Background As the use of nanopore sequencing for metagenomic analysis increases, tools capable of performing long-read taxonomic classification (ie. determining the composition of a sample) in a fast and accurate manner are needed. Existing tools were either designed for short-read data (eg. Centrifuge), take days to analyse modern sequencer outputs (eg. MetaMaps) or suffer from suboptimal accuracy (eg. CDKAM). Additionally, all tools require command line expertise and do not scale in the cloud. Results We present BugSeq, a novel, highly accurate metagenomic classifier for nanopore reads. We evaluate BugSeq on simulated data, mock microbial communities and real clinical samples. On the ZymoBIOMICS Even and Log communities, BugSeq (F1 = 0.95 at species level) offers better read classification than MetaMaps (F1 = 0.89–0.94) in a fraction of the time. BugSeq significantly improves on the accuracy of Centrifuge (F1 = 0.79–0.93) and CDKAM (F1 = 0.91–0.94) while offering competitive run times. When applied to 41 samples from patients with lower respiratory tract infections, BugSeq produces greater concordance with microbiological culture and qPCR compared with “What’s In My Pot” analysis. Conclusion BugSeq is deployed to the cloud for easy and scalable long-read metagenomic analyses. BugSeq is freely available for non-commercial use at https://bugseq.com/free.

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Readhead et al. recently reported in Neuron the detection and association of human herpesviruses 6A (HHV6A) and 7 (HHV7) with Alzheimer’s disease by shotgun sequencing. I was skeptical of the specificity of their modified Viromescan bioinformatics method and subsequent analysis for numerous reasons. Using their supplementary data, the prevalence of variola virus, the etiological agent of the eradicated disease smallpox, can be calculated at 97.5% of their Mount Sinai Brain Bank dataset. Reanalysis of Readhead et al.’s data using highly sensitive and specific alternative methods finds no HHV7 reads in their samples; HHV6A reads were found in only 2 out of their top 15 samples sorted by reported HHV6A abundance. Finally, recreation of Readhead et al.’s modified Viromescan method identifies reasons for its low specificity.

In some parts of the world, Corynebacterium diphtheriae has reemerged as a pathogen, especially as a cause of infections among impoverished and marginalized populations. We performed whole-genome sequencing (WGS) on all cutaneous C. diphtheriae isolates (n = 56) from Vancouver’s inner-city population over a 3-year time period (2015 to 2018). All isolates with complete genome assembly were toxin negative, contained a common set of 22 virulence factors, and shared a highly conserved accessory genome. One of our isolates harbored a novel plasmid conferring macrolide and lincosamide resistance. Fifty-two out of 56 isolates were multilocus sequence type 76, and single nucleotide variants (SNV) and core-genome multilocus sequence typing (cgMLST) analysis demonstrated tight clustering of our isolates relative to all publicly available C. diphtheriae genomes. All sequence type 76 (ST76) study isolates were within a median of 22 SNVs and 13 cgMLST alleles of each other, while NCBI genomes were within a median of 17,436 SNVs and 1,552 cgMLST alleles of each other (both P < 2.2 × 10−16). A single strain of C. diphtheriae appears to be causing cutaneous infections in the low-income population of Vancouver. Further research is needed to elucidate transmission networks in our study population and standardize C. diphtheriae epidemiological typing when whole genomes are sequenced.

Objectives The COVID-19 pandemic and ensuing public health emergency has emphasized the need to study SARS-CoV-2 pathogenesis. The human microbiome has been shown to regulate the host immune system and may influence host susceptibility to viral infection, as well as disease severity. Several studies have assessed whether compositional alterations in the nasopharyngeal microbiota are associated with SARS-CoV-2 infection. However, the results of these studies were varied, and many did not account for disease severity. This study aims to examine whether compositional differences in the nasopharyngeal microbiota are associated with SARS-CoV-2 infection status and disease severity. Methods We performed Nanopore full-length 16S rRNA sequencing on 194 nasopharyngeal swab specimens from hospitalized and community-dwelling SARS-CoV-2-infected and uninfected individuals. Sequence data analysis was performed using the BugSeq 16S analysis pipeline. Results We found significant beta (PERMANOVA p < 0.05), but not alpha (Kruskal-Wallis p > 0.05) diversity differences in the nasopharyngeal microbiota among our study groups. We identified several differentially abundant taxa associated with SARS-CoV-2 infection status and disease severity using ALDEx2. Finally, we observed a trend towards higher abundance of Enterobacteriaceae in specimens from hospitalized SARS-CoV-2-infected patients. Conclusions This study identified several alterations in the nasopharyngeal microbiome associated with SARS-CoV-2 infection status and disease severity. Understanding the role of the microbiome in infection susceptibility and severity may open new avenues of research for disease prevention and treatment.