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ABSTRACT Norovirus is the primary cause of viral gastroenteritis (GE). To investigate norovirus epidemiology, there is a need for whole-genome sequencing and reference sets consisting of complete genomes. To investigate the potential of shotgun metagenomic sequencing on the Illumina platform for whole-genome sequencing, 71 reverse transcriptase quantitative PCR (RT-qPCR) norovirus positive-feces (threshold cycle [CT],

In 2018, an upsurge in echovirus 30 (E30) infections was reported in Europe. We conducted a large-scale epidemiologic and evolutionary study of 1,329 E30 strains collected in 22 countries in Europe during 2016–2018. Most E30 cases affected persons 0–4 years of age (29%) and 25–34 years of age (27%). Sequences were divided into 6 genetic clades (G1–G6). Most (53%) sequences belonged to G1, followed by G6 (23%), G2 (17%), G4 (4%), G3 (0.3%), and G5 (0.2%). Each clade encompassed unique individual recombinant forms; G1 and G4 displayed >2 unique recombinant forms. Rapid turnover of new clades and recombinant forms occurred over time. Clades G1 and G6 dominated in 2018, suggesting the E30 upsurge was caused by emergence of 2 distinct clades circulating in Europe. Investigation into the mechanisms behind the rapid turnover of E30 is crucial for clarifying the epidemiology and evolution of these enterovirus infections.

Mumps cases continue to occur, also in countries with a relatively high vaccination rate. The last major outbreaks of mumps in the Netherlands were in 2009-2012 and thereafter, only small clusters and single cases were reported. Molecular epidemiology can provide insights in the circulation of mumps viruses. The aims of the present study were to analyze the molecular epidemiology of mumps viruses in the Netherlands in 2017-2019 and to compare the phylogenetic trees built from sequence data of near complete mumps virus genomes or from the SH gene and non-coding regions (SH+NCRs). To this end, Sanger sequence data from SH+NCRs were analyzed from 82 mumps genotype G viruses. In addition, near complete genomes were obtained from 10 mumps virus isolates using next-generation sequencing. Analysis of SH+NCRs sequences of mumps genotype G viruses revealed the presence of two major genetic lineages in the Netherlands, which was confirmed by analysis of near complete genomes. Comparison of phylogenetic trees built with SH+NCRs or near complete genomes indicated that the topology was similar, while somewhat longer branches were present in the phylogenetic tree with near complete genomes. These results confirm that analysis of SH + NCRs sequence data is a useful approach for molecular surveillance. Furthermore, data from recent mumps genotype G viruses might indicate (intermittent) circulation of mumps genotype G viruses in the Netherlands in 2017-2019.

Metagenomics poses opportunities for clinical and public health virology applications by offering a way to assess complete taxonomic composition of a clinical sample in an unbiased way. However, the techniques required are complicated and analysis standards have yet to develop. This, together with the wealth of different tools and workflows that have been proposed, poses a barrier for new users. We evaluated 49 published computational classification workflows for virus metagenomics in a literature review. To this end, we described the methods of existing workflows by breaking them up into five general steps and assessed their ease-of-use and validation experiments. Performance scores of previous benchmarks were summarized and correlations between methods and performance were investigated. We indicate the potential suitability of the different workflows for (1) time-constrained diagnostics, (2) surveillance and outbreak source tracing, (3) detection of remote homologies (discovery), and (4) biodiversity studies. We provide two decision trees for virologists to help select a workflow for medical or biodiversity studies, as well as directions for future developments in clinical viral metagenomics.

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]