rhinotypeR enables reproducible rhinovirus genotype assignment from VP4/2 sequences

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⁻<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, 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.

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Publication Details

Journal
Scientific Reports
Published
2026-02-11
DOI
https://doi.org/10.1038/s41598-026-37050-8
Primary Topic
Respiratory viral infections research
Type
article
Field-Weighted Citation Impact
0.00

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article

rhinotypeR enables reproducible rhinovirus genotype assignment from VP4/2 sequences

Ruth Nanjala, Martha M. Luka, Olaitan Igbagbo Awe, Winfred Gatua et al.
Scientific Reports
Respiratory viral infections research
article

rhinotypeR enables reproducible rhinovirus genotype assignment from VP4/2 sequences

Ruth Nanjala, Martha M. Luka, Olaitan Igbagbo Awe, Winfred Gatua, Wafaa M. Rashed
article en

Abstract

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⁻<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, 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.

Scientific ReportsVol. 16(1)
At Bristol (GB), Nuffield Orthopaedic Centre (GB), University of Bristol (GB), South African Thoracic Society (ZA), Genomas (United States) (US), MRC Epidemiology Unit (GB), Ahram Canadian University (EG), University of Glasgow (GB)
National Institutes of Health
Openalex Percentile: Top 58%
Respiratory viral infections research
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