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Honey bees (Apis mellifera) produce an enormous economic value through their pollination activities and play a central role in the biodiversity of entire ecosystems. Recent efforts have revealed the substantial influence that the gut microbiota exert on bee development, food digestion, and homeostasis in general. In this study, deep sequencing was used to characterize prokaryotic viral communities associated with honey bees, which was a blind spot in research up until now. The vast majority of the prokaryotic viral populations are novel at the genus level, and most of the encoded proteins comprise unknown functions. Nevertheless, genomes of bacteriophages were predicted to infect nearly every major beegut bacterium, and functional annotation and auxiliary metabolic gene discovery imply the potential to influence microbial metabolism. Furthermore, undiscovered genes involved in the synthesis of secondary metabolic biosynthetic gene clusters reflect a wealth of previously untapped enzymatic resources hidden in the bee bacteriophage community. [ABSTRACT FROM AUTHOR]

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]