ShinyVar: a web-based application for comparative Influenza variant analysis supporting structure-guided approaches to vaccine and antiviral drug design.

Background: Influenza viruses remain a significant global health threat due to their high mutation rates and extensive subtype diversity. Genetic variability promotes the mixing of viral subpopulations and facilitates the emergence of novel, potentially virulent, strains. Seasonal influenza vaccines, typically targeting the A/H1N1pdm09 subtype, A/H3N2 subtype, B/Victoria lineage, and B/Yamagata lineage must be reformulated annually, yet their effectiveness and antiviral resistance are continually challenged by antigenic drift. Notably, the H275Y mutation confers a marked reduction in sensitivity to oseltamivir. Comparative variant analysis provides an effective approach for understanding these genetic variations, which is essential for enhancing vaccine effectiveness and predicting drug resistance. Existing tools enable pairwise comparison of variant call format (VCF) files through command-line interfaces but a major limitation is that they only merge variants, and users must manually group variants across multiple samples. Methods: Here, we present ShinyVar, a web-based application developed using the R Shiny framework for comparative variant analysis. The application automatically identifies and visualizes mutations in influenza viruses (IFVs) through interactive comparison of variant profiles across subpopulations. The analysis included the A/Wisconsin/67/2022 (H1N1)pdm09-like virus, A/District of Columbia/27/2023 (H3N2)-like virus, B/Austria/1359417/2021 (B/Victoria lineage)-like virus, and B/Phuket/3073/2013 (B/Yamagata lineage)-like virus as representative subtypes for comparison across the past four years. Results: Variant analysis revealed 181 unique variants in B/Victoria lineage 2025, 101 unique variants in A/H1N1pdm09 2024, 37 unique variants in A/H3N2 subtype 2024, and seven unique variants in B/Yamagata lineage 2025. Structural analysis focused on HA_A/H1N pdm091, NA_A/H1N1 pdm09, and NA_B/Victoria. In HA_A/H1N1pdm09, eight non-synonymous mutations were identified. For downstream structural analysis, p.Thr137Ala or p.Thr120Ala mature numbering led to the loss of a hydrogen bond within the 5J8 antibody binding site, resulting in a slight decrease in binding affinity. In the NA_B/Victoria lineage, eleven non-synonymous mutations marginally disrupted key oseltamivir interaction residues (E276 and R292), resulting in lower binding affinity compared with NA_A/H1N1pdm09. Conclusions: ShinyVar is a fast and efficient tool for variant detection and visualization, making it suitable for monitoring not only IFV evolution but also the evolution of other pathogens. ShinyVar facilitates the identification of novel, shared, and unique variants linked to important traits, including drug resistance and virulence under selective pressure, contributing to improved insights into pathogen evolution and the rational design of vaccines and antiviral drugs.