1
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Wong Y, Ignatieva A, Koskela J, Gorjanc G, Wohns AW, Kelleher J. A general and efficient representation of ancestral recombination graphs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.03.565466. [PMID: 37961279 PMCID: PMC10635123 DOI: 10.1101/2023.11.03.565466] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. This approach is out of step with modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalises these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.
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Affiliation(s)
- Yan Wong
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - Anastasia Ignatieva
- School of Mathematics and Statistics, University of Glasgow, UK
- Department of Statistics, University of Oxford, UK
| | - Jere Koskela
- School of Mathematics, Statistics and Physics, Newcastle University, UK
- Department of Statistics, University of Warwick, UK
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, UK
| | - Anthony W Wohns
- Broad Institute of MIT and Harvard, Cambridge, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Jerome Kelleher
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
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2
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Brandt DYC, Huber CD, Chiang CWK, Ortega-Del Vecchyo D. The Promise of Inferring the Past Using the Ancestral Recombination Graph. Genome Biol Evol 2024; 16:evae005. [PMID: 38242694 PMCID: PMC10834162 DOI: 10.1093/gbe/evae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 01/21/2024] Open
Abstract
The ancestral recombination graph (ARG) is a structure that represents the history of coalescent and recombination events connecting a set of sequences (Hudson RR. In: Futuyma D, Antonovics J, editors. Gene genealogies and the coalescent process. In: Oxford Surveys in Evolutionary Biology; 1991. p. 1 to 44.). The full ARG can be represented as a set of genealogical trees at every locus in the genome, annotated with recombination events that change the topology of the trees between adjacent loci and the mutations that occurred along the branches of those trees (Griffiths RC, Marjoram P. An ancestral recombination graph. In: Donnelly P, Tavare S, editors. Progress in population genetics and human evolution. Springer; 1997. p. 257 to 270.). Valuable insights can be gained into past evolutionary processes, such as demographic events or the influence of natural selection, by studying the ARG. It is regarded as the "holy grail" of population genetics (Hubisz M, Siepel A. Inference of ancestral recombination graphs using ARGweaver. In: Dutheil JY, editors. Statistical population genomics. New York, NY: Springer US; 2020. p. 231-266.) since it encodes the processes that generate all patterns of allelic and haplotypic variation from which all commonly used summary statistics in population genetic research (e.g. heterozygosity and linkage disequilibrium) can be derived. Many previous evolutionary inferences relied on summary statistics extracted from the genotype matrix. Evolutionary inferences using the ARG represent a significant advancement as the ARG is a representation of the evolutionary history of a sample that shows the past history of recombination, coalescence, and mutation events across a particular sequence. This representation in theory contains as much information, if not more, than the combination of all independent summary statistics that could be derived from the genotype matrix. Consistent with this idea, some of the first ARG-based analyses have proven to be more powerful than summary statistic-based analyses (Speidel L, Forest M, Shi S, Myers SR. A method for genome-wide genealogy estimation for thousands of samples. Nat Genet. 2019:51(9):1321 to 1329.; Stern AJ, Wilton PR, Nielsen R. An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data. PLoS Genet. 2019:15(9):e1008384.; Hubisz MJ, Williams AL, Siepel A. Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph. PLoS Genet. 2020:16(8):e1008895.; Fan C, Mancuso N, Chiang CWK. A genealogical estimate of genetic relationships. Am J Hum Genet. 2022:109(5):812-824.; Fan C, Cahoon JL, Dinh BL, Ortega-Del Vecchyo D, Huber C, Edge MD, Mancuso N, Chiang CWK. A likelihood-based framework for demographic inference from genealogical trees. bioRxiv. 2023.10.10.561787. 2023.; Hejase HA, Mo Z, Campagna L, Siepel A. A deep-learning approach for inference of selective sweeps from the ancestral recombination graph. Mol Biol Evol. 2022:39(1):msab332.; Link V, Schraiber JG, Fan C, Dinh B, Mancuso N, Chiang CWK, Edge MD. Tree-based QTL mapping with expected local genetic relatedness matrices. bioRxiv. 2023.04.07.536093. 2023.; Zhang BC, Biddanda A, Gunnarsson ÁF, Cooper F, Palamara PF. Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits. Nat Genet. 2023:55(5):768-776.). As such, there has been significant interest in the field to investigate 2 main problems related to the ARG: (i) How can we estimate the ARG based on genomic data, and (ii) how can we extract information of past evolutionary processes from the ARG? In this perspective, we highlight 3 topics that pertain to these main issues: The development of computational innovations that enable the estimation of the ARG; remaining challenges in estimating the ARG; and methodological advances for deducing evolutionary forces and mechanisms using the ARG. This perspective serves to introduce the readers to the types of questions that can be explored using the ARG and to highlight some of the most pressing issues that must be addressed in order to make ARG-based inference an indispensable tool for evolutionary research.
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Affiliation(s)
- Débora Y C Brandt
- Department of Genetics Evolution and Environment, University College London, London, UK
| | - Christian D Huber
- Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Diego Ortega-Del Vecchyo
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma De México, Querétaro, Querétaro, Mexico
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3
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Esquivel Gomez LR, Weber A, Kocher A, Kühnert D. Recombination-aware phylogenetic analysis sheds light on the evolutionary origin of SARS-CoV-2. Sci Rep 2024; 14:541. [PMID: 38177346 PMCID: PMC10766966 DOI: 10.1038/s41598-023-50952-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/28/2023] [Indexed: 01/06/2024] Open
Abstract
SARS-CoV-2 can infect human cells through the recognition of the human angiotensin-converting enzyme 2 receptor. This affinity is given by six amino acid residues located in the variable loop of the receptor binding domain (RBD) within the Spike protein. Genetic recombination involving bat and pangolin Sarbecoviruses, and natural selection have been proposed as possible explanations for the acquisition of the variable loop and these amino acid residues. In this study we employed Bayesian phylogenetics to jointly reconstruct the phylogeny of the RBD among human, bat and pangolin Sarbecoviruses and detect recombination events affecting this region of the genome. A recombination event involving RaTG13, the closest relative of SARS-CoV-2 that lacks five of the six residues, and an unsampled Sarbecovirus lineage was detected. This result suggests that the variable loop of the RBD didn't have a recombinant origin and the key amino acid residues were likely present in the common ancestor of SARS-CoV-2 and RaTG13, with the latter losing five of them probably as the result of recombination.
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Affiliation(s)
- Luis Roger Esquivel Gomez
- Transmission, Infection, Diversification and Evolution Group (tide), Max Planck Institute of Geoanthropology (Formerly MPI for the Science of Human History), Jena, Germany.
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
- Phylogenomics Unit, Center for Artificial Intelligence in Public Health Research, Robert Koch Institute, Wildau, Germany.
| | - Ariane Weber
- Transmission, Infection, Diversification and Evolution Group (tide), Max Planck Institute of Geoanthropology (Formerly MPI for the Science of Human History), Jena, Germany
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Arthur Kocher
- Transmission, Infection, Diversification and Evolution Group (tide), Max Planck Institute of Geoanthropology (Formerly MPI for the Science of Human History), Jena, Germany
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Denise Kühnert
- Transmission, Infection, Diversification and Evolution Group (tide), Max Planck Institute of Geoanthropology (Formerly MPI for the Science of Human History), Jena, Germany.
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
- Phylogenomics Unit, Center for Artificial Intelligence in Public Health Research, Robert Koch Institute, Wildau, Germany.
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4
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Lewanski AL, Grundler MC, Bradburd GS. The era of the ARG: An introduction to ancestral recombination graphs and their significance in empirical evolutionary genomics. PLoS Genet 2024; 20:e1011110. [PMID: 38236805 PMCID: PMC10796009 DOI: 10.1371/journal.pgen.1011110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2024] Open
Abstract
In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. Instead, the genomes are related by a complex, interwoven collection of genealogies formalized in a structure called an ancestral recombination graph (ARG). An ARG extensively encodes the ancestry of the genome(s) and thus is replete with valuable information for addressing diverse questions in evolutionary biology. Despite its potential utility, technological and methodological limitations, along with a lack of approachable literature, have severely restricted awareness and application of ARGs in evolution research. Excitingly, recent progress in ARG reconstruction and simulation have made ARG-based approaches feasible for many questions and systems. In this review, we provide an accessible introduction and exploration of ARGs, survey recent methodological breakthroughs, and describe the potential for ARGs to further existing goals and open avenues of inquiry that were previously inaccessible in evolutionary genomics. Through this discussion, we aim to more widely disseminate the promise of ARGs in evolutionary genomics and encourage the broader development and adoption of ARG-based inference.
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Affiliation(s)
- Alexander L. Lewanski
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, United States of America
- W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, Michigan, United States of America
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, United States of America
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Michael C. Grundler
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Gideon S. Bradburd
- W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, Michigan, United States of America
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
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5
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Lewanski AL, Grundler MC, Bradburd GS. The era of the ARG: an empiricist's guide to ancestral recombination graphs. ARXIV 2023:arXiv:2310.12070v1. [PMID: 37904740 PMCID: PMC10614969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. Instead, the genomes are related by a complex, interwoven collection of genealogies formalized in a structure called an ancestral recombination graph (ARG). An ARG extensively encodes the ancestry of the genome(s) and thus is replete with valuable information for addressing diverse questions in evolutionary biology. Despite its potential utility, technological and methodological limitations, along with a lack of approachable literature, have severely restricted awareness and application of ARGs in empirical evolution research. Excitingly, recent progress in ARG reconstruction and simulation have made ARG-based approaches feasible for many questions and systems. In this review, we provide an accessible introduction and exploration of ARGs, survey recent methodological breakthroughs, and describe the potential for ARGs to further existing goals and open avenues of inquiry that were previously inaccessible in evolutionary genomics. Through this discussion, we aim to more widely disseminate the promise of ARGs in evolutionary genomics and encourage the broader development and adoption of ARG-based inference.
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Affiliation(s)
- Alexander L Lewanski
- Department of Integrative Biology, Michigan State University, East Lansing, MI, US
- W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI, US
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, US
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, US
| | - Michael C Grundler
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, US
| | - Gideon S Bradburd
- W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI, US
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, US
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6
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Campbell AM, Hauton C, Baker-Austin C, van Aerle R, Martinez-Urtaza J. An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens. Curr Opin Biotechnol 2023; 80:102898. [PMID: 36739640 DOI: 10.1016/j.copbio.2023.102898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/22/2022] [Accepted: 01/02/2023] [Indexed: 02/05/2023]
Abstract
It is critical to gain insight into how climate change impacts evolutionary responses within climate-sensitive pathogen populations, such as increased resilience, opportunistic responses and the emergence of dominant variants from highly variable genomic backgrounds and subsequent global dispersal. This review proposes a framework to support such analysis, by combining genomic evolutionary analysis with climate time-series data in a novel spatiotemporal dataframe for use within machine learning applications, to understand past and future evolutionary pathogen responses to climate change. Recommendations are presented to increase the feasibility of interdisciplinary applications, including the importance of robust spatiotemporal metadata accompanying genome submission to databases. Such workflows will inform accessible public health tools and early-warning systems, to aid decision-making and mitigate future human health threats.
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Affiliation(s)
- Amy M Campbell
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK; Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Chris Hauton
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK
| | - Craig Baker-Austin
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Ronny van Aerle
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Jaime Martinez-Urtaza
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK; Department of Genetics and Microbiology, Autonomous University of Barcelona, Barcelona, Spain.
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7
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Didelot X. Phylogenetic Analysis of Bacterial Pathogen Genomes. Methods Mol Biol 2023; 2674:87-99. [PMID: 37258962 DOI: 10.1007/978-1-0716-3243-7_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The development of high-throughput sequencing technology has led to a significant reduction in the time and cost of sequencing whole genomes of bacterial pathogens. Studies can sequence and compare hundreds or even thousands of genomes within a given bacterial population. A phylogenetic tree is the most frequently used method of depicting the relationships between these bacterial pathogen genomes. However, the presence of homologous recombination in most bacterial pathogen species can invalidate the application of standard phylogenetic tools. Here we describe a method to produce phylogenetic analyses that accounts for the disruptive effect of recombination. This allows users to investigate the recombination events that have occurred, as well as to produce more meaningful phylogenetic analyses which recover the clonal genealogy representing the clonal relationships between genomes.
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Affiliation(s)
- Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry, UK.
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8
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Didelot X, Parkhill J. A scalable analytical approach from bacterial genomes to epidemiology. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210246. [PMID: 35989600 PMCID: PMC9393561 DOI: 10.1098/rstb.2021.0246] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 02/17/2022] [Indexed: 12/21/2022] Open
Abstract
Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.
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Affiliation(s)
- Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry CV4 7AL, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK
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9
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A Bayesian approach to infer recombination patterns in coronaviruses. Nat Commun 2022; 13:4186. [PMID: 35859071 PMCID: PMC9297283 DOI: 10.1038/s41467-022-31749-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
As shown during the SARS-CoV-2 pandemic, phylogenetic and phylodynamic methods are essential tools to study the spread and evolution of pathogens. One of the central assumptions of these methods is that the shared history of pathogens isolated from different hosts can be described by a branching phylogenetic tree. Recombination breaks this assumption. This makes it problematic to apply phylogenetic methods to study recombining pathogens, including, for example, coronaviruses. Here, we introduce a Markov chain Monte Carlo approach that allows inference of recombination networks from genetic sequence data under a template switching model of recombination. Using this method, we first show that recombination is extremely common in the evolutionary history of SARS-like coronaviruses. We then show how recombination rates across the genome of the human seasonal coronaviruses 229E, OC43 and NL63 vary with rates of adaptation. This suggests that recombination could be beneficial to fitness of human seasonal coronaviruses. Additionally, this work sets the stage for Bayesian phylogenetic tracking of the spread and evolution of SARS-CoV-2 in the future, even as recombinant viruses become prevalent. Genetic recombination can confound standard phylogenetic approaches. Here, the authors present a method to reconstruct virus recombination networks, and show the importance of recombination in shaping the ongoing evolution of SARS-like, MERS and 3 human seasonal coronaviruses.
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10
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Shikov AE, Malovichko YV, Nizhnikov AA, Antonets KS. Current Methods for Recombination Detection in Bacteria. Int J Mol Sci 2022; 23:ijms23116257. [PMID: 35682936 PMCID: PMC9181119 DOI: 10.3390/ijms23116257] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/30/2022] [Accepted: 05/30/2022] [Indexed: 02/05/2023] Open
Abstract
The role of genetic exchanges, i.e., homologous recombination (HR) and horizontal gene transfer (HGT), in bacteria cannot be overestimated for it is a pivotal mechanism leading to their evolution and adaptation, thus, tracking the signs of recombination and HGT events is importance both for fundamental and applied science. To date, dozens of bioinformatics tools for revealing recombination signals are available, however, their pros and cons as well as the spectra of solvable tasks have not yet been systematically reviewed. Moreover, there are two major groups of software. One aims to infer evidence of HR, while the other only deals with horizontal gene transfer (HGT). However, despite seemingly different goals, all the methods use similar algorithmic approaches, and the processes are interconnected in terms of genomic evolution influencing each other. In this review, we propose a classification of novel instruments for both HR and HGT detection based on the genomic consequences of recombination. In this context, we summarize available methodologies paying particular attention to the type of traceable events for which a certain program has been designed.
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Affiliation(s)
- Anton E. Shikov
- Laboratory for Proteomics of Supra-Organismal Systems, All-Russia Research Institute for Agricultural Microbiology (ARRIAM), 196608 St. Petersburg, Russia; (A.E.S.); (Y.V.M.); (A.A.N.)
- Faculty of Biology, St. Petersburg State University (SPbSU), 199034 St. Petersburg, Russia
| | - Yury V. Malovichko
- Laboratory for Proteomics of Supra-Organismal Systems, All-Russia Research Institute for Agricultural Microbiology (ARRIAM), 196608 St. Petersburg, Russia; (A.E.S.); (Y.V.M.); (A.A.N.)
- Faculty of Biology, St. Petersburg State University (SPbSU), 199034 St. Petersburg, Russia
| | - Anton A. Nizhnikov
- Laboratory for Proteomics of Supra-Organismal Systems, All-Russia Research Institute for Agricultural Microbiology (ARRIAM), 196608 St. Petersburg, Russia; (A.E.S.); (Y.V.M.); (A.A.N.)
- Faculty of Biology, St. Petersburg State University (SPbSU), 199034 St. Petersburg, Russia
| | - Kirill S. Antonets
- Laboratory for Proteomics of Supra-Organismal Systems, All-Russia Research Institute for Agricultural Microbiology (ARRIAM), 196608 St. Petersburg, Russia; (A.E.S.); (Y.V.M.); (A.A.N.)
- Faculty of Biology, St. Petersburg State University (SPbSU), 199034 St. Petersburg, Russia
- Correspondence:
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11
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Featherstone LA, Zhang JM, Vaughan TG, Duchene S. Epidemiological Inference From Pathogen Genomes: A Review of Phylodynamic Models and Applications. Virus Evol 2022; 8:veac045. [PMID: 35775026 PMCID: PMC9241095 DOI: 10.1093/ve/veac045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/02/2022] [Indexed: 11/24/2022] Open
Abstract
Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.
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Affiliation(s)
- Leo A Featherstone
- Peter Doherty Institute for Infection and Immunity, University of Melbourne , Australia
| | - Joshua M Zhang
- Peter Doherty Institute for Infection and Immunity, University of Melbourne , Australia
| | - Timothy G Vaughan
- Department of Biosystems Science and Engineering, ETH Zurich , Basel, Switzerland
- Swiss Institute of Bioinformatics
| | - Sebastian Duchene
- Peter Doherty Institute for Infection and Immunity, University of Melbourne , Australia
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12
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Müller NF, Kistler KE, Bedford T. Recombination patterns in coronaviruses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2021.04.28.441806. [PMID: 33948594 PMCID: PMC8095201 DOI: 10.1101/2021.04.28.441806] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
As shown during the SARS-CoV-2 pandemic, phylogenetic and phylodynamic methods are essential tools to study the spread and evolution of pathogens. One of the central assumptions of these methods is that the shared history of pathogens isolated from different hosts can be described by a branching phylogenetic tree. Recombination breaks this assumption. This makes it problematic to apply phylogenetic methods to study recombining pathogens, including, for example, coronaviruses. Here, we introduce a Markov chain Monte Carlo approach that allows inference of recombination networks from genetic sequence data under a template switching model of recombination. Using this method, we first show that recombination is extremely common in the evolutionary history of SARS-like coronaviruses. We then show how recombination rates across the genome of the human seasonal coronaviruses 229E, OC43 and NL63 vary with rates of adaptation. This suggests that recombination could be beneficial to fitness of human seasonal coronaviruses. Additionally, this work sets the stage for Bayesian phylogenetic tracking of the spread and evolution of SARS-CoV-2 in the future, even as recombinant viruses become prevalent.
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Affiliation(s)
- Nicola F. Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kathryn E. Kistler
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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13
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Abstract
The reconstruction of genetic material of ancestral organisms constitutes a powerful application of evolutionary biology. A fundamental step in this inference is the ancestral sequence reconstruction (ASR), which can be performed with diverse methodologies implemented in computer frameworks. However, most of these methodologies ignore evolutionary properties frequently observed in microbes, such as genetic recombination and complex selection processes, that can bias the traditional ASR. From a practical perspective, here I review methodologies for the reconstruction of ancestral DNA and protein sequences, with particular focus on microbes, and including biases, recommendations, and software implementations. I conclude that microbial ASR is a complex analysis that should be carefully performed and that there is a need for methods to infer more realistic ancestral microbial sequences.
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Affiliation(s)
- Miguel Arenas
- Biomedical Research Center (CINBIO), University of Vigo, Vigo, Spain.
- Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.
- Galicia Sur Health Research Institute (IIS Galicia Sur), Vigo, Spain.
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14
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Van Poelvoorde LAE, Bogaerts B, Fu Q, De Keersmaecker SCJ, Thomas I, Van Goethem N, Van Gucht S, Winand R, Saelens X, Roosens N, Vanneste K. Whole-genome-based phylogenomic analysis of the Belgian 2016-2017 influenza A(H3N2) outbreak season allows improved surveillance. Microb Genom 2021; 7. [PMID: 34477544 PMCID: PMC8715427 DOI: 10.1099/mgen.0.000643] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Seasonal influenza epidemics are associated with high mortality and morbidity in the human population. Influenza surveillance is critical for providing information to national influenza programmes and for making vaccine composition predictions. Vaccination prevents viral infections, but rapid influenza evolution results in emerging mutants that differ antigenically from vaccine strains. Current influenza surveillance relies on Sanger sequencing of the haemagglutinin (HA) gene. Its classification according to World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC) guidelines is based on combining certain genotypic amino acid mutations and phylogenetic analysis. Next-generation sequencing technologies enable a shift to whole-genome sequencing (WGS) for influenza surveillance, but this requires laboratory workflow adaptations and advanced bioinformatics workflows. In this study, 253 influenza A(H3N2) positive clinical specimens from the 2016–2017 Belgian season underwent WGS using the Illumina MiSeq system. HA-based classification according to WHO/ECDC guidelines did not allow classification of all samples. A new approach, considering the whole genome, was investigated based on using powerful phylogenomic tools including beast and Nextstrain, which substantially improved phylogenetic classification. Moreover, Bayesian inference via beast facilitated reassortment detection by both manual inspection and computational methods, detecting intra-subtype reassortants at an estimated rate of 15 %. Real-time analysis (i.e. as an outbreak is ongoing) via Nextstrain allowed positioning of the Belgian isolates into the globally circulating context. Finally, integration of patient data with phylogenetic groups and reassortment status allowed detection of several associations that would have been missed when solely considering HA, such as hospitalized patients being more likely to be infected with A(H3N2) reassortants, and the possibility to link several phylogenetic groups to disease severity indicators could be relevant for epidemiological monitoring. Our study demonstrates that WGS offers multiple advantages for influenza monitoring in (inter)national influenza surveillance, and proposes an improved methodology. This allows leveraging all information contained in influenza genomes, and allows for more accurate genetic characterization and reassortment detection.
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Affiliation(s)
- Laura A E Van Poelvoorde
- Transversal Activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium.,National Influenza Centre, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium.,Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Bert Bogaerts
- Transversal Activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium.,Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.,Department of Information Technology, IDLab, IMEC, Ghent University, Ghent, Belgium
| | - Qiang Fu
- Transversal Activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
| | | | - Isabelle Thomas
- National Influenza Centre, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
| | | | - Steven Van Gucht
- National Influenza Centre, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
| | - Raf Winand
- Transversal Activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
| | - Xavier Saelens
- Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Nancy Roosens
- Transversal Activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
| | - Kevin Vanneste
- Transversal Activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
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15
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Liu X, Ogilvie HA, Nakhleh L. Variational inference using approximate likelihood under the coalescent with recombination. Genome Res 2021; 31:2107-2119. [PMID: 34426513 PMCID: PMC8559707 DOI: 10.1101/gr.273631.120] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 08/17/2021] [Indexed: 11/30/2022]
Abstract
Coalescent methods are proven and powerful tools for population genetics, phylogenetics, epidemiology, and other fields. A promising avenue for the analysis of large genomic alignments, which are increasingly common, is coalescent hidden Markov model (coalHMM) methods, but these methods have lacked general usability and flexibility. We introduce a novel method for automatically learning a coalHMM and inferring the posterior distributions of evolutionary parameters using black-box variational inference, with the transition rates between local genealogies derived empirically by simulation. This derivation enables our method to work directly with three or four taxa and through a divide-and-conquer approach with more taxa. Using a simulated data set resembling a human–chimp–gorilla scenario, we show that our method has comparable or better accuracy to previous coalHMM methods. Both species divergence times and population sizes were accurately inferred. The method also infers local genealogies, and we report on their accuracy. Furthermore, we discuss a potential direction for scaling the method to larger data sets through a divide-and-conquer approach. This accuracy means our method is useful now, and by deriving transition rates by simulation, it is flexible enough to enable future implementations of various population models.
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Affiliation(s)
- Xinhao Liu
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
| | - Huw A Ogilvie
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
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16
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Hufsky F, Lamkiewicz K, Almeida A, Aouacheria A, Arighi C, Bateman A, Baumbach J, Beerenwinkel N, Brandt C, Cacciabue M, Chuguransky S, Drechsel O, Finn RD, Fritz A, Fuchs S, Hattab G, Hauschild AC, Heider D, Hoffmann M, Hölzer M, Hoops S, Kaderali L, Kalvari I, von Kleist M, Kmiecinski R, Kühnert D, Lasso G, Libin P, List M, Löchel HF, Martin MJ, Martin R, Matschinske J, McHardy AC, Mendes P, Mistry J, Navratil V, Nawrocki EP, O’Toole ÁN, Ontiveros-Palacios N, Petrov AI, Rangel-Pineros G, Redaschi N, Reimering S, Reinert K, Reyes A, Richardson L, Robertson DL, Sadegh S, Singer JB, Theys K, Upton C, Welzel M, Williams L, Marz M. Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research. Brief Bioinform 2021; 22:642-663. [PMID: 33147627 PMCID: PMC7665365 DOI: 10.1093/bib/bbaa232] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 07/28/2020] [Accepted: 08/26/2020] [Indexed: 12/16/2022] Open
Abstract
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Christian Brandt
- Institute of Infectious Disease and Infection Control at Jena University Hospital, Germany
| | - Marco Cacciabue
- Consejo Nacional de Investigaciones Científicas y Tócnicas (CONICET) working on FMDV virology at the Instituto de Agrobiotecnología y Biología Molecular (IABiMo, INTA-CONICET) and at the Departamento de Ciencias Básicas, Universidad Nacional de Luján (UNLu), Argentina
| | | | - Oliver Drechsel
- bioinformatics department at the Robert Koch-Institute, Germany
| | | | - Adrian Fritz
- Computational Biology of Infection Research group of Alice C. McHardy at the Helmholtz Centre for Infection Research, Germany
| | - Stephan Fuchs
- bioinformatics department at the Robert Koch-Institute, Germany
| | - Georges Hattab
- Bioinformatics Division at Philipps-University Marburg, Germany
| | | | - Dominik Heider
- Data Science in Biomedicine at the Philipps-University of Marburg, Germany
| | | | | | - Stefan Hoops
- Biocomplexity Institute and Initiative at the University of Virginia, USA
| | - Lars Kaderali
- Bioinformatics and head of the Institute of Bioinformatics at University Medicine Greifswald, Germany
| | | | - Max von Kleist
- bioinformatics department at the Robert Koch-Institute, Germany
| | - Renó Kmiecinski
- bioinformatics department at the Robert Koch-Institute, Germany
| | | | - Gorka Lasso
- Chandran Lab, Albert Einstein College of Medicine, USA
| | | | | | | | | | | | | | - Alice C McHardy
- Computational Biology of Infection Research Lab at the Helmholtz Centre for Infection Research in Braunschweig, Germany
| | - Pedro Mendes
- Center for Quantitative Medicine of the University of Connecticut School of Medicine, USA
| | | | - Vincent Navratil
- Bioinformatics and Systems Biology at the Rhône Alpes Bioinformatics core facility, Universitó de Lyon, France
| | | | | | | | | | | | - Nicole Redaschi
- Development of the Swiss-Prot group at the SIB for UniProt and SIB resources that cover viral biology (ViralZone)
| | - Susanne Reimering
- Computational Biology of Infection Research group of Alice C. McHardy at the Helmholtz Centre for Infection Research
| | | | | | | | | | - Sepideh Sadegh
- Chair of Experimental Bioinformatics at Technical University of Munich, Germany
| | - Joshua B Singer
- MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK
| | | | - Chris Upton
- Department of Biochemistry and Microbiology, University of Victoria, Canada
| | | | | | - Manja Marz
- Friedrich Schiller University Jena, Germany
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17
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Ingle DJ, Howden BP, Duchene S. Development of Phylodynamic Methods for Bacterial Pathogens. Trends Microbiol 2021; 29:788-797. [PMID: 33736902 DOI: 10.1016/j.tim.2021.02.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/13/2021] [Accepted: 02/15/2021] [Indexed: 11/30/2022]
Abstract
Phylodynamic methods have been essential to understand the interplay between the evolution and epidemiology of infectious diseases. To date, the field has centered on viruses. Bacterial pathogens are seldom analyzed under such phylodynamic frameworks, due to their complex genome evolution and, until recently, a paucity of whole-genome sequence data sets with rich associated metadata. We posit that the increasing availability of bacterial genomes and epidemiological data means that the field is now ripe to lay the foundations for applying phylodynamics to bacterial pathogens. The development of new methods that integrate more complex genomic and ecological data will help to inform public heath surveillance and control strategies for bacterial pathogens that represent serious threats to human health.
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Affiliation(s)
- Danielle J Ingle
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia; National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australia; Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia
| | - Benjamin P Howden
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia; Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia; Doherty Applied Microbial Genomics, Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia
| | - Sebastian Duchene
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia.
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18
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A Phylogeny-Informed Proteomics Approach for Species Identification within the Burkholderia cepacia Complex. J Clin Microbiol 2020; 58:JCM.01741-20. [PMID: 32878952 DOI: 10.1128/jcm.01741-20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/26/2020] [Indexed: 01/17/2023] Open
Abstract
Ancestral genetic exchange between members of many important bacterial pathogen groups has resulted in phylogenetic relationships better described as networks than as bifurcating trees. In certain cases, these reticulated phylogenies have resulted in phenotypic and molecular overlap that challenges the construction of practical approaches for species identification in the clinical microbiology laboratory. Burkholderia cepacia complex (Bcc), a betaproteobacteria species group responsible for significant morbidity in persons with cystic fibrosis and chronic granulomatous disease, represents one such group where network-structured phylogeny has hampered the development of diagnostic methods for species-level discrimination. Here, we present a phylogeny-informed proteomics approach to facilitate diagnostic classification of pathogen groups with reticulated phylogenies, using Bcc as an example. Starting with a set of more than 800 Bcc and Burkholderia gladioli whole-genome assemblies, we constructed phylogenies with explicit representation of inferred interspecies recombination. Sixteen highly discriminatory peptides were chosen to distinguish B. cepacia, Burkholderia cenocepacia, Burkholderia multivorans, and B. gladioli and multiplexed into a single, rapid liquid chromatography-tandem mass spectrometry multiple reaction monitoring (LC-MS/MS MRM) assay. Testing of a blinded set of isolates containing these four Burkholderia species demonstrated 50/50 correct automatic negative calls (100% accuracy with a 95% confidence interval [CI] of 92.9 to 100%), and 70/70 correct automatic species-level positive identifications (100% accuracy with 95% CI 94.9 to 100%) after accounting for a single initial incorrect identification due to a preanalytic error, correctly identified on retesting. The approach to analysis described here is applicable to other pathogen groups for which development of diagnostic classification methods is complicated by interspecies recombination.
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19
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20
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Boskova V, Stadler T. PIQMEE: Bayesian Phylodynamic Method for Analysis of Large Data Sets with Duplicate Sequences. Mol Biol Evol 2020; 37:3061-3075. [PMID: 32492139 PMCID: PMC7530608 DOI: 10.1093/molbev/msaa136] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Next-generation sequencing of pathogen quasispecies within a host yields data sets of tens to hundreds of unique sequences. However, the full data set often contains thousands of sequences, because many of those unique sequences have multiple identical copies. Data sets of this size represent a computational challenge for currently available Bayesian phylogenetic and phylodynamic methods. Through simulations, we explore how large data sets with duplicate sequences affect the speed and accuracy of phylogenetic and phylodynamic analysis within BEAST 2. We show that using unique sequences only leads to biases, and using a random subset of sequences yields imprecise parameter estimates. To overcome these shortcomings, we introduce PIQMEE, a BEAST 2 add-on that produces reliable parameter estimates from full data sets with increased computational efficiency as compared with the currently available methods within BEAST 2. The principle behind PIQMEE is to resolve the tree structure of the unique sequences only, while simultaneously estimating the branching times of the duplicate sequences. Distinguishing between unique and duplicate sequences allows our method to perform well even for very large data sets. Although the classic method converges poorly for data sets of 6,000 sequences when allowed to run for 7 days, our method converges in slightly more than 1 day. In fact, PIQMEE can handle data sets of around 21,000 sequences with 20 unique sequences in 14 days. Finally, we apply the method to a real, within-host HIV sequencing data set with several thousand sequences per patient.
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Affiliation(s)
- Veronika Boskova
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics (SIB), Switzerland
- Center for Integrative Bioinformatics Vienna, Max Perutz Labs, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics (SIB), Switzerland
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21
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Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses. Proc Natl Acad Sci U S A 2020; 117:17104-17111. [PMID: 32631984 PMCID: PMC7382287 DOI: 10.1073/pnas.1918304117] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Genetic recombination processes, such as reassortment, make it complex or impossible to use standard phylogenetic and phylodynamic methods. This is due to the fact that the shared evolutionary history of individuals has to be represented by a phylogenetic network instead of a tree. We therefore require novel approaches that allow us to coherently model these processes and that allow us to perform inference in the presence of such processes. Here, we introduce an approach to infer reassortment networks of segmented viruses using a Markov chain Monte Carlo approach. Our approach allows us to study different aspects of the reassortment process and allows us to show fitness benefits of reassortment events in seasonal human influenza viruses. Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. Here, we introduce a coalescent-based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. This algorithm provides the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full-genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses.
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22
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Pang TY. A coarse-graining, ultrametric approach to resolve the phylogeny of prokaryotic strains with frequent homologous recombination. BMC Evol Biol 2020; 20:52. [PMID: 32381044 PMCID: PMC7204016 DOI: 10.1186/s12862-020-01616-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 04/20/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A frequent event in the evolution of prokaryotic genomes is homologous recombination, where a foreign DNA stretch replaces a genomic region similar in sequence. Recombination can affect the relative position of two genomes in a phylogenetic reconstruction in two different ways: (i) one genome can recombine with a DNA stretch that is similar to the other genome, thereby reducing their pairwise sequence divergence; (ii) one genome can recombine with a DNA stretch from an outgroup genome, increasing the pairwise divergence. While several recombination-aware phylogenetic algorithms exist, many of these cannot account for both types of recombination; some algorithms can, but do so inefficiently. Moreover, many of them reconstruct the ancestral recombination graph (ARG) to help infer the genome tree, and require that a substantial portion of each genome has not been affected by recombination, a sometimes unrealistic assumption. METHODS Here, we propose a Coarse-Graining approach for Phylogenetic reconstruction (CGP), which is recombination-aware but forgoes ARG reconstruction. It accounts for the tendency of a higher effective recombination rate between genomes with a lower phylogenetic distance. It is applicable even if all genomic regions have experienced substantial amounts of recombination, and can be used on both nucleotide and amino acid sequences. CGP considers the local density of substitutions along pairwise genome alignments, fitting a model to the empirical distribution of substitution density to infer the pairwise coalescent time. Given all pairwise coalescent times, CGP reconstructs an ultrametric tree representing vertical inheritance. RESULTS Based on simulations, we show that the proposed approach can reconstruct ultrametric trees with accurate topology, branch lengths, and root positioning. Applied to a set of E. coli strains, the reconstructed trees are most consistent with gene distributions when inferred from amino acid sequences, a data type that cannot be utilized by many alternative approaches. CONCLUSIONS The CGP algorithm is more accurate than alternative recombination-aware methods for ultrametric phylogenetic reconstructions.
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Affiliation(s)
- Tin Yau Pang
- Computational Cell Biology, Heinrich Heine University, 40225, Düsseldorf, Germany.
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23
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Norte AC, Margos G, Becker NS, Albino Ramos J, Núncio MS, Fingerle V, Araújo PM, Adamík P, Alivizatos H, Barba E, Barrientos R, Cauchard L, Csörgő T, Diakou A, Dingemanse NJ, Doligez B, Dubiec A, Eeva T, Flaisz B, Grim T, Hau M, Heylen D, Hornok S, Kazantzidis S, Kováts D, Krause F, Literak I, Mänd R, Mentesana L, Morinay J, Mutanen M, Neto JM, Nováková M, Sanz JJ, Pascoal da Silva L, Sprong H, Tirri IS, Török J, Trilar T, Tyller Z, Visser ME, Lopes de Carvalho I. Host dispersal shapes the population structure of a tick-borne bacterial pathogen. Mol Ecol 2020; 29:485-501. [PMID: 31846173 DOI: 10.1111/mec.15336] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 08/02/2019] [Accepted: 12/11/2019] [Indexed: 01/25/2023]
Abstract
Birds are hosts for several zoonotic pathogens. Because of their high mobility, especially of longdistance migrants, birds can disperse these pathogens, affecting their distribution and phylogeography. We focused on Borrelia burgdorferi sensu lato, which includes the causative agents of Lyme borreliosis, as an example for tick-borne pathogens, to address the role of birds as propagation hosts of zoonotic agents at a large geographical scale. We collected ticks from passerine birds in 11 European countries. B. burgdorferi s.l. prevalence in Ixodes spp. was 37% and increased with latitude. The fieldfare Turdus pilaris and the blackbird T. merula carried ticks with the highest Borrelia prevalence (92 and 58%, respectively), whereas robin Erithacus rubecula ticks were the least infected (3.8%). Borrelia garinii was the most prevalent genospecies (61%), followed by B. valaisiana (24%), B. afzelii (9%), B. turdi (5%) and B. lusitaniae (0.5%). A novel Borrelia genospecies "Candidatus Borrelia aligera" was also detected. Multilocus sequence typing (MLST) analysis of B. garinii isolates together with the global collection of B. garinii genotypes obtained from the Borrelia MLST public database revealed that: (a) there was little overlap among genotypes from different continents, (b) there was no geographical structuring within Europe, and (c) there was no evident association pattern detectable among B. garinii genotypes from ticks feeding on birds, questing ticks or human isolates. These findings strengthen the hypothesis that the population structure and evolutionary biology of tick-borne pathogens are shaped by their host associations and the movement patterns of these hosts.
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Affiliation(s)
- Ana Cláudia Norte
- MARE - Marine and Environmental Sciences Centre, University of Coimbra, Coimbra, Portugal.,Center for Vector and Infectious Diseases Research, National Institute of Health Dr. Ricardo Jorge, Lisbon, Portugal
| | - Gabriele Margos
- German National Reference Centre for Borrelia (NRZ), Bavarian Health and Food Safety Authority (LGL), Oberschleissheim, Germany
| | - Noémie S Becker
- Division of Evolutionary Biology, Faculty of Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Jaime Albino Ramos
- MARE - Marine and Environmental Sciences Centre, University of Coimbra, Coimbra, Portugal
| | - Maria Sofia Núncio
- Center for Vector and Infectious Diseases Research, National Institute of Health Dr. Ricardo Jorge, Lisbon, Portugal
| | - Volker Fingerle
- German National Reference Centre for Borrelia (NRZ), Bavarian Health and Food Safety Authority (LGL), Oberschleissheim, Germany
| | - Pedro Miguel Araújo
- MARE - Marine and Environmental Sciences Centre, University of Coimbra, Coimbra, Portugal
| | - Peter Adamík
- Department of Zoology, Palacky University, Olomouc, Czech Republic
| | | | - Emilio Barba
- Instituto Cavanilles de Biodiversidad y Biología Evolutiva (ICBiBE), Universidad de Valencia, Valencia, Spain
| | - Rafael Barrientos
- Department of Biodiversity, Ecology and Evolution, Universidad Complutense de Madrid, Madrid, Spain
| | - Laure Cauchard
- School of Biological Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Tibor Csörgő
- Ócsa Bird Ringing Station, Ócsa, Hungary.,Department of Anatomy, Cell and Developmental Biology, Eötvös Loránd University, Budapest, Hungary
| | - Anastasia Diakou
- Laboratory of Parasitology and Parasitic Diseases, Faculty of Health Sciences, School of Veterinary Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Niels J Dingemanse
- Behavioural Ecology, Department of Biology, Ludwig Maximilians University of Munich, Planegg-Martinsried, Germany
| | - Blandine Doligez
- CNRS - Department of Biometry and Evolutionary Biology (LBBE) - University Lyon 1, University of Lyon, Villeurbanne, France
| | - Anna Dubiec
- Museum and Institute of Zoology, Polish Academy of Sciences, Warszawa, Poland
| | - Tapio Eeva
- Department of Biology, University of Turku, Turku, Finland
| | - Barbara Flaisz
- Department of Parasitology and Zoology, University of Veterinary Medicine, Budapest, Hungary
| | - Tomas Grim
- Department of Zoology, Palacky University, Olomouc, Czech Republic
| | - Michaela Hau
- Evolutionary Physiology Laboratory, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Dieter Heylen
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium
| | - Sándor Hornok
- Department of Parasitology and Zoology, University of Veterinary Medicine, Budapest, Hungary
| | - Savas Kazantzidis
- Forest Research Institute, Hellenic Agricultural Organization "DEMETER", Thesaloniki, Greece
| | - David Kováts
- Ócsa Bird Ringing Station, Ócsa, Hungary.,Hungarian Biodiversity Research Society, Budapest, Hungary
| | | | - Ivan Literak
- Department of Biology and Wildlife Diseases, Faculty of Veterinary Hygiene and Ecology, University of Veterinary and Pharmaceutical Sciences Brno, Brno, Czech Republic
| | - Raivo Mänd
- Department of Zoology, University of Tartu, Tartu, Estonia
| | - Lucia Mentesana
- Evolutionary Physiology Laboratory, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Jennifer Morinay
- CNRS - Department of Biometry and Evolutionary Biology (LBBE) - University Lyon 1, University of Lyon, Villeurbanne, France.,Department of Ecology and Evolution, Animal Ecology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
| | - Marko Mutanen
- Department of Ecology and Genetics, University of Oulu, Oulu, Finland
| | - Júlio Manuel Neto
- Department of Biology, Molecular Ecology and Evolution Lab, University of Lund, Lund, Sweden
| | - Markéta Nováková
- Department of Biology and Wildlife Diseases, Faculty of Veterinary Hygiene and Ecology, University of Veterinary and Pharmaceutical Sciences Brno, Brno, Czech Republic.,Department of Biology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Juan José Sanz
- Departamento de Ecología Evolutiva, Museo Nacional de Ciencias Naturales (CSIC), Madrid, Spain
| | - Luís Pascoal da Silva
- Department of Life Sciences, CFE - Centre for Functional Ecology - Science for People & the Planet, University of Coimbra, Coimbra, Portugal.,CIBIO-InBIO, Research Center in Biodiversity and Genetic Resources, University of Porto, Porto, Portugal
| | - Hein Sprong
- National Institute of Public Health and Environment (RIVM), Laboratory for Zoonoses and Environmental Microbiology, Bilthoven, The Netherlands
| | - Ina-Sabrina Tirri
- Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland
| | - János Török
- Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary
| | - Tomi Trilar
- Slovenian Museum of Natural History, Ljubljana, Slovenia
| | - Zdeněk Tyller
- Department of Zoology, Palacky University, Olomouc, Czech Republic.,Museum of the Moravian Wallachia Region, Vsetín, Czech Republic
| | - Marcel E Visser
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Isabel Lopes de Carvalho
- Center for Vector and Infectious Diseases Research, National Institute of Health Dr. Ricardo Jorge, Lisbon, Portugal
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24
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Dudas G, Bedford T. The ability of single genes vs full genomes to resolve time and space in outbreak analysis. BMC Evol Biol 2019; 19:232. [PMID: 31878875 PMCID: PMC6933756 DOI: 10.1186/s12862-019-1567-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 12/17/2019] [Indexed: 12/12/2022] Open
Abstract
Background Inexpensive pathogen genome sequencing has had a transformative effect on the field of phylodynamics, where ever increasing volumes of data have promised real-time insight into outbreaks of infectious disease. As well as the sheer volume of pathogen isolates being sequenced, the sequencing of whole pathogen genomes, rather than select loci, has allowed phylogenetic analyses to be carried out at finer time scales, often approaching serial intervals for infections caused by rapidly evolving RNA viruses. Despite its utility, whole genome sequencing of pathogens has not been adopted universally and targeted sequencing of loci is common in some pathogen-specific fields. Results In this study we highlighted the utility of sequencing whole genomes of pathogens by re-analysing a well-characterised collection of Ebola virus sequences in the form of complete viral genomes (≈19 kb long) or the rapidly evolving glycoprotein (GP, ≈2 kb long) gene. We have quantified changes in phylogenetic, temporal, and spatial inference resolution as a result of this reduction in data and compared these to theoretical expectations. Conclusions We propose a simple intuitive metric for quantifying temporal resolution, i.e. the time scale over which sequence data might be informative of various processes as a quick back-of-the-envelope calculation of statistical power available to molecular clock analyses.
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Affiliation(s)
- Gytis Dudas
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, USA. .,Gothenburg Global Biodiversity Centre, Carl Skottsbergs gata 22B, Gothenburg, 413 19, Sweden.
| | - Trevor Bedford
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, USA
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25
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Didelot X, Croucher NJ, Bentley SD, Harris SR, Wilson DJ. Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res 2019; 46:e134. [PMID: 30184106 PMCID: PMC6294524 DOI: 10.1093/nar/gky783] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 08/21/2018] [Indexed: 12/15/2022] Open
Abstract
The sequencing and comparative analysis of a collection of bacterial genomes from a single species or lineage of interest can lead to key insights into its evolution, ecology or epidemiology. The tool of choice for such a study is often to build a phylogenetic tree, and more specifically when possible a dated phylogeny, in which the dates of all common ancestors are estimated. Here, we propose a new Bayesian methodology to construct dated phylogenies which is specifically designed for bacterial genomics. Unlike previous Bayesian methods aimed at building dated phylogenies, we consider that the phylogenetic relationships between the genomes have been previously evaluated using a standard phylogenetic method, which makes our methodology much faster and scalable. This two-step approach also allows us to directly exploit existing phylogenetic methods that detect bacterial recombination, and therefore to account for the effect of recombination in the construction of a dated phylogeny. We analysed many simulated datasets in order to benchmark the performance of our approach in a wide range of situations. Furthermore, we present applications to three different real datasets from recent bacterial genomic studies. Our methodology is implemented in a R package called BactDating which is freely available for download at https://github.com/xavierdidelot/BactDating.
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Affiliation(s)
- Xavier Didelot
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Nicholas J Croucher
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Stephen D Bentley
- The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Simon R Harris
- The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Daniel J Wilson
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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26
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Bouckaert R, Vaughan TG, Barido-Sottani J, Duchêne S, Fourment M, Gavryushkina A, Heled J, Jones G, Kühnert D, De Maio N, Matschiner M, Mendes FK, Müller NF, Ogilvie HA, du Plessis L, Popinga A, Rambaut A, Rasmussen D, Siveroni I, Suchard MA, Wu CH, Xie D, Zhang C, Stadler T, Drummond AJ. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol 2019; 15:e1006650. [PMID: 30958812 PMCID: PMC6472827 DOI: 10.1371/journal.pcbi.1006650] [Citation(s) in RCA: 1583] [Impact Index Per Article: 316.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 04/18/2019] [Accepted: 02/04/2019] [Indexed: 11/18/2022] Open
Abstract
Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.
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Affiliation(s)
- Remco Bouckaert
- Centre of Computational Evolution, University of Auckland, Auckland, New Zealand
- Max Planck Institute for the Science of Human History, Jena, Germany
| | - Timothy G. Vaughan
- ETH Zürich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joëlle Barido-Sottani
- ETH Zürich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sebastián Duchêne
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Victoria, Australia
| | - Mathieu Fourment
- ithree institute, University of Technology Sydney, Sydney, Australia
| | | | | | - Graham Jones
- Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, SE 405 30 Göteborg, Sweden
| | - Denise Kühnert
- Max Planck Institute for the Science of Human History, Jena, Germany
| | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridgeshire, UK
| | - Michael Matschiner
- Department of Environmental Sciences, University of Basel, 4051 Basel, Switzerland
| | - Fábio K. Mendes
- Centre of Computational Evolution, University of Auckland, Auckland, New Zealand
| | - Nicola F. Müller
- ETH Zürich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Huw A. Ogilvie
- Department of Computer Science, Rice University, Houston, TX 77005-1892, USA
| | - Louis du Plessis
- Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK
| | - Alex Popinga
- Centre of Computational Evolution, University of Auckland, Auckland, New Zealand
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, EH9 3FL UK
| | - David Rasmussen
- Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA
| | - Igor Siveroni
- Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, W2 1PG, UK
| | - Marc A. Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Chieh-Hsi Wu
- Department of Statistics, University of Oxford, OX1 3LB, UK
| | - Dong Xie
- Centre of Computational Evolution, University of Auckland, Auckland, New Zealand
| | - Chi Zhang
- Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, China
| | - Tanja Stadler
- ETH Zürich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Alexei J. Drummond
- Centre of Computational Evolution, University of Auckland, Auckland, New Zealand
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27
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Vaughan TG. IcyTree: rapid browser-based visualization for phylogenetic trees and networks. Bioinformatics 2018; 33:2392-2394. [PMID: 28407035 PMCID: PMC5860111 DOI: 10.1093/bioinformatics/btx155] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 03/21/2017] [Indexed: 01/25/2023] Open
Abstract
Summary IcyTree is an easy-to-use application which can be used to visualize a wide variety of phylogenetic trees and networks. While numerous phylogenetic tree viewers exist already, IcyTree distinguishes itself by being a purely online tool, having a responsive user interface, supporting phylogenetic networks (ancestral recombination graphs in particular), and efficiently drawing trees that include information such as ancestral locations or trait values. IcyTree also provides intuitive panning and zooming utilities that make exploring large phylogenetic trees of many thousands of taxa feasible. Availability and Implementation IcyTree is a web application and can be accessed directly at http://tgvaughan.github.com/icytree . Currently supported web browsers include Mozilla Firefox and Google Chrome. IcyTree is written entirely in client-side JavaScript (no plugin required) and, once loaded, does not require network access to run. IcyTree is free software, and the source code is made available at http://github.com/tgvaughan/icytree under version 3 of the GNU General Public License. Contact tgvaughan@gmail.com.
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Affiliation(s)
- Timothy G Vaughan
- Department of Computer Science, University of Auckland, Auckland, New Zealand
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28
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Ibrahim B, Arkhipova K, Andeweg AC, Posada-Céspedes S, Enault F, Gruber A, Koonin EV, Kupczok A, Lemey P, McHardy AC, McMahon DP, Pickett BE, Robertson DL, Scheuermann RH, Zhernakova A, Zwart MP, Schönhuth A, Dutilh BE, Marz M. Bioinformatics Meets Virology: The European Virus Bioinformatics Center's Second Annual Meeting. Viruses 2018; 10:E256. [PMID: 29757994 PMCID: PMC5977249 DOI: 10.3390/v10050256] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 05/11/2018] [Accepted: 05/11/2018] [Indexed: 11/16/2022] Open
Abstract
The Second Annual Meeting of the European Virus Bioinformatics Center (EVBC), held in Utrecht, Netherlands, focused on computational approaches in virology, with topics including (but not limited to) virus discovery, diagnostics, (meta-)genomics, modeling, epidemiology, molecular structure, evolution, and viral ecology. The goals of the Second Annual Meeting were threefold: (i) to bring together virologists and bioinformaticians from across the academic, industrial, professional, and training sectors to share best practice; (ii) to provide a meaningful and interactive scientific environment to promote discussion and collaboration between students, postdoctoral fellows, and both new and established investigators; (iii) to inspire and suggest new research directions and questions. Approximately 120 researchers from around the world attended the Second Annual Meeting of the EVBC this year, including 15 renowned international speakers. This report presents an overview of new developments and novel research findings that emerged during the meeting.
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Affiliation(s)
- Bashar Ibrahim
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, 07743 Jena, Germany.
| | - Ksenia Arkhipova
- Theoretical Biology and Bioinformatics, Utrecht University, 3508 TC Utrecht, The Netherlands.
| | - Arno C Andeweg
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Department of Viroscience, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands.
| | - Susana Posada-Céspedes
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland.
| | - François Enault
- Université Clermont Auvergne, CNRS, LMGE, F-63000 Clermont-Ferrand, France.
| | - Arthur Gruber
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, 05508-000 São Paulo, Brazil.
| | - Eugene V Koonin
- National Center for Biotechnology Information, NLM, National Institutes of Health, Bethesda, MD 20894, USA.
| | - Anne Kupczok
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Institute of General Microbiology, Kiel University, 24118 Kiel, Germany.
| | - Philippe Lemey
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Clinical and Epidemiological Virology, Rega Institute, KU Leuven, University of Leuven, 3000 Leuven, Belgium.
| | - Alice C McHardy
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research, 38124 Braunschweig, Germany.
| | - Dino P McMahon
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Institute of Biology, Free University Berlin, Schwendenerstr. 1, 14195 Berlin, Germany.
- Department for Materials and Environment, BAM Federal Institute for Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany.
| | - Brett E Pickett
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- J. Craig Venter Institute, Rockville, MD 20850, USA.
| | - David L Robertson
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- MRC-University of Glasgow Centre for Virus Research, Garscube Campus, Glasgow G61 1QH, UK.
| | - Richard H Scheuermann
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- J. Craig Venter Institute, La Jolla, CA 92037, USA.
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, 9700 RB Groningen, The Netherlands.
| | - Mark P Zwart
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6708 PB Wageningen, The Netherlands.
| | - Alexander Schönhuth
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Theoretical Biology and Bioinformatics, Utrecht University, 3508 TC Utrecht, The Netherlands.
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands.
| | - Bas E Dutilh
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Theoretical Biology and Bioinformatics, Utrecht University, 3508 TC Utrecht, The Netherlands.
| | - Manja Marz
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, 07743 Jena, Germany.
- Leibniz Institute for Age Research-Fritz Lipmann Institute, 07745 Jena, Germany.
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29
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Bromham L, Duchêne S, Hua X, Ritchie AM, Duchêne DA, Ho SYW. Bayesian molecular dating: opening up the black box. Biol Rev Camb Philos Soc 2017; 93:1165-1191. [DOI: 10.1111/brv.12390] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 11/13/2017] [Accepted: 11/17/2017] [Indexed: 12/27/2022]
Affiliation(s)
- Lindell Bromham
- Macroevolution & Macroecology, Division of Ecology & Evolution, Research School of Biology; Australian National University; Canberra ACT 2601 Australia
| | - Sebastián Duchêne
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute; The University of Melbourne; Melbourne VIC 3010 Australia
- School of Life and Environmental Sciences; University of Sydney; Sydney NSW 2006 Australia
| | - Xia Hua
- Macroevolution & Macroecology, Division of Ecology & Evolution, Research School of Biology; Australian National University; Canberra ACT 2601 Australia
| | - Andrew M. Ritchie
- School of Life and Environmental Sciences; University of Sydney; Sydney NSW 2006 Australia
| | - David A. Duchêne
- Macroevolution & Macroecology, Division of Ecology & Evolution, Research School of Biology; Australian National University; Canberra ACT 2601 Australia
- School of Life and Environmental Sciences; University of Sydney; Sydney NSW 2006 Australia
| | - Simon Y. W. Ho
- School of Life and Environmental Sciences; University of Sydney; Sydney NSW 2006 Australia
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