Dataset Information

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples.

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.

SUBMITTER: de Vries JJC

PROVIDER: S-EPMC7615111 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Publications

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples.

de Vries Jutte J C JJC Brown Julianne R JR Fischer Nicole N Sidorov Igor A IA Morfopoulou Sofia S Huang Jiabin J Munnink Bas B Oude BBO Sayiner Arzu A Bulgurcu Alihan A Rodriguez Christophe C Gricourt Guillaume G Keyaerts Els E Beller Leen L Bachofen Claudia C Kubacki Jakub J Samuel Cordey C Florian Laubscher L Dennis Schmitz S Beer Martin M Hoeper Dirk D Huber Michael M Kufner Verena V Zaheri Maryam M Lebrand Aitana A Papa Anna A van Boheemen Sander S Kroes Aloys C M ACM Breuer Judith J Lopez-Labrador F Xavier FX Claas Eric C J ECJ

Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology 20210708

<h4>Introduction</h4>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.<h4>Methods</h4>Metagenomic datasets from 13 clinical samples from patients with ence ...[more]

PMID: 34273858

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Dataset Information

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples.

Introduction

Methods

Results

Conclusion

Publications

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples.

Similar Datasets

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