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rhinotypeR enables reproducible rhinovirus genotype assignment from VP4/2 sequences

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 (< 10⁻10). 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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41598-026-37050-8

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Institution:
University of Oxford
Role:
Author


Publisher:
Nature Research
Journal:
Scientific Reports More from this journal
Volume:
16
Issue:
1
Article number:
6149
Publication date:
2026-02-11
Acceptance date:
2026-01-19
DOI:
EISSN:
2045-2322
ISSN:
2045-2322


Language:
English
Keywords:
Source identifiers:
3754524
Deposit date:
2026-02-13
ARK identifier:
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