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Shukla N, Srivastava N, Srivastava P, Narayan J. Setu: a pipeline for the robust assembly of SARS-CoV-2 genomes. Microbiol Resour Announc 2024; 13:e0023724. [PMID: 38847537 PMCID: PMC11256813 DOI: 10.1128/mra.00237-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 05/10/2024] [Indexed: 07/19/2024] Open
Abstract
Setu is an efficient pipeline integrating currently available open source bioinformatic tools to perform rapid de novo assembly to assist tracking of severe acute respiratory syndrome coronavirus 2 genome evolution in clinical data, being particularly useful for institutions with limited computing resources or personnel not familiar with bioinformatic pipelines.
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Affiliation(s)
- Nityendra Shukla
- CSIR-Institute of Genomics & Integrative Biology, New Delhi, Delhi, India
| | - Neha Srivastava
- Institute of Biotechnology, Amity University, Lucknow, India
| | | | - Jitendra Narayan
- CSIR-Institute of Genomics & Integrative Biology, New Delhi, Delhi, India
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Thomas A, Battenfeld T, Kraiselburd I, Anastasiou O, Dittmer U, Dörr AK, Dörr A, Elsner C, Gosch J, Le-Trilling VTK, Magin S, Scholtysik R, Yilmaz P, Trilling M, Schöler L, Köster J, Meyer F. UnCoVar: a reproducible and scalable workflow for transparent and robust virus variant calling and lineage assignment using SARS-CoV-2 as an example. BMC Genomics 2024; 25:647. [PMID: 38943066 PMCID: PMC11214259 DOI: 10.1186/s12864-024-10539-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/18/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND At a global scale, the SARS-CoV-2 virus did not remain in its initial genotype for a long period of time, with the first global reports of variants of concern (VOCs) in late 2020. Subsequently, genome sequencing has become an indispensable tool for characterizing the ongoing pandemic, particularly for typing SARS-CoV-2 samples obtained from patients or environmental surveillance. For such SARS-CoV-2 typing, various in vitro and in silico workflows exist, yet to date, no systematic cross-platform validation has been reported. RESULTS In this work, we present the first comprehensive cross-platform evaluation and validation of in silico SARS-CoV-2 typing workflows. The evaluation relies on a dataset of 54 patient-derived samples sequenced with several different in vitro approaches on all relevant state-of-the-art sequencing platforms. Moreover, we present UnCoVar, a robust, production-grade reproducible SARS-CoV-2 typing workflow that outperforms all other tested approaches in terms of precision and recall. CONCLUSIONS In many ways, the SARS-CoV-2 pandemic has accelerated the development of techniques and analytical approaches. We believe that this can serve as a blueprint for dealing with future pandemics. Accordingly, UnCoVar is easily generalizable towards other viral pathogens and future pandemics. The fully automated workflow assembles virus genomes from patient samples, identifies existing lineages, and provides high-resolution insights into individual mutations. UnCoVar includes extensive quality control and automatically generates interactive visual reports. UnCoVar is implemented as a Snakemake workflow. The open-source code is available under a BSD 2-clause license at github.com/IKIM-Essen/uncovar.
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Affiliation(s)
- Alexander Thomas
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Thomas Battenfeld
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Ivana Kraiselburd
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Olympia Anastasiou
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Ulf Dittmer
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Ann-Kathrin Dörr
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Adrian Dörr
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Carina Elsner
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Jule Gosch
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Vu Thuy Khanh Le-Trilling
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Institute for the Research on HIV & AIDS-associated Diseases, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Simon Magin
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - René Scholtysik
- Institute for the Research on HIV & AIDS-associated Diseases, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Pelin Yilmaz
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Mirko Trilling
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Institute for the Research on HIV & AIDS-associated Diseases, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Lara Schöler
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Institute for the Research on HIV & AIDS-associated Diseases, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Institute of Cell Biology (Cancer Research), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Johannes Köster
- Bioinformatics and Computational Oncology, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Division of Molecular and Cellular Oncology, Department of Medical Oncology, Harvard Medical School, Boston, MA, USA
| | - Folker Meyer
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany.
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Gauthier NPG, Chorlton SD, Krajden M, Manges AR. Agnostic Sequencing for Detection of Viral Pathogens. Clin Microbiol Rev 2023; 36:e0011922. [PMID: 36847515 PMCID: PMC10035330 DOI: 10.1128/cmr.00119-22] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
The advent of next-generation sequencing (NGS) technologies has expanded our ability to detect and analyze microbial genomes and has yielded novel molecular approaches for infectious disease diagnostics. While several targeted multiplex PCR and NGS-based assays have been widely used in public health settings in recent years, these targeted approaches are limited in that they still rely on a priori knowledge of a pathogen's genome, and an untargeted or unknown pathogen will not be detected. Recent public health crises have emphasized the need to prepare for a wide and rapid deployment of an agnostic diagnostic assay at the start of an outbreak to ensure an effective response to emerging viral pathogens. Metagenomic techniques can nonspecifically sequence all detectable nucleic acids in a sample and therefore do not rely on prior knowledge of a pathogen's genome. While this technology has been reviewed for bacterial diagnostics and adopted in research settings for the detection and characterization of viruses, viral metagenomics has yet to be widely deployed as a diagnostic tool in clinical laboratories. In this review, we highlight recent improvements to the performance of metagenomic viral sequencing, the current applications of metagenomic sequencing in clinical laboratories, as well as the challenges that impede the widespread adoption of this technology.
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Affiliation(s)
- Nick P. G. Gauthier
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Mel Krajden
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Amee R. Manges
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
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Liao H, Cai D, Sun Y. VirStrain: a strain identification tool for RNA viruses. Genome Biol 2022; 23:38. [PMID: 35101081 PMCID: PMC8801933 DOI: 10.1186/s13059-022-02609-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 01/12/2022] [Indexed: 12/18/2022] Open
Abstract
Viruses change constantly during replication, leading to high intra-species diversity. Although many changes are neutral or deleterious, some can confer on the virus different biological properties such as better adaptability. In addition, viral genotypes often have associated metadata, such as host residence, which can help with inferring viral transmission during pandemics. Thus, subspecies analysis can provide important insights into virus characterization. Here, we present VirStrain, a tool taking short reads as input with viral strain composition as output. We rigorously test VirStrain on multiple simulated and real virus sequencing datasets. VirStrain outperforms the state-of-the-art tools in both sensitivity and accuracy.
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Affiliation(s)
- Herui Liao
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, China
| | - Dehan Cai
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, China
| | - Yanni Sun
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, China.
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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] [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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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] [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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