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SpikeID: Rapid and unbiased identification of SARS-CoV-2 variants by spike sequencing

  • Keith Farrugia
  • , Zain Khalil
  • , Adriana van de Guchte
  • , Bremy Alburquerque
  • , Daniel Floda
  • , Komal Srivastava
  • , Luz H. Patiño
  • , Juan David Ramirez
  • , Alberto E. Paniz-Mondolfi
  • , Emilia Mia Sordillo
  • , Viviana Simon
  • , Ana S. Gonzalez-Reiche
  • , Harm van Bakel

    Research output: Contribution to JournalResearch Articlepeer-review

    Abstract

    Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) are characterized by distinct mutations in the S1 domain of the viral spike protein. This domain encompasses the N-terminal domain, the receptor-binding domain, and part of the cleavage site region. While mutations in other genomic regions of SARS-CoV-2 can impact VOC potential, the S1 domain holds particular importance for identifying variants and assessing antigenic evolution and immune escape potential. Methods: We describe a rapid high-throughput sequencing-based assay, SpikeID, for the unbiased detection and identification of SARS-CoV-2 variants based on spike S1 amplicon sequencing. We benchmarked the SpikeID assay against Illumina whole-genome sequencing across 622 clinical biospecimens, representing lineages that circulated globally from October 2021 to January 2024. Results: SpikeID unambiguously detected 100 % of WHO-designated VOCs and identified PANGO lineages circulating at ≥1 % prevalence in the New York City (NYC) area with 93 % accuracy in comparison to whole-genome sequencing. This reduction in accuracy was largely due to PANGO lineages that are only distinguishable by mutations outside the S1 domain. Conclusions: We demonstrate the utility and scalability of the SpikeID assay during the emergence and subsequent surge of Omicron and Omicron-derived lineages in New York City, and show that our approach enables cost-effective, reliable, and near-real-time detection of emerging lineages.

    Original languageEnglish (US)
    Article number105845
    JournalJournal of Clinical Virology
    Volume180
    DOIs
    StatePublished - Oct 2025

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    All Science Journal Classification (ASJC) codes

    • Virology
    • Infectious Diseases

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