Illumina and nanopore sequencing technologies are powerful tools that can be used to determine the bacterial composition of complex microbial communities. In this study, we compared nasal microbiota results at genus level using both Illumina and nanopore 16S rRNA gene sequencing. We also monitored the progression of nanopore sequencing in the accurate identification of species, using pure, single species cultures, and evaluated the performance of the nanopore EPI2ME 16S data analysis pipeline. Fifty-nine nasal swabs were sequenced using Illumina MiSeq and Oxford Nanopore 16S rRNA gene sequencing technologies. In addition, five pure cultures of relevant bacterial species were sequenced with the nanopore sequencing technology. The Illumina MiSeq sequence data were processed using bioinformatics modules present in the Mothur software package. Albacore and Guppy base calling, a workflow in nanopore EPI2ME (Oxford Nanopore Technologies—ONT, Oxford, UK) and an in-house developed bioinformatics script were used to analyze the nanopore data. At genus level, similar bacterial diversity profiles were found, and five main and established genera were identified by both platforms. However, probably due to mismatching of the nanopore sequence primers, the nanopore sequencing platform identified Corynebacterium in much lower abundance compared to Illumina sequencing. Further, when using default settings in the EPI2ME workflow, almost all sequence reads that seem to belong to the bacterial genus Dolosigranulum and a considerable part to the genus Haemophilus were only identified at family level. Nanopore sequencing of single species cultures demonstrated at least 88% accurate identification of the species at genus and species level for 4/5 strains tested, including improvements in accurate sequence read identification when the basecaller Guppy and Albacore, and when flowcell versions R9.4 (Oxford Nanopore Technologies—ONT, Oxford, UK) and R9.2 (Oxford Nanopore Technologies—ONT, Oxford, UK) were compared. In conclusion, the current study shows that the nanopore sequencing platform is comparable with the Illumina platform in detection bacterial genera of the nasal microbiota, but the nanopore platform does have problems in detecting bacteria within the genus Corynebacterium. Although advances are being made, thorough validation of the nanopore platform is still recommendable.

Multilocus sequence typing (MLST) is a widely used system for typing microorganisms by sequence analysis of housekeeping genes. The main advantage of MLST in comparison to other typing techniques is the unambiguity and transferability of sequence data. However, a main disadvantage is the high cost of DNA sequencing. Here we introduce a high-throughput MLST (HiMLST) method that employs next-generation sequencing (NGS) technology (Roche 454), to generate large quantities of high-quality MLST data at low costs. The HiMLST protocol consists of two steps. In the first step MLST target genes are amplified by PCR in multi-well plates. During this PCR the amplicons of each bacterial isolate are provided with a unique DNA barcode, the multiplex identifier (MID). In the second step all amplicons are pooled and sequenced in a single NGS-run. The MLST profile of each individual isolate can be retrieved easily using its unique MID. With HiMLST we have profiled 575 isolates of Legionella pneumophila, Staphylococcus aureus, Pseudomonas aeruginosa and Streptococcus pneumoniae in mixed species HiMLST experiments. In conclusion, the introduction of HiMLST paves the way for a broad employment of the MLST as a high-quality and cost-effective method for typing microbial species.

Viral enrichment by probe hybridization has been reported to significantly increase the sensitivity of viral metagenomics. This study compares the analytical performance of two targeted metagenomic virus capture probe-based methods: (i) SeqCap EZ HyperCap by Roche (ViroCap) and (ii) Twist Comprehensive Viral Research Panel workflow, for diagnostic use. Sensitivity, specificity, and limit of detection were analyzed using 25 synthetic viral sequences spiked in increasing proportions of human background DNA, eight clinical samples, and American Type Culture Collection (ATCC) Virome Virus Mix. Sensitivity and specificity were 95% and higher for both methods using the synthetic and reference controls as gold standard. Combining thresholds for viral sequence read counts and genome coverage [respectively 500 reads per million (RPM) and 10% coverage] resulted in optimal prediction of true positive results. Limits of detection were approximately 50–500 copies/mL for both methods as determined by ddPCR. Increasing proportions of spike-in cell-free human background sequences up to 99.999% (50 ng/mL) did not negatively affect viral detection, suggesting effective capture of viral sequences. These data show analytical performances in ranges applicable to clinical samples, for both probe hybridization metagenomic approaches. This study supports further steps toward more widespread use of viral metagenomics for pathogen detection, in clinical and surveillance settings using low biomass samples. IMPORTANCE: Viral metagenomics has been gradually applied for broad-spectrum pathogen detection of infectious diseases, surveillance of emerging diseases, and pathogen discovery. Viral enrichment by probe hybridization methods has been reported to significantly increase the sensitivity of viral metagenomics. During the past years, a specific hybridization panel distributed by Roche has been adopted in a broad range of different clinical and zoonotic settings. Recently, Twist Bioscience has released a new hybridization panel targeting human and animal viruses. This is the first report comparing the performance of viral metagenomic hybridization panels.

Rapid identification of the rise and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern remains critical for monitoring of the efficacy of diagnostics, therapeutics, vaccines, and control strategies. A wide range of SARS-CoV-2 next-generation sequencing (NGS) methods have been developed over the last years, but cross-sequence technology benchmarking studies have been scarce. In the current study, 26 clinical samples were sequenced using five protocols: AmpliSeq SARS-CoV-2 (Illumina), EasySeq RC-PCR SARS-CoV-2 (Illumina/NimaGen), Ion AmpliSeq SARS-CoV-2 (Thermo Fisher), custom primer sets (Oxford Nanopore Technologies (ONT)), and capture probe-based viral metagenomics (Roche/Illumina). Studied parameters included genome coverage, depth of coverage, amplicon distribution, and variant calling. The median SARS-CoV-2 genome coverage of samples with cycle threshold (Ct) values of 30 and lower ranged from 81.6 to 99.8% for, respectively, the ONT protocol and Illumina AmpliSeq protocol. Correlation of coverage with PCR Ct values varied per protocol. Amplicon distribution signatures differed across the methods, with peak differences of up to 4 log₁₀ at disbalanced positions in samples with high viral loads (Ct values ≤ 23). Phylogenetic analyses of consensus sequences showed clustering independent of the workflow used. The proportion of SARS-CoV-2 reads in relation to background sequences, as a (cost-)efficiency metric, was the highest for the EasySeq protocol. The hands-on time was the lowest when using EasySeq and ONT protocols, with the latter additionally having the shortest sequence runtime. In conclusion, the studied protocols differed on a variety of the studied metrics. This study provides data that assist laboratories when selecting protocols for their specific setting.

16S rRNA gene profiling has revolutionized the field of microbial ecology. Many researchers in various fields have embraced this technology to investigate bacterial compositions of samples derived from many different ecosystems. However, it is important to acknowledge the current limitations and drawbacks of 16S rRNA gene profiling. Although sample handling, DNA extraction methods and the choice of universal 16S rRNA gene PCR primers are well known factors that could seriously affect the final results of microbiota profiling studies, inevitable amplification artifacts, such as chimera formation and PCR competition, are seldom appreciated. Here we report on a novel micelle based amplification strategy, which overcomes these limitations via the clonal amplification of targeted DNA molecules. Our results show that micelle PCR drastically reduces chimera formation by a factor of 38 (1.5% vs. 56.9%) compared with traditional PCR, resulting in improved microbial diversity estimates. In addition, compartmentalization during micelle PCR prevents PCR competition due to unequal amplification rates of different 16S template molecules, generating robust and accurate 16S microbiota profiles required for comparative studies (e.g. longitudinal surveys).