KmerSignificance Score：面向病毒 k-mer 优先排序的判别性与生物学信息框架
KmerSignificance score: A discriminative and biologically informed framework for viral <i>k</i>-mer prioritization
研究团队提出 KmerSignificance Score（KSS），一个整合信息论判别力、优化氨基酸替换矩阵 MIYATA_EVO 与 UniProt 注释蛋白特征评分的病毒 k-mer 优先排序框架，三个分量与综合得分均被约束在 [0,1] 区间。判别分量平均 F1 为 0.881，与八项基线中最强的卡方检验（0.876）无显著差异，但 top 排序在 SARS-CoV-2 与 HIV-1 上与之不同；MIYATA_EVO 经遗传算法优化后，在八项氨基酸特征上的复合一致度较原 MIYATA 矩阵提升 28.4%。
来源：PLOS Computational Biology · journals.plos.org
by Dylan Lebatteux, Florian Corso, Hugo Soudeyns, Isabelle Boucoiran, Soren Gantt, Abdoulaye Baniré Diallo
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 τ=0.775), as expected for measures drawing on overlapping annotation content. Within each level, the continuous score provided finer numerical resolution among proteins. Literature cross-referencing on SARS-CoV-2 (278,738 sequences, 19 Pango-derived lineage labels), HIV-1 (12,223 sequences, 15 subtype labels), and human cytomegalovirus (HCMV; 399–646 sequences, 4–8 locus-specific genotypes) found that the top-ranked k-mers reported here map to established variant-defining mutations, subtype-specific polymorphisms, and genotype markers. KSS provides a standardized framework for viral k-mer prioritization with potential applications in variant surveillance, molecular epidemiology, and functional annotation. The tool is available at https://github.com/bioinfoUQAM/KmerSignificanceScore.