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Chao E, Chato C, Vender R, Olabode AS, Ferreira RC, Poon AFY. Molecular source attribution. PLoS Comput Biol 2022; 18:e1010649. [PMID: 36395093 PMCID: PMC9671344 DOI: 10.1371/journal.pcbi.1010649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Elisa Chao
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Connor Chato
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Reid Vender
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
- School of Medicine, Queen’s University, Kingston, Ontario, Canada
| | - Abayomi S. Olabode
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Roux-Cil Ferreira
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Art F. Y. Poon
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
- * E-mail:
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2
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Liu Z, Ma Y, Cheng Q, Liu Z. Finding Asymptomatic Spreaders in a COVID-19 Transmission Network by Graph Attention Networks. Viruses 2022; 14:v14081659. [PMID: 36016280 PMCID: PMC9413350 DOI: 10.3390/v14081659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/21/2022] [Accepted: 07/24/2022] [Indexed: 02/06/2023] Open
Abstract
In the COVID-19 epidemic the mildly symptomatic and asymptomatic infections generate a substantial portion of virus spread; these undetected individuals make it difficult to assess the effectiveness of preventive measures as most epidemic prevention strategies are based on the detected data. Effectively identifying the undetected infections in local transmission will be of great help in COVID-19 control. In this work, we propose an RNA virus transmission network representation model based on graph attention networks (RVTR); this model is constructed using the principle of natural language processing to learn the information of gene sequence and using a graph attention network to catch the topological character of COVID-19 transmission networks. Since SARS-CoV-2 will mutate when it spreads, our approach makes use of graph context loss function, which can reflect that the genetic sequence of infections with close spreading relation will be more similar than those with a long distance, to train our model. Our approach shows its ability to find asymptomatic spreaders both on simulated and real COVID-19 datasets and performs better when compared with other network representation and feature extraction methods.
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Affiliation(s)
- Zeyi Liu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; (Z.L.); (Z.L.)
- Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Yang Ma
- College of Systems Engineering, Aviation University of Air Force, Changchun 130000, China;
| | - Qing Cheng
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; (Z.L.); (Z.L.)
- Correspondence:
| | - Zhong Liu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; (Z.L.); (Z.L.)
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3
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Perera D, Perks B, Potemkin M, Liu A, Gordon PMK, Gill MJ, Long Q, van Marle G. Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection. PLoS One 2021; 16:e0261422. [PMID: 34910769 PMCID: PMC8673622 DOI: 10.1371/journal.pone.0261422] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 12/01/2021] [Indexed: 11/18/2022] Open
Abstract
The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help guide public health responses. We finetuned established phylogenetic pipelines using published SARS-CoV-2 genomic data to examine reasonable estimate transmission networks with the inference of unsampled infection sources. The system utilised Bayesian phylogenetics and TransPhylo to capture the evolutionary and infection dynamics of SARS-CoV-2. Our analyses gave insight into the transmissions within a population including unsampled sources of infection and the results aligned with epidemiological observations. We were able to observe the effects of preventive measures in Canada's "Atlantic bubble" and in populations such as New York State. The tools also inferred the cross-species disease transmission of SARS-CoV-2 transmission from humans to lions and tigers in New York City's Bronx Zoo. These phylogenetic tools offer a powerful approach in response to both the COVID-19 and other emerging infectious disease outbreaks.
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Affiliation(s)
- Deshan Perera
- Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, AB, Canada
| | - Ben Perks
- Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, AB, Canada
| | - Michael Potemkin
- Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, AB, Canada
| | - Andy Liu
- International Baccalaureate Diploma program, Sir Winston Churchill High School, Calgary, AB, Canada
| | - Paul M. K. Gordon
- Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, AB, Canada
| | - M. John Gill
- Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, AB, Canada
- Department of Microbiology, Immunology, and Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Quan Long
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Medical Genetics, and Mathematics & Statistics, Alberta Children’s Hospital Research Institute, O’Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
| | - Guido van Marle
- Department of Microbiology, Immunology, and Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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4
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Cao C, He J, Mak L, Perera D, Kwok D, Wang J, Li M, Mourier T, Gavriliuc S, Greenberg M, Morrissy AS, Sycuro LK, Yang G, Jeffares DC, Long Q. Reconstruction of Microbial Haplotypes by Integration of Statistical and Physical Linkage in Scaffolding. Mol Biol Evol 2021; 38:2660-2672. [PMID: 33547786 PMCID: PMC8136496 DOI: 10.1093/molbev/msab037] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
DNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or "haplotypes." However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics, and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here, we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools tailored to specific organismal systems, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of within-patient HIV populations previously unobserved in single position-based analysis.
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Affiliation(s)
- Chen Cao
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Jingni He
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada,Department of Cardiology, Xiangya Hospital, Central South University, Changsha, China
| | - Lauren Mak
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada,Present address: Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medicine of Cornell University, New York, NY, USA
| | - Deshan Perera
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Devin Kwok
- Department of Mathematics & Statistics, University of Calgary, Calgary, AB, Canada
| | - Jia Wang
- Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Minghao Li
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Tobias Mourier
- Pathogen Genomics Laboratory, Biological and Environmental Sciences and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Stefan Gavriliuc
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Matthew Greenberg
- Department of Mathematics & Statistics, University of Calgary, Calgary, AB, Canada
| | - A Sorana Morrissy
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Laura K Sycuro
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada,Department of Microbiology, Immunology, and Infectious Diseases, Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - Guang Yang
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada,Department of Medical Genetics, University of Calgary, Calgary, AB, Canada
| | - Daniel C Jeffares
- Department of Biology, York Biomedical Research Institute, University of York, York, United Kingdom
| | - Quan Long
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada,Department of Mathematics & Statistics, University of Calgary, Calgary, AB, Canada,Department of Medical Genetics, University of Calgary, Calgary, AB, Canada,Hotchkiss Brain Institute, O’Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada,Corresponding author: E-mail:
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Didelot X, Kendall M, Xu Y, White PJ, McCarthy N. Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo. Curr Protoc 2021; 1:e60. [PMID: 33617114 PMCID: PMC7995038 DOI: 10.1002/cpz1.60] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference.
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Affiliation(s)
- Xavier Didelot
- School of Life Sciences and Department of StatisticsUniversity of WarwickUnited Kingdom
| | - Michelle Kendall
- School of Life Sciences and Department of StatisticsUniversity of WarwickUnited Kingdom
| | - Yuanwei Xu
- Center for Computational Biology, Institute of Cancer and Genomic SciencesUniversity of BirminghamUnited Kingdom
| | - Peter J. White
- Department of Infectious Disease Epidemiology, School of Public HealthImperial College LondonUnited Kingdom
- Medical Research Council Centre for Global Infectious Disease Analysis, School of Public HealthImperial College LondonUnited Kingdom
- National Institute for Health Research Health Protection Research Unit in Modelling and Health Economics, School of Public HealthImperial College LondonUnited Kingdom
- Modelling and Economics Unit, National Infection ServicePublic Health EnglandLondonUnited Kingdom
| | - Noel McCarthy
- Warwick Medical SchoolUniversity of WarwickUnited Kingdom
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