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Santos JD, Sobral D, Pinheiro M, Isidro J, Bogaardt C, Pinto M, Eusébio R, Santos A, Mamede R, Horton DL, Gomes JP, Borges V. INSaFLU-TELEVIR: an open web-based bioinformatics suite for viral metagenomic detection and routine genomic surveillance. Genome Med 2024; 16:61. [PMID: 38659008 PMCID: PMC11044337 DOI: 10.1186/s13073-024-01334-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/15/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Implementation of clinical metagenomics and pathogen genomic surveillance can be particularly challenging due to the lack of bioinformatics tools and/or expertise. In order to face this challenge, we have previously developed INSaFLU, a free web-based bioinformatics platform for virus next-generation sequencing data analysis. Here, we considerably expanded its genomic surveillance component and developed a new module (TELEVIR) for metagenomic virus identification. RESULTS The routine genomic surveillance component was strengthened with new workflows and functionalities, including (i) a reference-based genome assembly pipeline for Oxford Nanopore technologies (ONT) data; (ii) automated SARS-CoV-2 lineage classification; (iii) Nextclade analysis; (iv) Nextstrain phylogeographic and temporal analysis (SARS-CoV-2, human and avian influenza, monkeypox, respiratory syncytial virus (RSV A/B), as well as a "generic" build for other viruses); and (v) algn2pheno for screening mutations of interest. Both INSaFLU pipelines for reference-based consensus generation (Illumina and ONT) were benchmarked against commonly used command line bioinformatics workflows for SARS-CoV-2, and an INSaFLU snakemake version was released. In parallel, a new module (TELEVIR) for virus detection was developed, after extensive benchmarking of state-of-the-art metagenomics software and following up-to-date recommendations and practices in the field. TELEVIR allows running complex workflows, covering several combinations of steps (e.g., with/without viral enrichment or host depletion), classification software (e.g., Kaiju, Kraken2, Centrifuge, FastViromeExplorer), and databases (RefSeq viral genome, Virosaurus, etc.), while culminating in user- and diagnosis-oriented reports. Finally, to potentiate real-time virus detection during ONT runs, we developed findONTime, a tool aimed at reducing costs and the time between sample reception and diagnosis. CONCLUSIONS The accessibility, versatility, and functionality of INSaFLU-TELEVIR are expected to supply public and animal health laboratories and researchers with a user-oriented and pan-viral bioinformatics framework that promotes a strengthened and timely viral metagenomic detection and routine genomics surveillance. INSaFLU-TELEVIR is compatible with Illumina, Ion Torrent, and ONT data and is freely available at https://insaflu.insa.pt/ (online tool) and https://github.com/INSaFLU (code).
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Affiliation(s)
- João Dourado Santos
- Genomics and Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, Portugal
| | - Daniel Sobral
- Genomics and Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, Portugal
| | - Miguel Pinheiro
- Institute of Biomedicine-iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Joana Isidro
- Genomics and Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, Portugal
| | - Carlijn Bogaardt
- Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Surrey, UK
| | - Miguel Pinto
- Genomics and Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, Portugal
| | - Rodrigo Eusébio
- Genomics and Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, Portugal
| | - André Santos
- Genomics and Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, Portugal
| | - Rafael Mamede
- Faculdade de Medicina, Instituto de Microbiologia, Instituto de Medicina Molecular, Universidade de Lisboa, Lisbon, Portugal
| | - Daniel L Horton
- Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Surrey, UK
| | - João Paulo Gomes
- Genomics and Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, Portugal
- Veterinary and Animal Research Centre (CECAV), Faculty of Veterinary Medicine, Lusófona University, Lisbon, Portugal
| | - Vítor Borges
- Genomics and Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, Portugal.
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Diao Z, Zhang Y, Chen Y, Han Y, Chang L, Ma Y, Feng L, Huang T, Zhang R, Li J. Assessing the Quality of Metagenomic Next-Generation Sequencing for Pathogen Detection in Lower Respiratory Infections. Clin Chem 2023; 69:1038-1049. [PMID: 37303219 DOI: 10.1093/clinchem/hvad072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/27/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND Laboratory-developed metagenomic next-generation sequencing (mNGS) assays are increasingly being used for the diagnosis of infectious disease. To ensure comparable results and advance the quality control for the mNGS assay, we initiated a large-scale multicenter quality assessment to scrutinize the ability of mNGS to detect pathogens in lower respiratory infections. METHODS A reference panel containing artificial microbial communities and real clinical samples was used to assess the performance of 122 laboratories. We comprehensively evaluated the reliability, the source of false-positive and false-negative microbes, as well as the ability to interpret the results. RESULTS A wide variety of weighted F1-scores was observed across 122 participants, with a range from 0.20 to 0.97. The majority of false positive microbes (68.56%, 399/582) were introduced from "wet lab." The loss of microbial sequence during wet labs was the chief cause (76.18%, 275/361) of false-negative errors. When the human context is 2 × 105 copies/mL, most DNA and RNA viruses at titers above 104 copies/mL could be detected by >80% of the participants, while >90% of the laboratories could detect bacteria and fungi at titers lower than 103 copies/mL. A total of 10.66% (13/122) to 38.52% (47/122) of the participants could detect the target pathogens but failed to reach a correct etiological diagnosis. CONCLUSIONS This study clarified the sources of false-positive and false-negative results and evaluated the performance of interpreting the results. This study was valuable for clinical mNGS laboratories to improve method development, avoid erroneous results being reported, and implement regulatory quality controls in the clinic.
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Affiliation(s)
- Zhenli Diao
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
| | - Yuanfeng Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
| | - Yuqing Chen
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
| | - Yanxi Han
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
| | - Lu Chang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
| | - Yu Ma
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
| | - Lei Feng
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
| | - Tao Huang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, People's Republic of China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China
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Tast Lahti E, Karamehmedovic N, Riedel H, Blom L, Boel J, Delibato E, Denis M, van Essen-Zandbergen A, Garcia-Fernandez A, Hendriksen R, Heydecke A, van Hoek AHAM, Huby T, Kwit R, Lucarelli C, Lundin K, Michelacci V, Owczarek S, Ring I, Sejer Kjeldgaard J, Sjögren I, Skóra M, Torpdahl M, Ugarte-Ruiz M, Veldman K, Ventola E, Zajac M, Jernberg C. One Health surveillance-A cross-sectoral detection, characterization, and notification of foodborne pathogens. Front Public Health 2023; 11:1129083. [PMID: 36969662 PMCID: PMC10034719 DOI: 10.3389/fpubh.2023.1129083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/16/2023] [Indexed: 03/29/2023] Open
Abstract
Introduction Several Proficiency Test (PT) or External Quality Assessment (EQA) schemes are currently available for assessing the ability of laboratories to detect and characterize enteropathogenic bacteria, but they are usually targeting one sector, covering either public health, food safety or animal health. In addition to sector-specific PTs/EQAs for detection, cross-sectoral panels would be useful for assessment of the capacity to detect and characterize foodborne pathogens in a One Health (OH) perspective and further improving food safety and interpretation of cross-sectoral surveillance data. The aims of the study were to assess the cross-sectoral capability of European public health, animal health and food safety laboratories to detect, characterize and notify findings of the foodborne pathogens Campylobacter spp., Salmonella spp. and Yersinia enterocolitica, and to develop recommendations for future cross-sectoral PTs and EQAs within OH. The PT/EQA scheme developed within this study consisted of a test panel of five samples, designed to represent a theoretical outbreak scenario. Methods A total of 15 laboratories from animal health, public health and food safety sectors were enrolled in eight countries: Denmark, France, Italy, the Netherlands, Poland, Spain, Sweden, and the United Kingdom. The laboratories analyzed the samples according to the methods used in the laboratory and reported the target organisms at species level, and if applicable, serovar for Salmonella and bioserotype for Yersinia. Results All 15 laboratories analyzed the samples for Salmonella, 13 for Campylobacter and 11 for Yersinia. Analytical errors were predominately false negative results. One sample (S. Stockholm and Y. enterocolitica O:3/BT4) with lower concentrations of target organisms was especially challenging, resulting in six out of seven false negative results. These findings were associated with laboratories using smaller sample sizes and not using enrichment methods. Detection of Salmonella was most commonly mandatory to notify within the three sectors in the eight countries participating in the pilot whereas findings of Campylobacter and Y. enterocolitica were notifiable from human samples, but less commonly from animal and food samples. Discussion The results of the pilot PT/EQA conducted in this study confirmed the possibility to apply a cross-sectoral approach for assessment of the joint OH capacity to detect and characterize foodborne pathogens.
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Affiliation(s)
- Elina Tast Lahti
- Department of Epidemiology and Disease Control, National Veterinary Institute (SVA), Uppsala, Sweden
- *Correspondence: Elina Tast Lahti
| | | | - Hilde Riedel
- Department of Biology, Swedish Food Agency, Uppsala, Sweden
| | - Linnea Blom
- Department of Biology, Swedish Food Agency, Uppsala, Sweden
| | - Jeppe Boel
- Department of Bacteria, Parasites and Fungi, Statens Serum Institut (SSI), Copenhagen, Denmark
| | - Elisabetta Delibato
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - Martine Denis
- Research Unit of Hygiene and Quality of Poultry and Pork Products, French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Ploufragan, France
| | - Alieda van Essen-Zandbergen
- Department of Bacteriology, Host-Pathogen Interaction, and Diagnostics Development, Wageningen Bioveterinary Research (WBVR) Part of Wageningen University and Research (WUR), Lelystad, Netherlands
| | | | - Rene Hendriksen
- Technical University of Denmark, The National Food Institute (DTU Food), Copenhagen, Denmark
| | - Anna Heydecke
- Center for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden
| | - Angela H. A. M. van Hoek
- Centre for Infectious Disease Control (Department Zoonoses and Environmental Microbiology), Dutch National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands
| | - Tom Huby
- Animal and Plant Health Agency (APHA), Weybridge, United Kingdom
| | - Renata Kwit
- Department of Microbiology, National Veterinary Research Institute (PIWet), Pulawy, Poland
| | - Claudia Lucarelli
- Department of Infectious Diseases, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - Karl Lundin
- Clinical Microbiology, Uppsala University Hospital, Uppsala, Sweden
| | - Valeria Michelacci
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - Slawomir Owczarek
- Department of Infectious Diseases, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - Isaac Ring
- Animal and Plant Health Agency (APHA), Weybridge, United Kingdom
| | - Jette Sejer Kjeldgaard
- Technical University of Denmark, The National Food Institute (DTU Food), Copenhagen, Denmark
| | | | - Milena Skóra
- Department of Microbiology, National Veterinary Research Institute (PIWet), Pulawy, Poland
| | - Mia Torpdahl
- Department of Bacteria, Parasites and Fungi, Statens Serum Institut (SSI), Copenhagen, Denmark
| | - María Ugarte-Ruiz
- VISAVET Health Surveillance Centre, Universidad Complutense Madrid, Madrid, Spain
| | - Kees Veldman
- Department of Bacteriology, Host-Pathogen Interaction, and Diagnostics Development, Wageningen Bioveterinary Research (WBVR) Part of Wageningen University and Research (WUR), Lelystad, Netherlands
| | - Eleonora Ventola
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - Magdalena Zajac
- Department of Microbiology, National Veterinary Research Institute (PIWet), Pulawy, Poland
| | - Cecilia Jernberg
- Department of Microbiology, Public Health Agency of Sweden, Solna, Sweden
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Carbo EC, Sidorov IA, van Rijn-klink AL, Pappas N, van Boheemen S, Mei H, Hiemstra PS, Eagan TM, Claas ECJ, Kroes ACM, de Vries JJC. Performance of Five Metagenomic Classifiers for Virus Pathogen Detection Using Respiratory Samples from a Clinical Cohort. Pathogens 2022; 11:340. [PMID: 35335664 PMCID: PMC8953373 DOI: 10.3390/pathogens11030340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/21/2022] [Accepted: 03/07/2022] [Indexed: 01/10/2023] Open
Abstract
Viral metagenomics is increasingly applied in clinical diagnostic settings for detection of pathogenic viruses. While several benchmarking studies have been published on the use of metagenomic classifiers for abundance and diversity profiling of bacterial populations, studies on the comparative performance of the classifiers for virus pathogen detection are scarce. In this study, metagenomic data sets (n = 88) from a clinical cohort of patients with respiratory complaints were used for comparison of the performance of five taxonomic classifiers: Centrifuge, Clark, Kaiju, Kraken2, and Genome Detective. A total of 1144 positive and negative PCR results for a total of 13 respiratory viruses were used as gold standard. Sensitivity and specificity of these classifiers ranged from 83 to 100% and 90 to 99%, respectively, and was dependent on the classification level and data pre-processing. Exclusion of human reads generally resulted in increased specificity. Normalization of read counts for genome length resulted in a minor effect on overall performance, however it negatively affected the detection of targets with read counts around detection level. Correlation of sequence read counts with PCR Ct-values varied per classifier, data pre-processing (R2 range 15.1–63.4%), and per virus, with outliers up to 3 log10 reads magnitude beyond the predicted read count for viruses with high sequence diversity. In this benchmarking study, sensitivity and specificity were within the ranges of use for diagnostic practice when the cut-off for defining a positive result was considered per classifier.
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Gauthier MEA, Lelwala RV, Elliott CE, Windell C, Fiorito S, Dinsdale A, Whattam M, Pattemore J, Barrero RA. Side-by-Side Comparison of Post-Entry Quarantine and High Throughput Sequencing Methods for Virus and Viroid Diagnosis. Biology (Basel) 2022; 11:biology11020263. [PMID: 35205129 PMCID: PMC8868628 DOI: 10.3390/biology11020263] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 01/27/2023]
Abstract
Rapid and safe access to new plant genetic stocks is crucial for primary plant industries to remain profitable, sustainable, and internationally competitive. Imported plant species may spend several years in Post Entry Quarantine (PEQ) facilities, undergoing pathogen testing which can impact the ability of plant industries to quickly adapt to new global market opportunities by accessing new varieties. Advances in high throughput sequencing (HTS) technologies provide new opportunities for a broad range of fields, including phytosanitary diagnostics. In this study, we compare the performance of two HTS methods (RNA-Seq and sRNA-Seq) with that of existing PEQ molecular assays in detecting and identifying viruses and viroids from various plant commodities. To analyze the data, we tested several bioinformatics tools which rely on different approaches, including direct-read, de novo, and reference-guided assembly. We implemented VirusReport, a new portable, scalable, and reproducible nextflow pipeline that analyses sRNA datasets to detect and identify viruses and viroids. We raise awareness of the need to evaluate cross-sample contamination when analyzing HTS data routinely and of using methods to mitigate index cross-talk. Overall, our results suggest that sRNA analyzed using VirReport provides opportunities to improve quarantine testing at PEQ by detecting all regulated exotic viruses from imported plants in a single assay.
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Affiliation(s)
- Marie-Emilie A. Gauthier
- eResearch, Research Infrastructure, Academic Division, Queensland University of Technology, Brisbane, QLD 4001, Australia; (M.-E.A.G.); (R.V.L.); (C.W.)
| | - Ruvini V. Lelwala
- eResearch, Research Infrastructure, Academic Division, Queensland University of Technology, Brisbane, QLD 4001, Australia; (M.-E.A.G.); (R.V.L.); (C.W.)
- Science and Surveillance Group, Post Entry Quarantine, Department of Agriculture, Water and the Environment, Mickleham, VIC 3064, Australia; (C.E.E.); (J.P.)
| | - Candace E. Elliott
- Science and Surveillance Group, Post Entry Quarantine, Department of Agriculture, Water and the Environment, Mickleham, VIC 3064, Australia; (C.E.E.); (J.P.)
| | - Craig Windell
- eResearch, Research Infrastructure, Academic Division, Queensland University of Technology, Brisbane, QLD 4001, Australia; (M.-E.A.G.); (R.V.L.); (C.W.)
| | - Sonia Fiorito
- Plant Innovation Centre, Post Entry Quarantine, Department of Agriculture, Water and the Environment, Mickleham, VIC 3064, Australia; (S.F.); (A.D.); (M.W.)
| | - Adrian Dinsdale
- Plant Innovation Centre, Post Entry Quarantine, Department of Agriculture, Water and the Environment, Mickleham, VIC 3064, Australia; (S.F.); (A.D.); (M.W.)
| | - Mark Whattam
- Plant Innovation Centre, Post Entry Quarantine, Department of Agriculture, Water and the Environment, Mickleham, VIC 3064, Australia; (S.F.); (A.D.); (M.W.)
| | - Julie Pattemore
- Science and Surveillance Group, Post Entry Quarantine, Department of Agriculture, Water and the Environment, Mickleham, VIC 3064, Australia; (C.E.E.); (J.P.)
| | - Roberto A. Barrero
- eResearch, Research Infrastructure, Academic Division, Queensland University of Technology, Brisbane, QLD 4001, Australia; (M.-E.A.G.); (R.V.L.); (C.W.)
- Correspondence:
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Han D, Diao Z, Lai H, Han Y, Xie J, Zhang R, Li J. Multilaboratory assessment of metagenomic next-generation sequencing for unbiased microbe detection. J Adv Res 2021; 38:213-222. [PMID: 35572414 PMCID: PMC9091723 DOI: 10.1016/j.jare.2021.09.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/12/2022] Open
Abstract
We developed a set of well-defined reference materials, which is very beneficial to monitor problems in mNGS workflows and identify optimal protocols. The high interlaboratory variability in the identification and quantitation of microbes indicates that the current mNGS protocols are in urgent need of standardization and optimization. The detection rate of mNGS for low-concentration microbes (less than 103 cell/ml) is significantly lower than that of microbes with a concentration of 104 cell/ml and higher. Only 56.7% to 83.3% of the laboratories showed a sufficient ability to obtain clear etiological diagnoses for three simulated cases combined with patient information. Addressing laboratory contamination(false positive) is an urgent task.
Introduction Metagenomic next-generation sequencing (mNGS) assay for detecting infectious agents is now in the stage of being translated into clinical practice. With no approved approaches or guidelines available, laboratories adopt customized mNGS assays to detect clinical samples. However, the accuracy, reliability, and problems of these routinely implemented assays are not clear. Objectives To evaluate the performance of 90 mNGS laboratories under routine testing conditions through analyzing identical samples. Methods Eleven microbial communities were generated using 15 quantitative microbial suspensions. They were used as reference materials to evaluate the false negatives and false positives of participating mNGS protocols, as well as the ability to distinguish genetically similar organisms and to identify true pathogens from other microbes based on fictitious case reports. Results High interlaboratory variability was found in the identification and the quantitative reads per million reads (RPM) values of each microbe in the samples, especially when testing microbes present at low concentrations (1 × 103 cell/ml or less). 42.2% (38/90) of the laboratories reported unexpected microbes (i.e. false positive problem). Only 56.7% (51/90) to 83.3% (75/90) of the laboratories showed a sufficient ability to obtain clear etiological diagnoses for three simulated cases combined with patient information. The analysis of the performance of mNGS in distinguishing genetically similar organisms in three samples revealed that only 56.6% to 63.0% of the laboratories recovered RPM ratios (RPMS. aureus/RPMS. epidermidis) within the range of a 2-fold change of the initial input ratios (indicating a relatively low level of bias). Conclusion The high interlaboratory variability found in both identifying microbes and distinguishing true pathogens emphasizes the urgent need for improving the accuracy and comparability of the results generated across different mNGS laboratories, especially in the detection of low-microbial-biomass samples.
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de Vries JJ, Brown JR, Fischer N, Sidorov IA, Morfopoulou S, Huang J, Munnink BBO, Sayiner A, Bulgurcu A, Rodriguez C, Gricourt G, Keyaerts E, Beller L, Bachofen C, Kubacki J, Cordey S, Laubscher F, Schmitz D, Beer M, Hoeper D, Huber M, Kufner V, Zaheri M, Lebrand A, Papa A, van Boheemen S, Kroes AC, Breuer J, Lopez-Labrador FX, Claas EC. Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples. J Clin Virol 2021; 141:104908. [PMID: 34273858 PMCID: PMC7615111 DOI: 10.1016/j.jcv.2021.104908] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 05/18/2021] [Accepted: 06/30/2021] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Metagenomic sequencing is increasingly being used in clinical settings for difficult to diagnose cases. The performance of viral metagenomic protocols relies to a large extent on the bioinformatic analysis. In this study, the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS) initiated a benchmark of metagenomic pipelines currently used in clinical virological laboratories. METHODS Metagenomic datasets from 13 clinical samples from patients with encephalitis or viral respiratory infections characterized by PCR were selected. The datasets were analyzed with 13 different pipelines currently used in virological diagnostic laboratories of participating ENNGS members. The pipelines and classification tools were: Centrifuge, DAMIAN, DIAMOND, DNASTAR, FEVIR, Genome Detective, Jovian, MetaMIC, MetaMix, One Codex, RIEMS, VirMet, and Taxonomer. Performance, characteristics, clinical use, and user-friendliness of these pipelines were analyzed. RESULTS Overall, viral pathogens with high loads were detected by all the evaluated metagenomic pipelines. In contrast, lower abundance pathogens and mixed infections were only detected by 3/13 pipelines, namely DNASTAR, FEVIR, and MetaMix. Overall sensitivity ranged from 80% (10/13) to 100% (13/13 datasets). Overall positive predictive value ranged from 71-100%. The majority of the pipelines classified sequences based on nucleotide similarity (8/13), only a minority used amino acid similarity, and 6 of the 13 pipelines assembled sequences de novo. No clear differences in performance were detected that correlated with these classification approaches. Read counts of target viruses varied between the pipelines over a range of 2-3 log, indicating differences in limit of detection. CONCLUSION A wide variety of viral metagenomic pipelines is currently used in the participating clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implicating the need for standardization and validation of metagenomic analysis for clinical diagnostic use. Future studies should address the selective effects due to the choice of different reference viral databases.
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Affiliation(s)
- Jutte J.C. de Vries
- Clinical Microbiological Laboratory, department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Julianne R. Brown
- Microbiology, Virology and Infection Prevention & Control, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
| | - Nicole Fischer
- University Medical Center Hamburg-Eppendorf, UKE Institute for Medical Microbiology, Virology and Hygiene, Germany
| | - Igor A. Sidorov
- Clinical Microbiological Laboratory, department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sofia Morfopoulou
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Jiabin Huang
- University Medical Center Hamburg-Eppendorf, UKE Institute for Medical Microbiology, Virology and Hygiene, Germany
| | | | - Arzu Sayiner
- Dokuz Eylul University, Medical Faculty, Izmir, Turkey
| | | | | | | | - Els Keyaerts
- Laboratory of Clinical and Epidemiological Virology (Rega Institute), KU Leuven, Belgium
| | - Leen Beller
- Laboratory of Clinical and Epidemiological Virology (Rega Institute), KU Leuven, Belgium
| | | | - Jakub Kubacki
- Institute of Virology, University of Zurich, Switzerland
| | - Samuel Cordey
- Laboratory of Virology, University Hospitals of Geneva, Geneva, Switzerland
| | - Florian Laubscher
- Laboratory of Virology, University Hospitals of Geneva, Geneva, Switzerland
| | - Dennis Schmitz
- RIVM National Institute for Public Health and Environment, Bilthoven, the Netherlands
| | - Martin Beer
- Friedrich-Loeffler-Institute, Institute of Diagnostic Virology, Greifswald, Germany
| | - Dirk Hoeper
- Friedrich-Loeffler-Institute, Institute of Diagnostic Virology, Greifswald, Germany
| | - Michael Huber
- Institute of Medical Virology, University of Zurich, Switzerland
| | - Verena Kufner
- Institute of Medical Virology, University of Zurich, Switzerland
| | - Maryam Zaheri
- Institute of Medical Virology, University of Zurich, Switzerland
| | | | - Anna Papa
- Department of Microbiology, Medical School, Aristotle University of Thessaloniki, Greece
| | | | - Aloys C.M. Kroes
- Clinical Microbiological Laboratory, department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Judith Breuer
- Microbiology, Virology and Infection Prevention & Control, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - F. Xavier Lopez-Labrador
- Virology Laboratory, Genomics and Health Area, Center for Public Health Research (FISABIO-Public Health), Generalitat Valenciana and Microbiology & Ecology Department, University of Valencia, Spain
- CIBERESP, Instituto de Salud Carlos III, Spain
| | - Eric C.J. Claas
- Clinical Microbiological Laboratory, department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands
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8
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Lau KA, Gonçalves da Silva A, Theis T, Gray J, Ballard SA, Rawlinson WD. Proficiency testing for bacterial whole genome sequencing in assuring the quality of microbiology diagnostics in clinical and public health laboratories. Pathology 2021; 53:902-911. [PMID: 34274166 DOI: 10.1016/j.pathol.2021.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/16/2021] [Accepted: 03/29/2021] [Indexed: 10/20/2022]
Abstract
The adoption of whole genome sequencing (WGS) data over the past decade for pathogen surveillance, and decision-making for infectious diseases has rapidly transformed the landscape of clinical microbiology and public health. However, for successful transition to routine use of these techniques, it is crucial to ensure the WGS data generated meet defined quality standards for pathogen identification, typing, antimicrobial resistance detection and surveillance. Further, the ongoing development of these standards will ensure that the bioinformatic processes are capable of accurately identifying and characterising organisms of interest, and thereby facilitate the integration of WGS into routine clinical and public health laboratory setting. A pilot proficiency testing (PT) program for WGS of infectious agents was developed to facilitate widely applicable standardisation and benchmarking standards for WGS across a range of laboratories. The PT participating laboratories were required to generate WGS data from two bacterial isolates, and submit the raw data for independent bioinformatics analysis, as well as analyse the data with their own processes and answer relevant questions about the data. Overall, laboratories used a diverse range of bioinformatics tools and could generate and analyse high-quality data, either meeting or exceeding the minimum requirements. This pilot has provided valuable insight into the current state of genomics in clinical microbiology and public health laboratories across Australia. It will provide a baseline guide for the standardisation of WGS and enable the development of a PT program that allows an ongoing performance benchmark for accreditation of WGS-based test processes.
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Affiliation(s)
| | - Anders Gonçalves da Silva
- Communicable Diseases Genomics Network (CDGN), Public Health Laboratory Network (PHLN), Australia; Microbiological Diagnostic Unit Public Health Laboratory (MDU PHL), The University of Melbourne at The Peter Doherty Institute for Immunity and Infection, Melbourne, Vic, Australia
| | | | - Joanna Gray
- RCPAQAP Biosecurity, St Leonards, NSW, Australia
| | - Susan A Ballard
- Communicable Diseases Genomics Network (CDGN), Public Health Laboratory Network (PHLN), Australia; Microbiological Diagnostic Unit Public Health Laboratory (MDU PHL), The University of Melbourne at The Peter Doherty Institute for Immunity and Infection, Melbourne, Vic, Australia
| | - William D Rawlinson
- Serology and Virology Division (SAViD) SEALS Microbiology, NSW Health Pathology, SOMS, BABS, Women's and Children's, University of NSW, Australia
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9
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de Vries JJC, Brown JR, Couto N, Beer M, Le Mercier P, Sidorov I, Papa A, Fischer N, Oude Munnink BB, Rodriquez C, Zaheri M, Sayiner A, Hönemann M, Cataluna AP, Carbo EC, Bachofen C, Kubacki J, Schmitz D, Tsioka K, Matamoros S, Höper D, Hernandez M, Puchhammer-Stöckl E, Lebrand A, Huber M, Simmonds P, Claas ECJ, López-Labrador FX. Recommendations for the introduction of metagenomic next-generation sequencing in clinical virology, part II: bioinformatic analysis and reporting. J Clin Virol 2021; 138:104812. [PMID: 33819811 DOI: 10.1016/j.jcv.2021.104812] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 03/20/2021] [Indexed: 12/11/2022]
Abstract
Metagenomic next-generation sequencing (mNGS) is an untargeted technique for determination of microbial DNA/RNA sequences in a variety of sample types from patients with infectious syndromes. mNGS is still in its early stages of broader translation into clinical applications. To further support the development, implementation, optimization and standardization of mNGS procedures for virus diagnostics, the European Society for Clinical Virology (ESCV) Network on Next-Generation Sequencing (ENNGS) has been established. The aim of ENNGS is to bring together professionals involved in mNGS for viral diagnostics to share methodologies and experiences, and to develop application guidelines. Following the ENNGS publication Recommendations for the introduction of mNGS in clinical virology, part I: wet lab procedure in this journal, the current manuscript aims to provide practical recommendations for the bioinformatic analysis of mNGS data and reporting of results to clinicians.
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Affiliation(s)
- Jutte J C de Vries
- Clinical Microbiological Laboratory, department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Julianne R Brown
- Microbiology, Virology and Infection Prevention & Control, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom.
| | - Natacha Couto
- Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, United Kingdom.
| | - Martin Beer
- Friedrich-Loeffler-Institute, Institute of Diagnostic Virology, Greifswald, Germany.
| | | | - Igor Sidorov
- Clinical Microbiological Laboratory, department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Anna Papa
- Department of Microbiology, Medical School, Aristotle University of Thessaloniki, Greece.
| | - Nicole Fischer
- University Medical Center Hamburg-Eppendorf, UKE Institute for Medical Microbiology, Virology and Hygiene, Germany.
| | | | - Christophe Rodriquez
- Department of Virology, University hospital Henri Mondor, Assistance Public des Hopitaux de Paris, Créteil, France.
| | - Maryam Zaheri
- Institute of Medical Virology, University of Zurich, Switzerland.
| | - Arzu Sayiner
- Dokuz Eylul University, Medical Faculty, Department of Medical Microbiology, Izmir, Turkey.
| | - Mario Hönemann
- Institute of Virology, Leipzig University, Leipzig, Germany.
| | - Alba Perez Cataluna
- Department of Preservation and Food Safety Technologies, IATA-CSIC, Paterna, Valencia, Spain.
| | - Ellen C Carbo
- Clinical Microbiological Laboratory, department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands.
| | | | - Jakub Kubacki
- Institute of Virology, University of Zurich, Switzerland.
| | - Dennis Schmitz
- RIVM National Institute for Public Health and Environment, Bilthoven, the Netherlands.
| | - Katerina Tsioka
- Department of Microbiology, Medical School, Aristotle University of Thessaloniki, Greece.
| | - Sébastien Matamoros
- Medical Microbiology and Infection Control, Amsterdam UMC, Amsterdam, the Netherlands.
| | - Dirk Höper
- Friedrich-Loeffler-Institute, Institute of Diagnostic Virology, Greifswald, Germany.
| | - Marta Hernandez
- Laboratory of Molecular Biology and Microbiology, Instituto Tecnologico Agrario de Castilla y Leon, Valladolid, Spain.
| | | | | | - Michael Huber
- Institute of Medical Virology, University of Zurich, Switzerland.
| | - Peter Simmonds
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Eric C J Claas
- Clinical Microbiological Laboratory, department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - F Xavier López-Labrador
- Virology Laboratory, Genomics and Health Area, Centre for Public Health Research (FISABIO-Public Health), Valencia, Spain; Department of Microbiology, Medical School, University of Valencia, Spain; CIBERESP, Instituto de Salud Carlos III, Madrid, Spain.
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10
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Nieuwenhuijse DF, Oude Munnink BB, Koopmans MPG. viromeBrowser: A Shiny App for Browsing Virome Sequencing Analysis Results. Viruses 2021; 13:437. [PMID: 33803225 DOI: 10.3390/v13030437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/26/2021] [Accepted: 03/04/2021] [Indexed: 11/16/2022] Open
Abstract
Experiments in which complex virome sequencing data is generated remain difficult to explore and unpack for scientists without a background in data science. The processing of raw sequencing data by high throughput sequencing workflows usually results in contigs in FASTA format coupled to an annotation file linking the contigs to a reference sequence or taxonomic identifier. The next step is to compare the virome of different samples based on the metadata of the experimental setup and extract sequences of interest that can be used in subsequent analyses. The viromeBrowser is an application written in the opensource R shiny framework that was developed in collaboration with end-users and is focused on three common data analysis steps. First, the application allows interactive filtering of annotations by default or custom quality thresholds. Next, multiple samples can be visualized to facilitate comparison of contig annotations based on sample specific metadata values. Last, the application makes it easy for users to extract sequences of interest in FASTA format. With the interactive features in the viromeBrowser we aim to enable scientists without a data science background to compare and extract annotation data and sequences from virome sequencing analysis results.
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11
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Wylezich C, Calvelage S, Schlottau K, Ziegler U, Pohlmann A, Höper D, Beer M. Next-generation diagnostics: virus capture facilitates a sensitive viral diagnosis for epizootic and zoonotic pathogens including SARS-CoV-2. Microbiome 2021; 9:51. [PMID: 33610182 DOI: 10.1186/s40168-020-00973-z/figures/4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 12/07/2020] [Indexed: 05/18/2023]
Abstract
BACKGROUND The detection of pathogens in clinical and environmental samples using high-throughput sequencing (HTS) is often hampered by large amounts of background information, which is especially true for viruses with small genomes. Enormous sequencing depth can be necessary to compile sufficient information for identification of a certain pathogen. Generic HTS combining with in-solution capture enrichment can markedly increase the sensitivity for virus detection in complex diagnostic samples. METHODS A virus panel based on the principle of biotinylated RNA baits was developed for specific capture enrichment of epizootic and zoonotic viruses (VirBaits). The VirBaits set was supplemented by a SARS-CoV-2 predesigned bait set for testing recent SARS-CoV-2-positive samples. Libraries generated from complex samples were sequenced via generic HTS (without enrichment) and afterwards enriched with the VirBaits set. For validation, an internal proficiency test for emerging epizootic and zoonotic viruses (African swine fever virus, Ebolavirus, Marburgvirus, Nipah henipavirus, Rift Valley fever virus) was conducted. RESULTS The VirBaits set consists of 177,471 RNA baits (80-mer) based on about 18,800 complete viral genomes targeting 35 epizootic and zoonotic viruses. In all tested samples, viruses with both DNA and RNA genomes were clearly enriched ranging from about 10-fold to 10,000-fold for viruses including distantly related viruses with at least 72% overall identity to viruses represented in the bait set. Viruses showing a lower overall identity (38% and 46%) to them were not enriched but could nonetheless be detected based on capturing conserved genome regions. The internal proficiency test supports the improved virus detection using the combination of HTS plus targeted enrichment but also points to the risk of cross-contamination between samples. CONCLUSIONS The VirBaits approach showed a high diagnostic performance, also for distantly related viruses. The bait set is modular and expandable according to the favored diagnostics, health sector, or research question. The risk of cross-contamination needs to be taken into consideration. The application of the RNA-baits principle turned out to be user friendly, and even non-experts can easily use the VirBaits workflow. The rapid extension of the established VirBaits set adapted to actual outbreak events is possible as shown for SARS-CoV-2. Video abstract.
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Affiliation(s)
- Claudia Wylezich
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany.
| | - Sten Calvelage
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Kore Schlottau
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Ute Ziegler
- Institute of Novel and Emerging Infectious Diseases, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Anne Pohlmann
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Dirk Höper
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Martin Beer
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
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12
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Wylezich C, Calvelage S, Schlottau K, Ziegler U, Pohlmann A, Höper D, Beer M. Next-generation diagnostics: virus capture facilitates a sensitive viral diagnosis for epizootic and zoonotic pathogens including SARS-CoV-2. Microbiome 2021; 9:51. [PMID: 33610182 PMCID: PMC7896545 DOI: 10.1186/s40168-020-00973-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 12/07/2020] [Indexed: 05/15/2023]
Abstract
BACKGROUND The detection of pathogens in clinical and environmental samples using high-throughput sequencing (HTS) is often hampered by large amounts of background information, which is especially true for viruses with small genomes. Enormous sequencing depth can be necessary to compile sufficient information for identification of a certain pathogen. Generic HTS combining with in-solution capture enrichment can markedly increase the sensitivity for virus detection in complex diagnostic samples. METHODS A virus panel based on the principle of biotinylated RNA baits was developed for specific capture enrichment of epizootic and zoonotic viruses (VirBaits). The VirBaits set was supplemented by a SARS-CoV-2 predesigned bait set for testing recent SARS-CoV-2-positive samples. Libraries generated from complex samples were sequenced via generic HTS (without enrichment) and afterwards enriched with the VirBaits set. For validation, an internal proficiency test for emerging epizootic and zoonotic viruses (African swine fever virus, Ebolavirus, Marburgvirus, Nipah henipavirus, Rift Valley fever virus) was conducted. RESULTS The VirBaits set consists of 177,471 RNA baits (80-mer) based on about 18,800 complete viral genomes targeting 35 epizootic and zoonotic viruses. In all tested samples, viruses with both DNA and RNA genomes were clearly enriched ranging from about 10-fold to 10,000-fold for viruses including distantly related viruses with at least 72% overall identity to viruses represented in the bait set. Viruses showing a lower overall identity (38% and 46%) to them were not enriched but could nonetheless be detected based on capturing conserved genome regions. The internal proficiency test supports the improved virus detection using the combination of HTS plus targeted enrichment but also points to the risk of cross-contamination between samples. CONCLUSIONS The VirBaits approach showed a high diagnostic performance, also for distantly related viruses. The bait set is modular and expandable according to the favored diagnostics, health sector, or research question. The risk of cross-contamination needs to be taken into consideration. The application of the RNA-baits principle turned out to be user friendly, and even non-experts can easily use the VirBaits workflow. The rapid extension of the established VirBaits set adapted to actual outbreak events is possible as shown for SARS-CoV-2. Video abstract.
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Affiliation(s)
- Claudia Wylezich
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany.
| | - Sten Calvelage
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Kore Schlottau
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Ute Ziegler
- Institute of Novel and Emerging Infectious Diseases, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Anne Pohlmann
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Dirk Höper
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
| | - Martin Beer
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493, Greifswald-Insel Riems, Germany
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13
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Acera Mateos P, Balboa RF, Easteal S, Eyras E, Patel HR. PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses. Sci Rep 2021; 11:3209. [PMID: 33547380 PMCID: PMC7864945 DOI: 10.1038/s41598-021-82043-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/12/2021] [Indexed: 01/30/2023] Open
Abstract
Viral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated method to identify viral co-infections in patient RNA-seq data. We developed PACIFIC, a deep-learning algorithm that accurately detects SARS-CoV-2 and other common RNA respiratory viruses from RNA-seq data. Using in silico data, PACIFIC recovers the presence and relative concentrations of viruses with > 99% precision and recall. PACIFIC accurately detects SARS-CoV-2 and other viral infections in 63 independent in vitro cell culture and patient datasets. PACIFIC is an end-to-end tool that enables the systematic monitoring of viral infections in the current global pandemic.
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Affiliation(s)
- Pablo Acera Mateos
- John Curtin School of Medical Research, Australian National University, Canberra, ACT 2600 Australia
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT 2600 Australia
| | - Renzo F. Balboa
- John Curtin School of Medical Research, Australian National University, Canberra, ACT 2600 Australia
- National Centre for Indigenous Genomics, Australian National University, Canberra, ACT 2600 Australia
| | - Simon Easteal
- John Curtin School of Medical Research, Australian National University, Canberra, ACT 2600 Australia
- National Centre for Indigenous Genomics, Australian National University, Canberra, ACT 2600 Australia
| | - Eduardo Eyras
- John Curtin School of Medical Research, Australian National University, Canberra, ACT 2600 Australia
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT 2600 Australia
- IMIM - Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain
- Catalan Institution for Research and Advanced Studies, 08010 Barcelona, Spain
| | - Hardip R. Patel
- John Curtin School of Medical Research, Australian National University, Canberra, ACT 2600 Australia
- National Centre for Indigenous Genomics, Australian National University, Canberra, ACT 2600 Australia
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14
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Ebinger A, Fischer S, Höper D. A theoretical and generalized approach for the assessment of the sample-specific limit of detection for clinical metagenomics. Comput Struct Biotechnol J 2020; 19:732-742. [PMID: 33552445 PMCID: PMC7822954 DOI: 10.1016/j.csbj.2020.12.040] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/16/2020] [Accepted: 12/24/2020] [Indexed: 12/18/2022] Open
Abstract
Metagenomics is a powerful tool to identify novel or unexpected pathogens, since it is generic and relatively unbiased. The limit of detection (LOD) is a critical parameter for the routine application of methods in the clinical diagnostic context. Although attempts for the determination of LODs for metagenomics next-generation sequencing (mNGS) have been made previously, these were only applicable for specific target species in defined samples matrices. Therefore, we developed and validated a generalized probability-based model to assess the sample-specific LOD of mNGS experiments (LODmNGS). Initial rarefaction analyses with datasets of Borna disease virus 1 human encephalitis cases revealed a stochastic behavior of virus read detection. Based on this, we transformed the Bernoulli formula to predict the minimal necessary dataset size to detect one virus read with a probability of 99%. We validated the formula with 30 datasets from diseased individuals, resulting in an accuracy of 99.1% and an average of 4.5 ± 0.4 viral reads found in the calculated minimal dataset size. We demonstrated by modeling the virus genome size, virus-, and total RNA-concentration that the main determinant of mNGS sensitivity is the virus-sample background ratio. The predicted LODmNGS for the respective pathogenic virus in the datasets were congruent with the virus-concentration determined by RT-qPCR. Theoretical assumptions were further confirmed by correlation analysis of mNGS and RT-qPCR data from the samples of the analyzed datasets. This approach should guide standardization of mNGS application, due to the generalized concept of LODmNGS.
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Affiliation(s)
- Arnt Ebinger
- Institute for Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493 Greifswald-Insel Riems, Mecklenburg-Western Pomerania, Germany
| | - Susanne Fischer
- Institute of Infectology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493 Greifswald-Insel Riems, Mecklenburg-Western Pomerania, Germany
| | - Dirk Höper
- Institute for Diagnostic Virology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493 Greifswald-Insel Riems, Mecklenburg-Western Pomerania, Germany
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15
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Sala C, Mordhorst H, Grützke J, Brinkmann A, Petersen TN, Poulsen C, Cotter PD, Crispie F, Ellis RJ, Castellani G, Amid C, Hakhverdyan M, Guyader SL, Manfreda G, Mossong J, Nitsche A, Ragimbeau C, Schaeffer J, Schlundt J, Tay MYF, Aarestrup FM, Hendriksen RS, Pamp SJ, De Cesare A. Metagenomics-Based Proficiency Test of Smoked Salmon Spiked with a Mock Community. Microorganisms 2020; 8:microorganisms8121861. [PMID: 33255715 PMCID: PMC7760972 DOI: 10.3390/microorganisms8121861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 11/17/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
An inter-laboratory proficiency test was organized to assess the ability of participants to perform shotgun metagenomic sequencing of cold smoked salmon, experimentally spiked with a mock community composed of six bacteria, one parasite, one yeast, one DNA, and two RNA viruses. Each participant applied its in-house wet-lab workflow(s) to obtain the metagenomic dataset(s), which were then collected and analyzed using MG-RAST. A total of 27 datasets were analyzed. Sample pre-processing, DNA extraction protocol, library preparation kit, and sequencing platform, influenced the abundance of specific microorganisms of the mock community. Our results highlight that despite differences in wet-lab protocols, the reads corresponding to the mock community members spiked in the cold smoked salmon, were both detected and quantified in terms of relative abundance, in the metagenomic datasets, proving the suitability of shotgun metagenomic sequencing as a genomic tool to detect microorganisms belonging to different domains in the same food matrix. The implementation of standardized wet-lab protocols would highly facilitate the comparability of shotgun metagenomic sequencing dataset across laboratories and sectors. Moreover, there is a need for clearly defining a sequencing reads threshold, to consider pathogens as detected or undetected in a food sample.
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Affiliation(s)
- Claudia Sala
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy;
| | - Hanne Mordhorst
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kemitorvet, DK-2800 Kgs, 2800 Lyngby, Denmark; (H.M.); (T.N.P.); (C.P.); (F.M.A.); (R.S.H.); (S.J.P.)
| | - Josephine Grützke
- German Federal Institute for Risk Assessment, Department of Biological Safety, 12277 Berlin, Germany;
| | - Annika Brinkmann
- Highly Pathogenic Viruses, ZBS 1, Centre for Biological Threats and Special Pathogens, Robert Koch Institute, 13353 Berlin, Germany; (A.B.); (A.N.)
| | - Thomas N. Petersen
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kemitorvet, DK-2800 Kgs, 2800 Lyngby, Denmark; (H.M.); (T.N.P.); (C.P.); (F.M.A.); (R.S.H.); (S.J.P.)
| | - Casper Poulsen
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kemitorvet, DK-2800 Kgs, 2800 Lyngby, Denmark; (H.M.); (T.N.P.); (C.P.); (F.M.A.); (R.S.H.); (S.J.P.)
| | - Paul D. Cotter
- Teagasc Food Research Centre, Moorepark, APC Microbiome Ireland and Vistamilk, T12 YN60 Co. Cork, Ireland; (P.D.C.); (F.C.)
| | - Fiona Crispie
- Teagasc Food Research Centre, Moorepark, APC Microbiome Ireland and Vistamilk, T12 YN60 Co. Cork, Ireland; (P.D.C.); (F.C.)
| | - Richard J. Ellis
- Surveillance and Laboratory Services Department, Animal and Plant Health Agency, APHA Weybridge, Addlestone, Surrey, KT15 3NB, UK;
| | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40127 Bologna, Italy;
| | - Clara Amid
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK;
| | | | - Soizick Le Guyader
- Laboratoire de Microbiologie, CEDEX 03, 44311 Nantes, France; (S.L.G.); (J.S.)
| | - Gerardo Manfreda
- Department of Agricultural and Food Sciences, University of Bologna, 40064 Ozzano dell’Emilia, Italy;
| | - Joël Mossong
- Epidemiology and Microbial Genomics, Laboratoire National de Santé, L-3555 Dudelange, Luxembourg; (J.M.); (C.R.)
| | - Andreas Nitsche
- Highly Pathogenic Viruses, ZBS 1, Centre for Biological Threats and Special Pathogens, Robert Koch Institute, 13353 Berlin, Germany; (A.B.); (A.N.)
| | - Catherine Ragimbeau
- Epidemiology and Microbial Genomics, Laboratoire National de Santé, L-3555 Dudelange, Luxembourg; (J.M.); (C.R.)
| | - Julien Schaeffer
- Laboratoire de Microbiologie, CEDEX 03, 44311 Nantes, France; (S.L.G.); (J.S.)
| | - Joergen Schlundt
- Nanyang Technological University Food Technology Centre (NAFTEC), Nanyang Technological University (NTU), 62 Nanyang Dr, Singapore 637459, Singapore; (J.S.); (M.Y.F.T.)
| | - Moon Y. F. Tay
- Nanyang Technological University Food Technology Centre (NAFTEC), Nanyang Technological University (NTU), 62 Nanyang Dr, Singapore 637459, Singapore; (J.S.); (M.Y.F.T.)
| | - Frank M. Aarestrup
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kemitorvet, DK-2800 Kgs, 2800 Lyngby, Denmark; (H.M.); (T.N.P.); (C.P.); (F.M.A.); (R.S.H.); (S.J.P.)
| | - Rene S. Hendriksen
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kemitorvet, DK-2800 Kgs, 2800 Lyngby, Denmark; (H.M.); (T.N.P.); (C.P.); (F.M.A.); (R.S.H.); (S.J.P.)
| | - Sünje Johanna Pamp
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kemitorvet, DK-2800 Kgs, 2800 Lyngby, Denmark; (H.M.); (T.N.P.); (C.P.); (F.M.A.); (R.S.H.); (S.J.P.)
| | - Alessandra De Cesare
- Department of Veterinary Medical Sciences, University of Bologna, Via Tolara di Sopra 50, 40064 Ozzano dell’Emilia, Italy
- Correspondence:
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Abstract
One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted.
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Affiliation(s)
- Marica Grossegesse
- Centre
for Biological Threats and Special Pathogens, Highly Pathogenic Viruses
(ZBS 1), Robert Koch Institute, Seestr. 10, Berlin 13353, Germany
| | - Felix Hartkopf
- Microbial
Genomics (NG 1), Robert Koch Institute, Berlin 13353, Germany
- Section
eScience (S.3), Federal Institute for Materials
Research and Testing, Unter den Eichen 87, Berlin 12205, Germany
| | - Andreas Nitsche
- Centre
for Biological Threats and Special Pathogens, Highly Pathogenic Viruses
(ZBS 1), Robert Koch Institute, Seestr. 10, Berlin 13353, Germany
| | - Lars Schaade
- Centre
for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin 13353, Germany
| | - Joerg Doellinger
- Centre
for Biological Threats and Special Pathogens, Proteomics and Spectroscopy
(ZBS 6), Robert Koch Institute, Berlin 13353, Germany
| | - Thilo Muth
- Section
eScience (S.3), Federal Institute for Materials
Research and Testing, Unter den Eichen 87, Berlin 12205, Germany
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17
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Höper D, Grützke J, Brinkmann A, Mossong J, Matamoros S, Ellis RJ, Deneke C, Tausch SH, Cuesta I, Monzón S, Juliá M, Petersen TN, Hendriksen RS, Pamp SJ, Leijon M, Hakhverdyan M, Walsh AM, Cotter PD, Chandrasekaran L, Tay MYF, Schlundt J, Sala C, De Cesare A, Nitsche A, Beer M, Wylezich C. Proficiency Testing of Metagenomics-Based Detection of Food-Borne Pathogens Using a Complex Artificial Sequencing Dataset. Front Microbiol 2020; 11:575377. [PMID: 33250869 PMCID: PMC7672002 DOI: 10.3389/fmicb.2020.575377] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 10/13/2020] [Indexed: 01/16/2023] Open
Abstract
Metagenomics-based high-throughput sequencing (HTS) enables comprehensive detection of all species comprised in a sample with a single assay and is becoming a standard method for outbreak investigation. However, unlike real-time PCR or serological assays, HTS datasets generated for pathogen detection do not easily provide yes/no answers. Rather, results of the taxonomic read assignment need to be assessed by trained personnel to gain information thereof. Proficiency tests are important instruments of validation, harmonization, and standardization. Within the European Union funded project COMPARE [COllaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks in Europe], we conducted a proficiency test to scrutinize the ability to assess diagnostic metagenomics data. An artificial dataset resembling shotgun sequencing of RNA from a sample of contaminated trout was provided to 12 participants with the request to provide a table with per-read taxonomic assignments at species level and a report with a summary and assessment of their findings, considering different categories like pathogen, background, or contaminations. Analysis of the read assignment tables showed that the software used reliably classified the reads taxonomically overall. However, usage of incomplete reference databases or inappropriate data pre-processing caused difficulties. From the combination of the participants' reports with their read assignments, we conclude that, although most species were detected, a number of important taxa were not or not correctly categorized. This implies that knowledge of and awareness for potentially dangerous species and contaminations need to be improved, hence, capacity building for the interpretation of diagnostic metagenomics datasets is necessary.
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Affiliation(s)
- Dirk Höper
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
| | - Josephine Grützke
- Department of Biological Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Annika Brinkmann
- Centre for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - Joël Mossong
- Département de Microbiologie, Laboratoire National de Santé, Dudelange, Luxembourg
| | - Sébastien Matamoros
- Department of Medical Microbiology, Amsterdam UMC University of Amsterdam, Amsterdam, Netherlands
| | | | - Carlus Deneke
- Department of Biological Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Simon H. Tausch
- Department of Biological Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Isabel Cuesta
- Bioinformatics Unit, Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Sara Monzón
- Bioinformatics Unit, Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Miguel Juliá
- Bioinformatics Unit, Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Thomas Nordahl Petersen
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Lyngby, Denmark
| | - Rene S. Hendriksen
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Lyngby, Denmark
| | - Sünje J. Pamp
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Lyngby, Denmark
| | - Mikael Leijon
- Department of Microbiology, National Veterinary Institute (SVA), Uppsala, Sweden
| | - Mikhayil Hakhverdyan
- Department of Microbiology, National Veterinary Institute (SVA), Uppsala, Sweden
| | - Aaron M. Walsh
- Teagasc Food Research Centre, APC Microbiome Ireland and Vistamilk, Moorepark, Ireland
| | - Paul D. Cotter
- Teagasc Food Research Centre, APC Microbiome Ireland and Vistamilk, Moorepark, Ireland
| | - Lakshmi Chandrasekaran
- Nanyang Technological University Food Technology Centre (NAFTEC), Nanyang Technological University (NTU), Singapore, Singapore
| | - Moon Y. F. Tay
- Nanyang Technological University Food Technology Centre (NAFTEC), Nanyang Technological University (NTU), Singapore, Singapore
| | - Joergen Schlundt
- Nanyang Technological University Food Technology Centre (NAFTEC), Nanyang Technological University (NTU), Singapore, Singapore
| | - Claudia Sala
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Alessandra De Cesare
- Department of Veterinary Medical Sciences, University of Bologna, Bologna, Italy
| | - Andreas Nitsche
- Centre for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - Martin Beer
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
| | - Claudia Wylezich
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
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19
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Desdouits M, de Graaf M, Strubbia S, Oude Munnink BB, Kroneman A, Le Guyader FS, Koopmans MPG. Novel opportunities for NGS-based one health surveillance of foodborne viruses. One Health Outlook 2020; 2:14. [PMID: 33829135 PMCID: PMC7993515 DOI: 10.1186/s42522-020-00015-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 05/01/2020] [Indexed: 05/15/2023]
Abstract
Foodborne viral infections rank among the top 5 causes of disease, with noroviruses and hepatitis A causing the greatest burden globally. Contamination of foods by infected food handlers or through environmental pollution are the main sources of foodborne illness, with a lesser role for consumption of products from infected animals. Viral partial genomic sequencing has been used for more than two decades to track foodborne outbreaks and whole genome or metagenomics next-generation-sequencing (NGS) are new additions to the toolbox of food microbiology laboratories. We discuss developments in the field of targeted and metagenomic NGS, with an emphasis on application in food virology, the challenges and possible solutions towards future routine application.
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Affiliation(s)
- Marion Desdouits
- IFREMER, Laboratoire de Microbiologie, LSEM/SG2M, Nantes, France
| | - Miranda de Graaf
- Viroscience Department, Erasmus Medical Centre, Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Sofia Strubbia
- IFREMER, Laboratoire de Microbiologie, LSEM/SG2M, Nantes, France
| | - Bas B. Oude Munnink
- Viroscience Department, Erasmus Medical Centre, Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Annelies Kroneman
- Centre for Infectious Disease Control, National Institute of Public Health and the Environment, Bilthoven, The Netherlands
| | | | - Marion P. G. Koopmans
- Viroscience Department, Erasmus Medical Centre, Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
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20
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Chappell JG, Byaruhanga T, Tsoleridis T, Ball JK, McClure CP. Identification of Infectious Agents in High-Throughput Sequencing Data Sets Is Easily Achievable Using Free, Cloud-Based Bioinformatics Platforms. J Clin Microbiol 2019; 57:e01386-19. [PMID: 31757881 DOI: 10.1128/JCM.01386-19] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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21
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Zamperin G, Lucas P, Cano I, Ryder D, Abbadi M, Stone D, Cuenca A, Vigouroux E, Blanchard Y, Panzarin V. Sequencing of animal viruses: quality data assurance for NGS bioinformatics. Virol J 2019; 16:140. [PMID: 31752912 PMCID: PMC6868765 DOI: 10.1186/s12985-019-1223-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 09/16/2019] [Indexed: 12/30/2022] Open
Abstract
Background Next generation sequencing (NGS) is becoming widely used among diagnostics and research laboratories, and nowadays it is applied to a variety of disciplines, including veterinary virology. The NGS workflow comprises several steps, namely sample processing, library preparation, sequencing and primary/secondary/tertiary bioinformatics (BI) analyses. The latter is constituted by a complex process extremely difficult to standardize, due to the variety of tools and metrics available. Thus, it is of the utmost importance to assess the comparability of results obtained through different methods and in different laboratories. To achieve this goal, we have organized a proficiency test focused on the bioinformatics components for the generation of complete genome sequences of salmonid rhabdoviruses. Methods Three partners, that performed virus sequencing using different commercial library preparation kits and NGS platforms, gathered together and shared with each other 75 raw datasets which were analyzed separately by the participants to produce a consensus sequence according to their own bioinformatics pipeline. Results were then compared to highlight discrepancies, and a subset of inconsistencies were investigated more in detail. Results In total, we observed 526 discrepancies, of which 39.5% were located at genome termini, 14.1% at intergenic regions and 46.4% at coding regions. Among these, 10 SNPs and 99 indels caused changes in the protein products. Overall reproducibility was 99.94%. Based on the analysis of a subset of inconsistencies investigated more in-depth, manual curation appeared the most critical step affecting sequence comparability, suggesting that the harmonization of this phase is crucial to obtain comparable results. The analysis of a calibrator sample allowed assessing BI accuracy, being 99.983%. Conclusions We demonstrated the applicability and the usefulness of BI proficiency testing to assure the quality of NGS data, and recommend a wider implementation of such exercises to guarantee sequence data uniformity among different virology laboratories.
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Affiliation(s)
- Gianpiero Zamperin
- Department of Comparative Biomedical Sciences, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), viale dell'Università 10, 35120, Legnaro (PD), Italy
| | - Pierrick Lucas
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Ploufragan-Plouzané-Niort Laboratory, Viral Genetics and Biosecurity Unit, 22440, Ploufragan, France.,Bretagne Loire University, place Paul Ricoeur CS 54417, 35044, Rennes, France
| | - Irene Cano
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Barrack Road, The Nothe Weymouth, Dorset, DT4 8UB, UK
| | - David Ryder
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Barrack Road, The Nothe Weymouth, Dorset, DT4 8UB, UK
| | - Miriam Abbadi
- Department of Comparative Biomedical Sciences, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), viale dell'Università 10, 35120, Legnaro (PD), Italy
| | - David Stone
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Barrack Road, The Nothe Weymouth, Dorset, DT4 8UB, UK
| | - Argelia Cuenca
- European Union Reference Laboratory for Fish and Crustacean Diseases, DTU aqua, Kemitorvet 202, 2800, Kgs. Lyngby, Denmark
| | - Estelle Vigouroux
- Bretagne Loire University, place Paul Ricoeur CS 54417, 35044, Rennes, France.,French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Ploufragan-Plouzané-Niort Laboratory, Viral Diseases in Fish Unit, 29280, Plouzané, France
| | - Yannick Blanchard
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Ploufragan-Plouzané-Niort Laboratory, Viral Genetics and Biosecurity Unit, 22440, Ploufragan, France. .,Bretagne Loire University, place Paul Ricoeur CS 54417, 35044, Rennes, France.
| | - Valentina Panzarin
- Department of Comparative Biomedical Sciences, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), viale dell'Università 10, 35120, Legnaro (PD), Italy.
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Junier T, Huber M, Schmutz S, Kufner V, Zagordi O, Neuenschwander S, Ramette A, Kubacki J, Bachofen C, Qi W, Laubscher F, Cordey S, Kaiser L, Beuret C, Barbié V, Fellay J, Lebrand A. Viral Metagenomics in the Clinical Realm: Lessons Learned from a Swiss-Wide Ring Trial. Genes (Basel) 2019; 10:E655. [PMID: 31466373 DOI: 10.3390/genes10090655] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 08/21/2019] [Accepted: 08/24/2019] [Indexed: 01/20/2023] Open
Abstract
Shotgun metagenomics using next generation sequencing (NGS) is a promising technique to analyze both DNA and RNA microbial material from patient samples. Mostly used in a research setting, it is now increasingly being used in the clinical realm as well, notably to support diagnosis of viral infections, thereby calling for quality control and the implementation of ring trials (RT) to benchmark pipelines and ensure comparable results. The Swiss NGS clinical virology community therefore decided to conduct a RT in 2018, in order to benchmark current metagenomic workflows used at Swiss clinical virology laboratories, and thereby contribute to the definition of common best practices. The RT consisted of two parts (increments), in order to disentangle the variability arising from the experimental compared to the bioinformatics parts of the laboratory pipeline. In addition, the RT was also designed to assess the impact of databases compared to bioinformatics algorithms on the final results, by asking participants to perform the bioinformatics analysis with a common database, in addition to using their own in-house database. Five laboratories participated in the RT (seven pipelines were tested). We observed that the algorithms had a stronger impact on the overall performance than the choice of the reference database. Our results also suggest that differences in sample preparation can lead to significant differences in the performance, and that laboratories should aim for at least 5-10 Mio reads per sample and use depth of coverage in addition to other interpretation metrics such as the percent of coverage. Performance was generally lower when increasing the number of viruses per sample. The lessons learned from this pilot study will be useful for the development of larger-scale RTs to serve as regular quality control tests for laboratories performing NGS analyses of viruses in a clinical setting.
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Affiliation(s)
- Jobin John Jacob
- Department of Clinical Microbiology, Christian Medical College, Vellore - 632 004, Tamil Nadu, India
| | - Balaji Veeraraghavan
- Department of Clinical Microbiology, Christian Medical College, Vellore - 632 004, Tamil Nadu, India
| | - Karthick Vasudevan
- Department of Clinical Microbiology, Christian Medical College, Vellore - 632 004, Tamil Nadu, India
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