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Pekar JE, Wang Y, Wang JC, Shao Y, Taki F, Forgione LA, Amin H, Clabby T, Johnson K, Torian LV, Braunstein SL, Pathela P, Omoregie E, Hughes S, Suchard MA, Vasylyeva TI, Lemey P, Wertheim JO. Genomic epidemiology reveals 2022 mpox epidemic in New York City governed by heavy-tailed sexual contact networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.30.24311083. [PMID: 39132479 PMCID: PMC11312668 DOI: 10.1101/2024.07.30.24311083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The global mpox epidemic in 2022 was likely caused by transmission of mpox virus (MPXV) through sexual contact networks, with New York City (NYC) experiencing the first and largest outbreak in the United States. By performing a phylogeographic and epidemiological analysis of MPXV, we identify at least 200 introductions of MPXV into NYC and 84 leading to onward transmission. Through a comparative analysis with human immunodeficiency virus (HIV) in NYC, we find that both MPXV and HIV genomic cluster sizes are best fit by scale-free distributions and that people in MPXV clusters are more likely to have previously received an HIV diagnosis (odds ratio=1.58; p =0.012) and be a member of a recently growing HIV transmission cluster, indicating overlapping sexual contact networks. We then model the transmission of MPXV through sexual contact networks and show that highly connected individuals would be disproportionately infected at the start of an epidemic, thereby likely resulting in the exhaustion of the most densely connected parts of the sexual network. This dynamic explains the rapid expansion and decline of the NYC outbreak, as well as the estimated cumulative incidence of less than 2% within high-risk populations. By synthesizing the genomic epidemiology of MPXV and HIV with epidemic modeling, we demonstrate that MPXV transmission dynamics can be understood by general principles of sexually transmitted pathogens.
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Ly-Trong N, Bielow C, De Maio N, Minh BQ. CMAPLE: Efficient Phylogenetic Inference in the Pandemic Era. Mol Biol Evol 2024; 41:msae134. [PMID: 38934791 PMCID: PMC11232695 DOI: 10.1093/molbev/msae134] [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: 02/09/2024] [Revised: 05/15/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
We have recently introduced MAPLE (MAximum Parsimonious Likelihood Estimation), a new pandemic-scale phylogenetic inference method exclusively designed for genomic epidemiology. In response to the need for enhancing MAPLE's performance and scalability, here we present two key components: (i) CMAPLE software, a highly optimized C++ reimplementation of MAPLE with many new features and advancements, and (ii) CMAPLE library, a suite of application programming interfaces to facilitate the integration of the CMAPLE algorithm into existing phylogenetic inference packages. Notably, we have successfully integrated CMAPLE into the widely used IQ-TREE 2 software, enabling its rapid adoption in the scientific community. These advancements serve as a vital step toward better preparedness for future pandemics, offering researchers powerful tools for large-scale pathogen genomic analysis.
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Affiliation(s)
- Nhan Ly-Trong
- School of Computing, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT 2600, Australia
| | - Chris Bielow
- Bioinformatics Solution Center, Freie Universität Berlin, 14195 Berlin, Germany
| | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Bui Quang Minh
- School of Computing, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT 2600, Australia
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3
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Iglhaut C, Pečerska J, Gil M, Anisimova M. Please Mind the Gap: Indel-Aware Parsimony for Fast and Accurate Ancestral Sequence Reconstruction and Multiple Sequence Alignment Including Long Indels. Mol Biol Evol 2024; 41:msae109. [PMID: 38842253 PMCID: PMC11221656 DOI: 10.1093/molbev/msae109] [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: 03/25/2024] [Revised: 05/30/2024] [Accepted: 06/03/2024] [Indexed: 06/07/2024] Open
Abstract
Despite having important biological implications, insertion, and deletion (indel) events are often disregarded or mishandled during phylogenetic inference. In multiple sequence alignment, indels are represented as gaps and are estimated without considering the distinct evolutionary history of insertions and deletions. Consequently, indels are usually excluded from subsequent inference steps, such as ancestral sequence reconstruction and phylogenetic tree search. Here, we introduce indel-aware parsimony (indelMaP), a novel way to treat gaps under the parsimony criterion by considering insertions and deletions as separate evolutionary events and accounting for long indels. By identifying the precise location of an evolutionary event on the tree, we can separate overlapping indel events and use affine gap penalties for long indel modeling. Our indel-aware approach harnesses the phylogenetic signal from indels, including them into all inference stages. Validation and comparison to state-of-the-art inference tools on simulated data show that indelMaP is most suitable for densely sampled datasets with closely to moderately related sequences, where it can reach alignment quality comparable to probabilistic methods and accurately infer ancestral sequences, including indel patterns. Due to its remarkable speed, our method is well suited for epidemiological datasets, eliminating the need for downsampling and enabling the exploitation of the additional information provided by dense taxonomic sampling. Moreover, indelMaP offers new insights into the indel patterns of biologically significant sequences and advances our understanding of genetic variability by considering gaps as crucial evolutionary signals rather than mere artefacts.
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Affiliation(s)
- Clara Iglhaut
- Institute of Computational Life Science, Zurich University of Applied Science, Wädenswil, Switzerland
- Faculty of Mathematics and Science, University of Zurich, Zürich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jūlija Pečerska
- Institute of Computational Life Science, Zurich University of Applied Science, Wädenswil, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Manuel Gil
- Institute of Computational Life Science, Zurich University of Applied Science, Wädenswil, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Maria Anisimova
- Institute of Computational Life Science, Zurich University of Applied Science, Wädenswil, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Dellicour S, Bastide P, Rocu P, Fargette D, Hardy OJ, Suchard MA, Guindon S, Lemey P. How fast are viruses spreading in the wild? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588821. [PMID: 38645268 PMCID: PMC11030353 DOI: 10.1101/2024.04.10.588821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics allowing to unveil the dispersal dynamics and evaluate the capacity to spread among hosts. Heterogeneous sampling intensity of genomic sequences can however impact the accuracy of dispersal insights gained through phylogeographic inference. In our study, we implement a simulation framework to evaluate the robustness of three dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance signal metric - to the sampling effort. Our results reveal that both the diffusion coefficient and isolation-by-distance signal metrics appear to be robust to the number of samples considered for the phylogeographic reconstruction. We then use these two dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of isolation-by-distance patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.
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Meijers M, Ruchnewitz D, Eberhardt J, Karmakar M, Łuksza M, Lässig M. Concepts and methods for predicting viral evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585703. [PMID: 38746108 PMCID: PMC11092427 DOI: 10.1101/2024.03.19.585703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .
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Meijers M, Ruchnewitz D, Eberhardt J, Karmakar M, Łuksza M, Lässig M. Concepts and methods for predicting viral evolution. ARXIV 2024:arXiv:2403.12684v2. [PMID: 38745695 PMCID: PMC11092678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.
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Affiliation(s)
- Matthijs Meijers
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Denis Ruchnewitz
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Jan Eberhardt
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Malancha Karmakar
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Departments of Oncological Sciences and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Lässig
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
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Windah ALL, Tallei TE, AlShehail BM, Suoth EJ, Fatimawali, Alhashem YN, Halwani MA, AlShakhal MM, Aljeldah M, Alissa M, Alsuwat MA, Almanaa TN, Alshehri AA, Rabaan AA. Immunoinformatics-Driven Strategies for Advancing Epitope-Based Vaccine Design for West Nile Virus. J Pharm Sci 2024; 113:906-917. [PMID: 38042341 DOI: 10.1016/j.xphs.2023.11.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/04/2023]
Abstract
The West Nile virus (WNV) is the causative agent of West Nile disease (WND), which poses a potential risk of meningitis or encephalitis. The aim of the study was to design an epitope-based vaccine for WNV by utilizing computational analyses. The epitope-based vaccine design process encompassed WNV sequence collection, phylogenetic tree construction, and sequence alignment. Computational models identified B-cell and T-cell epitopes, followed by immunological property analysis. Epitopes were then modeled and docked with B-cell receptors, MHC I, and MHC II. Molecular dynamics simulations further explored dynamic interactions between epitopes and receptors. The findings indicated that the B-cell epitope QINHHWHKSGSSIG, along with three T-cell epitopes (FLVHREWFM for MHC I, NPFVSVATANAKVLI for MHC II, and NAYYVMTVGTKTFLV for MHC II), successfully passed the immunological evaluations. These four epitopes were further subjected to docking and molecular dynamics simulation studies. Although each demonstrated favorable affinities with their respective receptors, only NAYYVMTVGTKTFLV displayed a stable interaction with MHC II during MDS analysis, hence emerging as a potential candidate for a WNV epitope-based vaccine. This study demonstrates a comprehensive approach to epitope vaccine design, combining computational analyses, molecular modeling, and simulation techniques to identify potential vaccine candidates for WNV.
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Affiliation(s)
- Axl Laurens Lukas Windah
- Department of Chemistry, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, East Java, Indonesia
| | - Trina Ekawati Tallei
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado 95115, North Sulawesi, Indonesia.
| | - Bashayer M AlShehail
- Pharmacy Practice Department, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Elly Juliana Suoth
- Pharmacy Study Program, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Mana-do 95115, North Sulawesi, Indonesia
| | - Fatimawali
- Pharmacy Study Program, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Mana-do 95115, North Sulawesi, Indonesia
| | - Yousef N Alhashem
- Clinical Laboratory Science Department, Mohammed Al-Mana College for Medical Sciences, Dammam 34222, Saudi Arabia
| | - Muhammad A Halwani
- Department of Medical Microbiology, Faculty of Medicine, Al Baha University. Al Baha 4781, Saudi Arabia
| | - Mouayd M AlShakhal
- Internal Medicine Department, Qatif Central Hospital, Qatif 32654, Saudi Arabia
| | - Mohammed Aljeldah
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin 39831, Saudi Arabia
| | - Mohammed Alissa
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Meshari A Alsuwat
- Clinical Laboratory Sciences Department, College of Applied Medical Sciences, Taif University, Al-Taif 21974, Saudi Arabia
| | - Taghreed N Almanaa
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Ahmad A Alshehri
- Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia
| | - Ali A Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran 31311, Saudi Arabia; College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; Department of Public Health and Nutrition, The University of Haripur, Haripur 22610, Pakistan
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8
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Raglow Z, Surie D, Chappell JD, Zhu Y, Martin ET, Kwon JH, Frosch AE, Mohamed A, Gilbert J, Bendall EE, Bahr A, Halasa N, Talbot HK, Grijalva CG, Baughman A, Womack KN, Johnson C, Swan SA, Koumans E, McMorrow ML, Harcourt JL, Atherton LJ, Burroughs A, Thornburg NJ, Self WH, Lauring AS. SARS-CoV-2 shedding and evolution in patients who were immunocompromised during the omicron period: a multicentre, prospective analysis. THE LANCET. MICROBE 2024; 5:e235-e246. [PMID: 38286131 DOI: 10.1016/s2666-5247(23)00336-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 01/31/2024]
Abstract
BACKGROUND Prolonged SARS-CoV-2 infections in people who are immunocompromised might predict or source the emergence of highly mutated variants. The types of immunosuppression placing patients at highest risk for prolonged infection have not been systematically investigated. We aimed to assess risk factors for prolonged SARS-CoV-2 infection and associated intrahost evolution. METHODS In this multicentre, prospective analysis, participants were enrolled at five US medical centres. Eligible patients were aged 18 years or older, were SARS-CoV-2-positive in the previous 14 days, and had a moderately or severely immunocompromising condition or treatment. Nasal specimens were tested by real-time RT-PCR every 2-4 weeks until negative in consecutive specimens. Positive specimens underwent viral culture and whole genome sequencing. A Cox proportional hazards model was used to assess factors associated with duration of infection. FINDINGS From April 11, 2022, to Oct 1, 2022, 156 patients began the enrolment process, of whom 150 were enrolled and included in the analyses. Participants had B-cell malignancy or anti-B-cell therapy (n=18), solid organ transplantation or haematopoietic stem-cell transplantation (HSCT; n=59), AIDS (n=5), non-B-cell malignancy (n=23), and autoimmune or autoinflammatory conditions (n=45). 38 (25%) participants were real-time RT-PCR-positive and 12 (8%) were culture-positive 21 days or longer after initial SARS-CoV-2 detection or illness onset. Compared with the group with autoimmune or autoinflammatory conditions, patients with B-cell dysfunction (adjusted hazard ratio 0·32 [95% CI 0·15-0·64]), solid organ transplantation or HSCT (0·60 [0·38-0·94]), and AIDS (0·28 [0·08-1·00]) had longer duration of infection, defined as time to last positive real-time RT-PCR test. There was no significant difference in the non-B-cell malignancy group (0·58 [0·31-1·09]). Consensus de novo spike mutations were identified in five individuals who were real-time RT-PCR-positive longer than 56 days; 14 (61%) of 23 were in the receptor-binding domain. Mutations shared by multiple individuals were rare (<5%) in global circulation. INTERPRETATION In this cohort, prolonged replication-competent omicron SARS-CoV-2 infections were uncommon. Within-host evolutionary rates were similar across patients, but individuals with infections lasting longer than 56 days accumulated spike mutations, which were distinct from those seen globally. Populations at high risk should be targeted for repeated testing and treatment and monitored for the emergence of antiviral resistance. FUNDING US Centers for Disease Control and Prevention.
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Affiliation(s)
- Zoe Raglow
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Diya Surie
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - James D Chappell
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuwei Zhu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Emily T Martin
- School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennie H Kwon
- Department of Medicine, Washington University, St Louis, MO, USA
| | - Anne E Frosch
- Department of Medicine, Hennepin County Medical Center, Minneapolis, MN, USA
| | - Amira Mohamed
- Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Julie Gilbert
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Emily E Bendall
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Auden Bahr
- Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Natasha Halasa
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - H Keipp Talbot
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Carlos G Grijalva
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adrienne Baughman
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kelsey N Womack
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cassandra Johnson
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sydney A Swan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Emilia Koumans
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Meredith L McMorrow
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jennifer L Harcourt
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Lydia J Atherton
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Ashley Burroughs
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Natalie J Thornburg
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Wesley H Self
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam S Lauring
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA.
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9
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Ye Y, Shum MH, Tsui JL, Yu G, Smith DK, Zhu H, Wu JT, Guan Y, Lam TTY. Robust expansion of phylogeny for fast-growing genome sequence data. PLoS Comput Biol 2024; 20:e1011871. [PMID: 38330139 PMCID: PMC10898724 DOI: 10.1371/journal.pcbi.1011871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 02/27/2024] [Accepted: 01/29/2024] [Indexed: 02/10/2024] Open
Abstract
Massive sequencing of SARS-CoV-2 genomes has urged novel methods that employ existing phylogenies to add new samples efficiently instead of de novo inference. 'TIPars' was developed for such challenge integrating parsimony analysis with pre-computed ancestral sequences. It took about 21 seconds to insert 100 SARS-CoV-2 genomes into a 100k-taxa reference tree using 1.4 gigabytes. Benchmarking on four datasets, TIPars achieved the highest accuracy for phylogenies of moderately similar sequences. For highly similar and divergent scenarios, fully parsimony-based and likelihood-based phylogenetic placement methods performed the best respectively while TIPars was the second best. TIPars accomplished efficient and accurate expansion of phylogenies of both similar and divergent sequences, which would have broad biological applications beyond SARS-CoV-2. TIPars is accessible from https://tipars.hku.hk/ and source codes are available at https://github.com/id-bioinfo/TIPars.
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Affiliation(s)
- Yongtao Ye
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P. R. China
- Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong SAR, P. R. China
| | - Marcus H Shum
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P. R. China
- Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong SAR, P. R. China
| | - Joseph L Tsui
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P. R. China
- Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong SAR, P. R. China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - David K Smith
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P. R. China
| | - Huachen Zhu
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P. R. China
- Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong SAR, P. R. China
- Guangdong-Hongkong Joint Laboratory of Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou, Guangdong, P. R. China
- EKIH (Gewuzhikang) Pathogen Research Institute, Futian District, Shenzhen City, Guangdong, P. R. China
| | - Joseph T Wu
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P. R. China
- Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong SAR, P. R. China
| | - Yi Guan
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P. R. China
- Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong SAR, P. R. China
- Guangdong-Hongkong Joint Laboratory of Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou, Guangdong, P. R. China
- EKIH (Gewuzhikang) Pathogen Research Institute, Futian District, Shenzhen City, Guangdong, P. R. China
| | - Tommy Tsan-Yuk Lam
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P. R. China
- Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong SAR, P. R. China
- Guangdong-Hongkong Joint Laboratory of Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou, Guangdong, P. R. China
- EKIH (Gewuzhikang) Pathogen Research Institute, Futian District, Shenzhen City, Guangdong, P. R. China
- Centre for Immunology & Infection Limited, 17W Hong Kong Science & Technology Parks, Hong Kong SAR, P. R. China
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10
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Ma B, Gong H, Xu Q, Gao Y, Guan A, Wang H, Hua K, Luo R, Jin H. Bases-dependent Rapid Phylogenetic Clustering (Bd-RPC) enables precise and efficient phylogenetic estimation in viruses. Virus Evol 2024; 10:veae005. [PMID: 38361823 PMCID: PMC10868571 DOI: 10.1093/ve/veae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/06/2024] [Accepted: 01/22/2024] [Indexed: 02/17/2024] Open
Abstract
Understanding phylogenetic relationships among species is essential for many biological studies, which call for an accurate phylogenetic tree to understand major evolutionary transitions. The phylogenetic analyses present a major challenge in estimation accuracy and computational efficiency, especially recently facing a wave of severe emerging infectious disease outbreaks. Here, we introduced a novel, efficient framework called Bases-dependent Rapid Phylogenetic Clustering (Bd-RPC) for new sample placement for viruses. In this study, a brand-new recoding method called Frequency Vector Recoding was implemented to approximate the phylogenetic distance, and the Phylogenetic Simulated Annealing Search algorithm was developed to match the recoded distance matrix with the phylogenetic tree. Meanwhile, the indel (insertion/deletion) was heuristically introduced to foreign sequence recognition for the first time. Here, we compared the Bd-RPC with the recent placement software (PAGAN2, EPA-ng, TreeBeST) and evaluated it in Alphacoronavirus, Alphaherpesvirinae, and Betacoronavirus by using Split and Robinson-Foulds distances. The comparisons showed that Bd-RPC maintained the highest precision with great efficiency, demonstrating good performance in new sample placement on all three virus genera. Finally, a user-friendly website (http://www.bd-rpc.xyz) is available for users to classify new samples instantly and facilitate exploration of the phylogenetic research in viruses, and the Bd-RPC is available on GitHub (http://github.com/Bin-Ma/bd-rpc).
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Affiliation(s)
- Bin Ma
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
| | - Huimin Gong
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
| | - Qianshuai Xu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
| | - Yuan Gao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
| | - Aohan Guan
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
| | - Haoyu Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
| | - Kexin Hua
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
| | - Rui Luo
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
| | - Hui Jin
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, No.1 Shizishan Street, Wuhan, Hubei 430070, China
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11
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de Bernardi Schneider A, Su M, Hinrichs AS, Wang J, Amin H, Bell J, Wadford DA, O’Toole Á, Scher E, Perry MD, Turakhia Y, De Maio N, Hughes S, Corbett-Detig R. SARS-CoV-2 lineage assignments using phylogenetic placement/UShER are superior to pangoLEARN machine-learning method. Virus Evol 2024; 10:vead085. [PMID: 38361813 PMCID: PMC10868549 DOI: 10.1093/ve/vead085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 12/13/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024] Open
Abstract
With the rapid spread and evolution of SARS-CoV-2, the ability to monitor its transmission and distinguish among viral lineages is critical for pandemic response efforts. The most commonly used software for the lineage assignment of newly isolated SARS-CoV-2 genomes is pangolin, which offers two methods of assignment, pangoLEARN and pUShER. PangoLEARN rapidly assigns lineages using a machine-learning algorithm, while pUShER performs a phylogenetic placement to identify the lineage corresponding to a newly sequenced genome. In a preliminary study, we observed that pangoLEARN (decision tree model), while substantially faster than pUShER, offered less consistency across different versions of pangolin v3. Here, we expand upon this analysis to include v3 and v4 of pangolin, which moved the default algorithm for lineage assignment from pangoLEARN in v3 to pUShER in v4, and perform a thorough analysis confirming that pUShER is not only more stable across versions but also more accurate. Our findings suggest that future lineage assignment algorithms for various pathogens should consider the value of phylogenetic placement.
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Affiliation(s)
- Adriano de Bernardi Schneider
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Michelle Su
- Department of Health and Mental Hygiene, New York City Public Health Laboratory, New York, NY 10016, USA
| | - Angie S Hinrichs
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Jade Wang
- Department of Health and Mental Hygiene, New York City Public Health Laboratory, New York, NY 10016, USA
| | - Helly Amin
- Department of Health and Mental Hygiene, New York City Public Health Laboratory, New York, NY 10016, USA
| | - John Bell
- California Department of Public Health (CDPH), VRDL/COVIDNet, Richmond, CA 94804, USA
| | - Debra A Wadford
- California Department of Public Health (CDPH), VRDL/COVIDNet, Richmond, CA 94804, USA
| | - Áine O’Toole
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Emily Scher
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Marc D Perry
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Yatish Turakhia
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA
| | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SD, UK
| | - Scott Hughes
- Department of Health and Mental Hygiene, New York City Public Health Laboratory, New York, NY 10016, USA
| | - Russ Corbett-Detig
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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12
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Penn MJ, Scheidwasser N, Penn J, Donnelly CA, Duchêne DA, Bhatt S. Leaping through Tree Space: Continuous Phylogenetic Inference for Rooted and Unrooted Trees. Genome Biol Evol 2023; 15:evad213. [PMID: 38085949 PMCID: PMC10745275 DOI: 10.1093/gbe/evad213] [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] [Accepted: 11/16/2023] [Indexed: 12/24/2023] Open
Abstract
Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. However, finding suitable phylogenies from the vast space of possible trees remains challenging. To address this problem, for the first time, we perform both tree exploration and inference in a continuous space where the computation of gradients is possible. This continuous relaxation allows for major leaps across tree space in both rooted and unrooted trees, and is less susceptible to convergence to local minima. Our approach outperforms the current best methods for inference on unrooted trees and, in simulation, accurately infers the tree and root in ultrametric cases. The approach is effective in cases of empirical data with negligible amounts of data, which we demonstrate on the phylogeny of jawed vertebrates. Indeed, only a few genes with an ultrametric signal were generally sufficient for resolving the major lineages of vertebrates. Optimization is possible via automatic differentiation and our method presents an effective way forward for exploring the most difficult, data-deficient phylogenetic questions.
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Affiliation(s)
- Matthew J Penn
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Neil Scheidwasser
- Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Joseph Penn
- Department of Physics, University of Oxford, Oxford, United Kingdom
| | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - David A Duchêne
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Samir Bhatt
- Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom
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13
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Kramer AM, Thornlow B, Ye C, De Maio N, McBroome J, Hinrichs AS, Lanfear R, Turakhia Y, Corbett-Detig R. Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations. Syst Biol 2023; 72:1039-1051. [PMID: 37232476 PMCID: PMC10627557 DOI: 10.1093/sysbio/syad031] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 05/14/2023] [Accepted: 06/22/2023] [Indexed: 05/27/2023] Open
Abstract
Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 data sets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger data sets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established ML implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar data sets with particularly dense sampling and short branch lengths.
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Affiliation(s)
- Alexander M Kramer
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Bryan Thornlow
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Cheng Ye
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA
| | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Jakob McBroome
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Angie S Hinrichs
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Robert Lanfear
- Department of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, ACT 2601, Australia
| | - Yatish Turakhia
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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14
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Sanderson T. Taxonium, a web-based tool for exploring large phylogenetic trees. eLife 2022; 11:e82392. [PMID: 36377483 PMCID: PMC9704803 DOI: 10.7554/elife.82392] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Abstract
The COVID-19 pandemic has resulted in a step change in the scale of sequencing data, with more genomes of SARS-CoV-2 having been sequenced than any other organism on earth. These sequences reveal key insights when represented as a phylogenetic tree, which captures the evolutionary history of the virus, and allows the identification of transmission events and the emergence of new variants. However, existing web-based tools for exploring phylogenies do not scale to the size of datasets now available for SARS-CoV-2. We have developed Taxonium, a new tool that uses WebGL to allow the exploration of trees with tens of millions of nodes in the browser for the first time. Taxonium links each node to associated metadata and supports mutation-annotated trees, which are able to capture all known genetic variation in a dataset. It can either be run entirely locally in the browser, from a server-based backend, or as a desktop application. We describe insights that analysing a tree of five million sequences can provide into SARS-CoV-2 evolution, and provide a tool at cov2tree.org for exploring a public tree of more than five million SARS-CoV-2 sequences. Taxonium can be applied to any tree, and is available at taxonium.org, with source code at github.com/theosanderson/taxonium.
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