can contribute to resolving the computational bottleneck and improve current methodology in phylogenetic analyses.
Abstract: Despite recent advances achieved by application of high-performance computing methods and novel algorithmic techniques to maximum likelihood (ML)-based inference programs, the major computational bottleneck still consists in the computation of bootstrap support values. Conducting a probably insufficient number of 100 bootstrap (BS) analyses with current ML programs on large datasets—either with respect to the number of taxa or base pairs—can easily require a month of run time. Therefore, we have developed, implemented, and thoroughly tested rapid bootstrap heuristics in RAxML (Randomized Axelerated Maximum Likelihood) that are more than an order of magnitude faster than current algorithms. These new heuristics can contribute to resolving the computational bottleneck and improve current methodology in phylogenetic analyses. Computational experiments to assess the performance and relative accuracy of these heuristics were conducted on 22 diverse DNA and AA (amino acid), single gene as well as multigene, real-world alignments containing 125 up to 7764 sequences. The standard BS (SBS) and rapid BS (RBS) values drawn on the best-scoring ML tree are highly correlated and show almost identical average support values. The weighted RF (Robinson-Foulds) distance between SBS- and RBS-based consensus trees was smaller than 6% in all cases (average 4%). More importantly, RBS inferences are between 8 and 20 times faster (average 14.73) than SBS analyses with RAxML and between 18 and 495 times faster than BS analyses with competing programs, such as PHYML or GARLI. Moreover, this performance improvement increases with alignment size. Finally, we have set up two freely accessible Web servers for this significantly improved version of RAxML that provide access to the 200-CPU cluster of the Vital-IT unit at the Swiss Institute of Bioinformatics and the 128-CPU cluster of the CIPRES project at the San Diego Supercomputer Center. These Web servers offer the possibility to conduct large-scale phylogenetic inferences to a large part of the community that does not have access to, or the expertise to use, high-performance computing resources. (Maximum likelihood; phylogenetic inference; rapid bootstrap; RAxML; support values.)
6,585 citations
"RAxML version 8: a tool for phyloge..." refers background in this paper
...Its major strength is a fast maximum likelihood tree search algorithm that returns trees with good likelihood scores....
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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.
Abstract: Nonparametric bootstrap has been a widely used tool in phylogenetic analysis to assess the clade support of phylogenetic trees. However, with the rapidly growing amount of data, this task remains a computational bottleneck. Recently, approximation methods such as the RAxML rapid bootstrap (RBS) and the Shimodaira-Hasegawa-like approximate likelihood ratio test have been introduced to speed up the bootstrap. Here, we suggest an ultrafast bootstrap approximation approach (UFBoot) to compute the support of phylogenetic groups in maximum likelihood (ML) based trees. To achieve this, we combine the resampling estimated log-likelihood method with a simple but effective collection scheme of candidate trees. We also propose a stopping rule that assesses the convergence of branch support values to automatically determine when to stop collecting candidate trees. UFBoot achieves a median speed up of 3.1 (range: 0.66-33.3) to 10.2 (range: 1.32-41.4) compared with RAxML RBS for real DNA and amino acid alignments, respectively. Moreover, our extensive simulations show that UFBoot is robust against moderate model violations and the support values obtained appear to be relatively unbiased compared with the conservative standard bootstrap. This provides a more direct interpretation of the bootstrap support. We offer an efficient and easy-to-use software (available at http://www.cibiv.at/software/iqtree) to perform the UFBoot analysis with ML tree inference.
2,469 citations
"RAxML version 8: a tool for phyloge..." refers background in this paper
...In the following, I will present some of the most notable new features and extensions of RAxML....
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TL;DR: Several new avenues of research are opened by an explicitly model-based approach to phylogenetic analysis of discrete morphological data, including combined-data likelihood analyses (morphology + sequence data), likelihood ratio tests, and Bayesian analyses.
Abstract: Evolutionary biologists have adopted simplelikelihood models for purposes of estimating ancestral states and evaluating character independence on specieed phylogenies; however, for pur- poses of estimating phylogenies byusing discrete morphological data, maximum parsimony remains the only option. This paper explores the possibility of using standard, well-behaved Markov models for estimating morphological phylogenies (including branch lengths) under the likelihood criterion. AnimportantmodiecationofstandardMarkovmodelsinvolvesmakingthelikelihoodconditionalon characters being variable, because constant characters are absent in morphological data sets. Without this modiecation, branch lengths are often overestimated, resulting in potentially serious biases in tree topology selection. Several new avenues of research are opened by an explicitly model-based approach to phylogenetic analysis of discrete morphological data, including combined-data likeli- hood analyses (morphologyCsequence data), likelihood ratio tests, and Bayesian analyses. (Discrete morphological character; Markov model; maximum likelihood; phylogeny.) The increased availability of nucleotide and protein sequences from a diversity of both organisms and genes has stimu- lated the development of stochastic models describing evolutionary change in molecu- lar sequences over time. Such models are not only useful for estimating molecular evolutionary parameters of interest but also important as the basis for phylogenetic inference using the method of maximum likelihood (ML) and Bayesian inference. ML provides a very general framework for esti- mation and has been extensively applied in diverse eelds of science (Casella and Berger, 1990); however, the popularity of ML in phylogenetic inference has lagged behind thatofotheroptimality criteria(suchas max- imum parsimony), primarily because of its much greater computational cost for evalu- ating any givencandidate tree.Recent devel- opments on the algorithmic aspects of ML inference as applied to phylogeny recon- struction (Olsen et al., 1994; Lewis, 1998; Salter and Pearl, 2001; Swofford, 2001) have succeeded in reducing this computational cost substantially, and ML phylogeny esti- mates involving hundreds of terminal taxa are now entering the realm of feasibility. Bayesian methods (based on a likelihood foundation) offer the prospect of obtaining meaningful nodal support measures with- out the unreasonable computational burden imposed by existing methods such as boot- strapping (Rannala and Yang, 1996; Yang and Rannala, 1997; Larget and Simon, 1999;
2,351 citations
"RAxML version 8: a tool for phyloge..." refers background in this paper
...It can correct for ascertainment bias (Lewis, 2001) for all of the above data types....
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