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Journal of Medical Virology
Article . 2025 . Peer-reviewed
License: CC BY
Data sources: Crossref
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PubMed Central
Other literature type . 2025
License: CC BY
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Performance of Long‐Read Single‐Molecule Real‐Time Sequencing for SARS‐CoV‐2 Genotyping in Clinical Samples

Authors: Pauline Trémeaux; Justine Latour; Camille Vellas; Sofia Demmou; Noémie Ranger; Antonin Bal; Jacques Izopet;

Performance of Long‐Read Single‐Molecule Real‐Time Sequencing for SARS‐CoV‐2 Genotyping in Clinical Samples

Abstract

ABSTRACTDue to the continuous genetic evolution of SARS‐CoV‐2, numerous variants have emerged and different whole genome sequencing techniques, necessary for accurate virus typing, have been developed. In this study, we evaluated the performance of PacBio single‐molecule real‐time (SMRT) sequencing for SARS‐CoV‐2 typing. Reproducibility was assessed on two internal quality controls, whose median reading depths were 1154X and 1059X. The overall sensitivity on 1646 clinical samples collected between January 2023 and June 2024 was 83.6% and was correlated to the viral load. By comparison, the overall sensitivity of short‐read illumina sequencing over the same period of time on 271 samples was 90.8%. Although less sensitive, SMRT sequencing was more efficient for the identification of the two lineages in a co‐infection case due to the amplification of long fragments. Comparing the results obtained by the two techniques, 10 out of 50 samples were identified with the same clade but not the exact same lineage at the time of analysis, because of the very frequent updates of the Pango taxonomy. Nevertheless, we obtained very similar fasta consensus sequences with a maximum difference of 4 nucleotides, showing that both methods provide accurate typing of SARS‐CoV‐2, useful for epidemiological or clinical studies.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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