Microbiology SpectrumGenomicsPeer-reviewed journal article
Direct genome sequencing of respiratory viruses from low viral load clinical specimens using target capture sequencing technology
Nobuhiro TakemaeiD, Yumani Kuba, Kunihiro Oba, Tsutomu Kageyama
Abstract
The use of metagenomic next-generation sequencing technology to obtain complete viral genome sequences directly from clinical samples with low viral load remains challenging--especially in the case of respiratory viruses--due to the low copy number of viral versus host genomes. To overcome this limitation, target capture sequencing for the enrichment of specific genomes has been developed and applied for direct genome sequencing of viruses. However, as the efficiency of enrichment varies depending on the probes, the type of clinical sample, etc., validation is essential before target capture sequencing can be applied to clinical diagnostics. Here we evaluated the utility of target capture sequencing with a comprehensive viral probe panel for clinical respiratory specimens collected from patients diagnosed with SARS-CoV-2 or influenza type A. We focused on clinical specimens containing low copy numbers of viral genomes. Target capture sequencing yielded approximately 180- and 2000-fold higher read counts of SARS-CoV-2 and influenza A virus, respectively, than metagenomic sequencing when the RNA extracted from specimens contained 59.3 copies/{micro}L of SARS-CoV-2 or 544 copies/{micro}L of influenza A virus, respectively. In addition, the target capture sequencing identified sequence reads in all SARS-CoV-2- or influenza type A-positive specimens with 70% of the full-length genomes of SARS-CoV-2 or influenza A virus. Furthermore, the target capture sequencing using comprehensive probes identified co-infections with viruses other than SARS-CoV-2, suggesting that this approach will not only detect a wide range of viruses, but also contribute to epidemiological studies.
- organism: human
- assay: metagenomics
- research focus: variant calling
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: experimental assay
- Citations
- 12
- FWCI
- 1.63
- Field percentile
- top 16%
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