KmerSignificance score: A discriminative and biologically informed framework for viral k-mer prioritization

Distinguishing closely related viral strains requires identifying genomic regions where subtle sequence differences carry biological significance. While k -mer-based approaches offer computational efficiency for genome analysis, existing methods lack standardized frameworks for evaluating which k -mers are most informative. Current selection strategies focus primarily on statistical discriminative power without integrating biological context. We introduce KmerSignificance Score (KSS), a k -mer prioritization framework combining three components: an information-theoretic method measuring strain-distinguishing capacity, an optimized amino acid substitution matrix (MIYATA_EVO) for mutation impact assessment, and a protein characterization score derived from annotations and database cross-references retrieved through UniProt. All three component scores and the integrated KSS are bounded in [0,1] by construction, providing a common numerical scale for ranking positions within a dataset. The discriminative component achieved a mean F1 of 0.881, not significantly different from chi-squared (0.876), the strongest of eight baselines. Top-ranked selections from the discriminative component nonetheless differed from those produced by chi-squared, especially for SARS-CoV-2 and HIV-1. The MIYATA_EVO matrix, optimized using a genetic algorithm, increased the composite agreement score across eight amino acid characteristics by 28.4% over the original MIYATA matrix. Across 17,471 viral proteins, the protein characterization score tracked UniProt’s five-level annotation score closely (Kendall’s