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<p>Rhinoviruses (RVs) are among the most prevalent human respiratory pathogens, yet their molecular characterization remains fragmented across analytical tools and inconsistent between studies. Current genotype assignment typically relies on sequence alignment, pairwise distance calculation, and prototype comparison. This fragmentation hinders reproducibility and scalability. Here, we present <i>rhinotypeR</i>, an open-source R package that provides a scriptable and transparent workflow for RV genotyping based on the VP4/2 genomic region. The package integrates multiple analytical steps; alignment, distance calculation, genotype assignment, and visualization within the Bioconductor ecosystem and applies standardized species-specific thresholds (10.5% for HRV-A/C and 9.5% for HRV-B). Using a validation dataset encompassing over 90% of known RV types, <i>rhinotypeR</i> reproduced pairwise genetic distances obtained with <i>ape</i> and MEGA X with Mantel correlation (r = 1.000, <i>p</i> = 0.001) and negligible numerical deviation (&lt; 10⁻<sup>10</sup>). Approximately 80% of sequences showed complete agreement with previous genotype assignments by multiple analysts, and most remaining discrepancies occurred near the classification thresholds. Ct value distributions were broadly similar across matched, mismatched, and unassigned sequences, indicating that discrepancies were unlikely to be driven by viral load. By consolidating fragmented analytical steps into a reproducible and automated framework, <i>rhinotypeR</i> improves consistency in rhinovirus genotyping and supports scalable, transparent molecular surveillance. The package is freely available through Bioconductor for research and routine public health applications.</p>

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rhinotypeR enables reproducible rhinovirus genotype assignment from VP4/2 sequences

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Martha M. Luka,
  • Ruth Nanjala,
  • Wafaa M. Rashed,
  • Winfred Gatua,
  • Olaitan I. Awe

摘要

Rhinoviruses (RVs) are among the most prevalent human respiratory pathogens, yet their molecular characterization remains fragmented across analytical tools and inconsistent between studies. Current genotype assignment typically relies on sequence alignment, pairwise distance calculation, and prototype comparison. This fragmentation hinders reproducibility and scalability. Here, we present rhinotypeR, an open-source R package that provides a scriptable and transparent workflow for RV genotyping based on the VP4/2 genomic region. The package integrates multiple analytical steps; alignment, distance calculation, genotype assignment, and visualization within the Bioconductor ecosystem and applies standardized species-specific thresholds (10.5% for HRV-A/C and 9.5% for HRV-B). Using a validation dataset encompassing over 90% of known RV types, rhinotypeR reproduced pairwise genetic distances obtained with ape and MEGA X with Mantel correlation (r = 1.000, p = 0.001) and negligible numerical deviation (< 10⁻10). Approximately 80% of sequences showed complete agreement with previous genotype assignments by multiple analysts, and most remaining discrepancies occurred near the classification thresholds. Ct value distributions were broadly similar across matched, mismatched, and unassigned sequences, indicating that discrepancies were unlikely to be driven by viral load. By consolidating fragmented analytical steps into a reproducible and automated framework, rhinotypeR improves consistency in rhinovirus genotyping and supports scalable, transparent molecular surveillance. The package is freely available through Bioconductor for research and routine public health applications.