Jutte J C de Vries1, Julianne R Brown2, Nicole Fischer3, Igor A Sidorov4, Sofia Morfopoulou5, Jiabin Huang6, Bas B Oude Munnink7, Arzu Sayiner8, Alihan Bulgurcu9, Christophe Rodriguez10, Guillaume Gricourt11, Els Keyaerts12, Leen Beller13, Claudia Bachofen14, Jakub Kubacki15, Cordey Samuel16, Laubscher Florian17, Schmitz Dennis18, Martin Beer19, Dirk Hoeper20, Michael Huber21, Verena Kufner22, Maryam Zaheri23, Aitana Lebrand24, Anna Papa25, Sander van Boheemen26, Aloys C M Kroes27, Judith Breuer28, F Xavier Lopez-Labrador29, Eric C J Claas30. 1. Clinical Microbiological Laboratory, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: jjcdevries@lumc.nl. 2. Microbiology, Virology and Infection Prevention & Control, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom. Electronic address: julianne.brown@gosh.nhs.uk. 3. University Medical Center Hamburg-Eppendorf, UKE Institute for Medical Microbiology, Virology and Hygiene, Germany. Electronic address: nfischer@uke.de. 4. Clinical Microbiological Laboratory, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: I.A.Sidorov@lumc.nl. 5. Division of Infection and Immunity, University College London, London, United Kingdom. Electronic address: sofia.morfopoulou.10@ucl.ac.uk. 6. University Medical Center Hamburg-Eppendorf, UKE Institute for Medical Microbiology, Virology and Hygiene, Germany. Electronic address: j.huang@uke.de. 7. Viroscience, Erasmus Medical Center, Rotterdam, the Netherlands. Electronic address: b.oudemunnink@erasmusmc.nl. 8. Dokuz Eylul University, Medical Faculty, Izmir, Turkey. Electronic address: arzu.sayiner@deu.edu.tr. 9. Hospital Henri Mondor, Paris, France. 10. Hospital Henri Mondor, Paris, France. Electronic address: christophe.rodriguez@aphp.fr. 11. Hospital Henri Mondor, Paris, France. Electronic address: guillaume.gricourt@aphp.fr. 12. Laboratory of Clinical and Epidemiological Virology (Rega Institute), KU Leuven, Belgium. Electronic address: els.keyaerts@kuleuven.be. 13. Laboratory of Clinical and Epidemiological Virology (Rega Institute), KU Leuven, Belgium. Electronic address: leen.beller@kuleuven.be. 14. Institute of Virology, University of Zurich, Switzerland. Electronic address: claudia.bachofen@uzh.ch. 15. Institute of Virology, University of Zurich, Switzerland. Electronic address: jakub.kubacki@uzh.ch. 16. Laboratory of Virology, University Hospitals of Geneva, Geneva, Switzerland. Electronic address: Samuel.Cordey@hcuge.ch. 17. Laboratory of Virology, University Hospitals of Geneva, Geneva, Switzerland. Electronic address: Florian.Laubscher@hcuge.ch. 18. RIVM National Institute for Public Health and Environment, Bilthoven, the Netherlands. Electronic address: Dennis.Schmitz@RIVM.nl. 19. Friedrich-Loeffler-Institute, Institute of Diagnostic Virology, Greifswald, Germany. Electronic address: martin.beer@fli.de. 20. Friedrich-Loeffler-Institute, Institute of Diagnostic Virology, Greifswald, Germany. Electronic address: dirk.hoeper@fli.de. 21. Institute of Medical Virology, University of Zurich, Switzerland. Electronic address: huber.michael@virology.uzh.ch. 22. Institute of Medical Virology, University of Zurich, Switzerland. Electronic address: kufner.verena@virology.uzh.ch. 23. Institute of Medical Virology, University of Zurich, Switzerland. Electronic address: zaheri.maryam@virology.uzh.ch. 24. Swiss Institute of Bioinformatics, Geneva, Switzerland. Electronic address: aitana.lebrand@sib.swiss. 25. Department of Microbiology, Medical School, Aristotle University of Thessaloniki, Greece. Electronic address: annap@auth.gr. 26. Viroscience, Erasmus Medical Center, Rotterdam, the Netherlands. Electronic address: s.vanboheemen@erasmusmc.nl. 27. Clinical Microbiological Laboratory, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: A.C.M.Kroes@lumc.nl. 28. 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. Electronic address: breuej@gosh.nhs.uk. 29. 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. Electronic address: F.Xavier.Lopez@uv.es. 30. Clinical Microbiological Laboratory, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: E.C.J.Claas@lumc.nl.
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.
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.
Authors: Ellen C Carbo; Anne Russcher; Margriet E M Kraakman; Caroline S de Brouwer; Igor A Sidorov; Mariet C W Feltkamp; Aloys C M Kroes; Eric C J Claas; Jutte J C de Vries Journal: Pathogens Date: 2022-02-11
Authors: Franziska Hufsky; Ana Abecasis; Patricia Agudelo-Romero; Magda Bletsa; Katherine Brown; Claudia Claus; Stefanie Deinhardt-Emmer; Li Deng; Caroline C Friedel; María Inés Gismondi; Evangelia Georgia Kostaki; Denise Kühnert; Urmila Kulkarni-Kale; Karin J Metzner; Irmtraud M Meyer; Laura Miozzi; Luca Nishimura; Sofia Paraskevopoulou; Alba Pérez-Cataluña; Janina Rahlff; Emma Thomson; Charlotte Tumescheit; Lia van der Hoek; Lore Van Espen; Anne-Mieke Vandamme; Maryam Zaheri; Neta Zuckerman; Manja Marz Journal: Viruses Date: 2022-07-12 Impact factor: 5.818
Authors: Ellen C Carbo; Igor A Sidorov; Anneloes L van Rijn-Klink; Nikos Pappas; Sander van Boheemen; Hailiang Mei; Pieter S Hiemstra; Tomas M Eagan; Eric C J Claas; Aloys C M Kroes; Jutte J C de Vries Journal: Pathogens Date: 2022-03-11