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 discovery of multiple lineages of pangolin coronavirus and their similarity to SARS-CoV-2 suggests that pangolins should be considered as possible hosts in the emergence of new coronaviruses and should be removed from wet markets to prevent zoonotic transmission.
Abstract: The ongoing outbreak of viral pneumonia in China and across the world is associated with a new coronavirus, SARS-CoV-21. This outbreak has been tentatively associated with a seafood market in Wuhan, China, where the sale of wild animals may be the source of zoonotic infection2. Although bats are probable reservoir hosts for SARS-CoV-2, the identity of any intermediate host that may have facilitated transfer to humans is unknown. Here we report the identification of SARS-CoV-2-related coronaviruses in Malayan pangolins (Manis javanica) seized in anti-smuggling operations in southern China. Metagenomic sequencing identified pangolin-associated coronaviruses that belong to two sub-lineages of SARS-CoV-2-related coronaviruses, including one that exhibits strong similarity in the receptor-binding domain to SARS-CoV-2. The discovery of multiple lineages of pangolin coronavirus and their similarity to SARS-CoV-2 suggests that pangolins should be considered as possible hosts in the emergence of new coronaviruses and should be removed from wet markets to prevent zoonotic transmission.
1,461 citations
TL;DR: The results suggest that the development of new variations in functional sites in the receptor-binding domain (RBD) of the spike seen in SARS-CoV-2 and viruses from pangolin SARSr-CoVs are likely caused by natural selection besides recombination.
Abstract: The SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China, and has since impacted a large portion of China and raised major global concern. Herein, we investigated the extent of molecular divergence between SARS-CoV-2 and other related coronaviruses. Although we found only 4% variability in genomic nucleotides between SARS-CoV-2 and a bat SARS-related coronavirus (SARSr-CoV; RaTG13), the difference at neutral sites was 17%, suggesting the divergence between the two viruses is much larger than previously estimated. Our results suggest that the development of new variations in functional sites in the receptor-binding domain (RBD) of the spike seen in SARS-CoV-2 and viruses from pangolin SARSr-CoVs are likely caused by natural selection besides recombination. Population genetic analyses of 103 SARS-CoV-2 genomes indicated that these viruses had two major lineages (designated L and S), that are well defined by two different SNPs that show nearly complete linkage across the viral strains sequenced to date. We found that L lineage was more prevalent than the S lineage within the limited patient samples we examined. The implication of these evolutionary changes on disease etiology remains unclear. These findings strongly underscores the urgent need for further comprehensive studies that combine viral genomic data, with epidemiological studies of coronavirus disease 2019 (COVID-19).
1,369 citations
Cites methods from "RAxML version 8: a tool for phyloge..."
...12 [47] was used to build the maximum likelihood phylogenetic tree of 103 aligned SARS-CoV-2...
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TL;DR: This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted genes trees, gene duplication events, the rooted species tree, and comparative genomic statistics.
Abstract: Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted genes trees, gene duplication events, the rooted species tree, and comparative genomic statistics. Each output is benchmarked on appropriate real or simulated datasets and, where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.
1,366 citations
TL;DR: ASTRAL-III is a faster version of the ASTRAL method for phylogenetic reconstruction and can scale up to 10,000 species and removes low support branches from gene trees, resulting in improved accuracy.
Abstract: Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions. We introduce ASTRAL-III, which substantially improves the running time of ASTRAL-II and guarantees polynomial running time as a function of both the number of species (n) and the number of genes (k). ASTRAL-III limits the bipartition constraint set (X) to grow at most linearly with n and k. Moreover, it handles polytomies more efficiently than ASTRAL-II, exploits similarities between gene trees better, and uses several techniques to avoid searching parts of the search space that are mathematically guaranteed not to include the optimal tree. The asymptotic running time of ASTRAL-III in the presence of polytomies is $O\left ((nk)^{1.726} D \right)$
where D=O(nk) is the sum of degrees of all unique nodes in input trees. The running time improvements enable us to test whether contracting low support branches in gene trees improves the accuracy by reducing noise. In extensive simulations, we show that removing branches with very low support (e.g., below 10%) improves accuracy while overly aggressive filtering is harmful. We observe on a biological avian phylogenomic dataset of 14K genes that contracting low support branches greatly improve results. ASTRAL-III is a faster version of the ASTRAL method for phylogenetic reconstruction and can scale up to 10,000 species. With ASTRAL-III, low support branches can be removed, resulting in improved accuracy.
1,261 citations
Cites methods from "RAxML version 8: a tool for phyloge..."
...The gene tree should leave the relationship between identical sequences unresolved (FastTree [32] automatically does it and RAxML, which outputs an arbitrary resolution, warns the user about the input)....
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...Gene trees are estimated using RAxML [41] with 200 replicates of bootstrapping....
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University of Birmingham1, Bernhard Nocht Institute for Tropical Medicine2, Ontario Institute for Cancer Research3, University of Toronto4, Public Health England5, European Centre for Disease Prevention and Control6, University of Edinburgh7, Robert Koch Institute8, Swiss Tropical and Public Health Institute9, University College London10, Paul Ehrlich Institute11, University of Liverpool12, Rega Institute for Medical Research13, Kenya Medical Research Institute14, Friedrich Loeffler Institute15, Janssen-Cilag16, Technische Universität München17, Public Health Agency of Canada18, Pasteur Institute19, Sandia National Laboratories20, MRIGlobal21, World Health Organization22, University of London23, Norwegian Institute of Public Health24, Defence Science and Technology Laboratory25, Bundeswehr Institute of Microbiology26, National Institutes of Health27
TL;DR: This paper presents sequence data and analysis of 142 EBOV samples collected during the period March to October 2015 and shows that real-time genomic surveillance is possible in resource-limited settings and can be established rapidly to monitor outbreaks.
Abstract: A nanopore DNA sequencer is used for real-time genomic surveillance of the Ebola virus epidemic in the field in Guinea; the authors demonstrate that it is possible to pack a genomic surveillance laboratory in a suitcase and transport it to the field for on-site virus sequencing, generating results within 24 hours of sample collection. This paper reports the use of nanopore DNA sequencers (known as MinIONs) for real-time genomic surveillance of the Ebola virus epidemic, in the field in Guinea. The authors demonstrate that it is possible to pack a genomic surveillance laboratory in a suitcase and transport it to the field for on-site virus sequencing, generating results within 24 hours of sample collection. The Ebola virus disease epidemic in West Africa is the largest on record, responsible for over 28,599 cases and more than 11,299 deaths1. Genome sequencing in viral outbreaks is desirable to characterize the infectious agent and determine its evolutionary rate. Genome sequencing also allows the identification of signatures of host adaptation, identification and monitoring of diagnostic targets, and characterization of responses to vaccines and treatments. The Ebola virus (EBOV) genome substitution rate in the Makona strain has been estimated at between 0.87 × 10−3 and 1.42 × 10−3 mutations per site per year. This is equivalent to 16–27 mutations in each genome, meaning that sequences diverge rapidly enough to identify distinct sub-lineages during a prolonged epidemic2,3,4,5,6,7. Genome sequencing provides a high-resolution view of pathogen evolution and is increasingly sought after for outbreak surveillance. Sequence data may be used to guide control measures, but only if the results are generated quickly enough to inform interventions8. Genomic surveillance during the epidemic has been sporadic owing to a lack of local sequencing capacity coupled with practical difficulties transporting samples to remote sequencing facilities9. To address this problem, here we devise a genomic surveillance system that utilizes a novel nanopore DNA sequencing instrument. In April 2015 this system was transported in standard airline luggage to Guinea and used for real-time genomic surveillance of the ongoing epidemic. We present sequence data and analysis of 142 EBOV samples collected during the period March to October 2015. We were able to generate results less than 24 h after receiving an Ebola-positive sample, with the sequencing process taking as little as 15–60 min. We show that real-time genomic surveillance is possible in resource-limited settings and can be established rapidly to monitor outbreaks.
1,187 citations
Cites background from "RAxML version 8: a tool for phyloge..."
...England, Porton Down, Wiltshire SP4 0JG, UK (12)Friedrich-Loeffler-Institute, Greifswald, Germany (13)Spiez Laboratory, Spiez, Switzerland (14)Janssen-Cilag, Stockholm, Sweden (15)Public Health Agency of Canada, Winnipeg, Canada (16)Institut Pasteur Dakar, Dakar, Senegal (17)Laboratoire de Fièvres Hémorragiques de Guinée, Conakry, Guinea (18)Sandia National Laboratories, Albuquerque, New Mexico, USA (19)Ratoma Ebola Diagnostic Center, Conakry, Guinea (20)MRIGlobal, Kansas City, USA (21)Expertise France, Laboratoire K-plan de Forecariah en Guinée, Paris, France (22)Fédération des Laboratoires - HIA Bégin, Paris, France (23)Laboratoire de Biologie - Centre de Traitement des Soignants, Conakry, Guinée (24)World Health Organization, Conakry, Guinea (25)Institut National de Santé Publique, Conakry, Guinea (26)Ministry of Health Guinea, Conakry, Guinea (27)Defence Science and Technology Laboratory (Dstl) Porton Down, Salisbury SP4 0JQ, UK (28)Oxford Nanopore Technologies, Oxford, UK (29)Ontario Institute for Cancer Research, Toronto, Canada (30)Department of Cellular and Molecular Medicine, School of Medical Sciences, University of Bristol, Bristol BS8 1TD, UK (31)National Institute for Infectious Diseases L....
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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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