Genome sequencing technologies continue to develop with remarkable pace, yet analytical approaches for reconstructing and classifying viral genomes from mixed samples remain limited in their performance and usability. Existing solutions generally target expert users and often have unclear scope, making it challenging to critically evaluate their performance. There is a growing need for intuitive analytical tooling for researchers lacking specialist computing expertise and that is applicable in diverse experimental circumstances. Notable technical challenges have impeded progress; for example, fragments of viral genomes are typically orders of magnitude less abundant than those of host, bacteria, and/or other organisms in clinical and environmental metagenomes; observed viral genomes often deviate considerably from reference genomes demanding use of exhaustive alignment approaches; high intrapopulation viral diversity can lead to ambiguous sequence reconstruction; and finally, the relatively few documented viral reference genomes compared to the estimated number of distinct viral taxa renders classification problematic. Various software tools have been developed to accommodate the unique challenges and use cases associated with characterizing viral sequences; however, the quality of these tools varies, and their use often necessitates computing expertise or access to powerful computers, thus limiting their usefulness to many researchers. In this review, we consider the general and application-specific challenges posed by viral sequencing and analysis, outline the landscape of available tools and methodologies, and propose ways of overcoming the current barriers to effective analysis.

We compared the behavior of two approaches (Cluster Picker and HIV-TRACE) at varying genetic distances to identify transmission clusters. We used three HIV gp41 sequence data sets originating from the Rakai Community Cohort Study: (1) next-generation sequences (NGS) from nine linked couples; (2) NGS from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n=1022) containing 91 epidemiologically linked heterosexual couples. We calculated the optimal genetic distance threshold to separate linked versus unlinked NGS datasets using a receiver operating curve analysis (ROC). We evaluated the number, size and composition of clusters detected by Cluster Picker and HIV-TRACE at six genetic distance thresholds (1%-5.3%) on all three datasets. We further tested the effect of using all NGS sequences, versus only a single variant for each patient/time point, for data sets (1) and (2). The optimal gp41 genetic distance threshold to distinguish linked and unlinked couples and individuals was 5.3% and 4%, respectively. HIV-TRACE tended to detect larger and fewer clusters, whereas Cluster Picker detected more clusters containing only two sequences. For NGS data sets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distance, respectively. However at 5.3% genetic distance, 20% of couples in data set (3) did not cluster using either program, and for >1/3 of couples cluster assignment were discordant. We suggest caution in choosing thresholds for clustering analyses in a generalized epidemic.

Introduction: Whole genome sequencing (WGS) of bacterial isolates can be used to identify antimicrobial resistance (AMR) genes. Previous studies have shown that genotype-based AMR has variable accuracy for predicting carbapenem resistance in carbapenem-resistant Enterobacterales (CRE); however, the majority of these studies used short-read platforms (e.g. Illumina) to generate sequence data. In this study, our objective was to determine whether Oxford Nanopore Technologies (ONT) long-read WGS would improve detection of carbapenem AMR genes with respect to short-read only WGS for nine clinical CRE samples. We measured the minimum inhibitory breakpoint (MIC) using two phenotype assays (MicroScan and ETEST) for six antibiotics, including two carbapenems (meropenem and ertapenem) and four non-carbapenems (gentamicin, ciprofloxacin, cefepime, and trimethoprim/sulfamethoxazole). We generated short-read data using the Illumina NextSeq and long-read data using the ONT MinION. Four assembly methods were compared: ONT-only assembly; ONT-only assembly plus short-read polish; ONT + short-read hybrid assembly plus short-read polish; short-read only assembly. Results: Consistent with previous studies, our results suggest that the hybrid assembly produced the highest quality results as measured by gene completeness and contig circularization. However, ONT-only methods had minimal impact on the detection of AMR genes and plasmids compared to short-read methods, although, notably, differences in gene copy number differed between methods. All four assembly methods showed identical presence/absence of the blaKPC-2 carbapenemase gene for all samples. The two phenotype assays showed 100% concordant results for the non-carbapenems, but only 65% concordance for the two carbapenems. The presence/absence of AMR genes was 100% concordant with AMR phenotypes for all four non-carbapenem drugs, although only 22%—50% sensitivity for the carbapenems. Conclusions: Overall, these findings suggest that the lack of complete correspondence between CRE AMR genotype and phenotype for carbapenems, while concerning, is independent of sequencing platform/assembly method.

Viral infections are known to modulate the upper respiratory tract microbiome, but few studies have addressed differences in the nasopharyngeal microbiome following SARS-CoV-2 infection. Using nasopharyngeal swab medical waste samples from 79 confirmed SARS-CoV-2 positive and 20 SARS-CoV-2 negative patients, we assessed microbiome composition with metagenomic sequencing. COVID-19 status and breathing assistive device use was associated with differences in beta diversity, principal component analyses, community composition and abundance of several species. Serratia more frequently appeared in COVID-19 patient samples compared to negative patient samples, and Serratia, Streptococcus, Enterobacter, Veillonella, Prevotella, and Rothia appeared more frequently in samples of those who used breathing assistive devices. Smoking and age were associated with differences in alpha diversity. Cross-sectional differences in the microbiome were apparent with SARS-CoV-2 infection, but longitudinal studies are needed to understand the dynamics of viral and breathing treatment modulation of microbes.

Despite the potential relevance to clinical outcome, intra-host dynamics of SARS-CoV-2 are unclear. Here, we quantify and characterize intra-host variation in SARS-CoV-2 raw sequence data uploaded to SRA as of 14 April 2020, and compare results between two sequencing methods (amplicon and RNA-Seq). Raw fastq files were quality filtered and trimmed using Trimmomatic, mapped to the WuhanHu1 reference genome using Bowtie2, and variants called with bcftools mpileup . To ensure sufficient coverage, we only included samples with 10X coverage for >90% of the genome (n=406 samples), and only variants with a depth >=10. Derived (i.e. non-reference) alleles were found at 408 sites. The number of polymorphic sites (i.e. sites with multiple alleles) within samples ranged from 0-13, with 72% of samples (295/406) having at least one polymorphic site. Correlation between number of polymorphic sites and coverage was very low for both sequencing methods (R ² < 0.1, p < 0.05). Polymorphisms were observed >1 sample at 66 sites (range: 2-38 samples). The minor allele frequency (MAF) at each shared polymorphic site was 0.03% - 48.5%. 33/66 sites occurred in ORF1a1b, and 37/66 changes were non-synonymous. At 10/66 sites, derived alleles were found in samples sequenced using both methods. Polymorphic amplicon samples were found at 10/10 positions, while polymorphic RNA-Seq samples were found at 7/10 positions. In conclusion, our results suggest that intra-host variation is prevalent among clinical samples. While mutations resulting from amplification and/or sequencing errors cannot be excluded, the observation of shared polymorphic sites with high MAF across multiple samples and sequencing methods is consistent with true underlying variation. Further investigation into intra-host evolutionary dynamics, particularly with longitudinal sampling, is critical for broader understanding of disease progression.