RESUMO
Whole-genome sequencing (WGS) represents the main technology for SARS-CoV-2 lineage characterization in diagnostic laboratories worldwide. The rapid, near-full-length sequencing of the viral genome is commonly enabled by high-throughput sequencing of PCR amplicons derived from cDNA molecules. Here, we present a new approach called NASCarD (Nanopore Adaptive Sampling with Carrier DNA), which allows a low amount of nucleic acids to be sequenced while selectively enriching for sequences of interest, hence limiting the production of non-target sequences. Using COVID-19 positive samples available during the omicron wave, we demonstrate how the method may lead to >99% genome completeness of the SARS-CoV-2 genome sequences within 7 h of sequencing at a competitive cost. The new approach may have applications beyond SARS-CoV-2 sequencing for other DNA or RNA pathogens in clinical samples.
RESUMO
Enteroviruses are small RNA viruses that affect millions of people each year by causing an important burden of disease with a broad spectrum of symptoms. In routine diagnostic laboratories, enteroviruses are identified by PCR-based methods, often combined with partial sequencing for genotyping. In this proof-of-principle study, we assessed direct RNA sequencing (DRS) using nanopore sequencing technology for fast whole-genome sequencing of viruses directly from clinical samples. The approach was complemented by sequencing the corresponding viral cDNA via Illumina MiSeq sequencing. DRS of total RNA extracted from three different enterovirus-positive stool samples produced long RNA fragments, covering between 59% and 99.6% of the most similar reference genome sequences. The identification of the enterovirus sequences in the samples was confirmed by short-read cDNA sequencing. Sequence identity between DRS and Illumina MiSeq enterovirus consensus sequences ranged between 94% and 97%. Here, we show that nanopore DRS can be used to correctly identify enterovirus genotypes from patient stool samples with high viral load and that the approach also provides rich metatranscriptomic information on sample composition for all life domains.
