Biology subjects

Soudeyns, H.

Publications and source records attributed to Soudeyns, H..

3 recordsLinked to original sources

bioRxiv2026

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 relevance. 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 protein-level functional importance scoring derived from UniProt annotations. KSS produces standardized scores in the [0, 1] interval, enabling direct cross-dataset comparison. The discriminative component achieved classification performance comparable or superior to all tested alternatives (mean F1 = 0.880 vs. 0.718-0.877 for six established methods) while additionally providing bounded scores with consistent empirical distributions for cross-dataset comparability. MIYATA EVO, optimized via genetic algorithm, improved biophysical property correlations by 28.4% over the original MIYATA matrix. Protein scoring on 17,470 viral proteins showed robust agreement with UniProt annotation scores (Kendall{tau} = 0.777) while revealing finer functional distinctions. Literature validation on SARS-CoV-2 (278,738 sequences, 19 variants), HIV-1 (12,223 sequences, 15 subtypes), and human cytomegalovirus (HCMV; 399-646 sequences, 4-8 genotypes) confirmed that high-scoring k-mers consistently map to established variant-defining mutations, subtype-specific polymorphisms, and genotype markers. KSS provides a standardized framework for viral k-mer prioritization with applications in variant surveillance, molecular epidemiology, and functional annotation. The tool is available at https://github.com/bioinfoUQAM/KmerSignificanceScore. Author summaryIdentifying genetic differences between closely related viral strains is essential for pandemic preparedness, vaccine development, and understanding disease outbreaks. With millions of viral genomes now sequenced, researchers need tools that can rapidly pinpoint which genomic differences matter most biologically, not just which are statistically distinctive. Current k-mer-based approaches identify patterns that distinguish viral strains but cannot assess whether those differences affect protein function or disease phenotype. We developed KmerSignificance Score (KSS), a framework that we designed to rank short genomic sequences by combining three types of evidence: how well they distinguish viral strains, how much the encoded amino acid changes affect protein properties, and how functionally important the affected protein is. We standardized the resulting scores on a 0-to-1 scale, allowing direct comparison across different viruses and studies. We validated our framework on three major human pathogens (SARS-CoV-2, HIV-1, and human cytomegalovirus) and found that top-scoring positions consistently correspond to sites with documented roles in immune evasion, drug resistance, viral fitness, and strain classification. Our framework can help prioritize genomic features for surveillance of emerging variants, guide experimental validation, and support molecular epidemiology.

bioinformatics↗

bioRxiv2022

Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures

Machine learning was shown to be effective at identifying distinctive genomic signatures among viral sequences. These signatures are defined as pervasive motifs in the viral genome that allow discrimination between species or variants. In the context of SARS-CoV-2, the identification of these signatures can assist in taxonomic and phylogenetic studies, improve in the recognition and definition of emerging variants, and aid in the characterization of functional properties of polymorphic gene products. In this paper, we assess KEVOLVE, an approach based on a genetic algorithm with a machine-learning kernel, to identify multiple genomic signatures based on minimal sets of k-mers. In a comparative study, in which we analyzed large SARS-CoV-2 genome dataset, KEVOLVE was more effective at identifying variant-discriminative signatures than several gold-standard statistical tools. Subsequently, these signatures were characterized using a new extension of KEVOLVE (KANALYZER) to highlight variations of the discriminative signatures among different classes of variants, their genomic location, and the mutations involved. The majority of identified signatures were associated with known mutations among the different variants, in terms of functional and pathological impact based on available literature. Here we showed that KEVOLVE is a robust machine learning approach to identify discriminative signatures among SARS-CoV-2 variants, which are frequently also biologically relevant, while bypassing multiple sequence alignments. The source code of the method and additional resources are available at: https://github.com/bioinfoUQAM/KEVOLVE.

bioinformatics↗

bioRxiv2021

The mutational landscape of SARS-CoV-2 variants diversifies T cell targets in an HLA supertype-dependent manner

The rapid, global dispersion of SARS-CoV-2 since its initial identification in December 2019 has led to the emergence of a diverse range of variants. The initial concerns regarding the virus were quickly compounded with concerns relating to the impact of its mutated forms on viral infectivity, pathogenicity and immunogenicity. To address the latter, we seek to understand how the mutational landscape of SARS-CoV-2 has shaped HLA-restricted T cell immunity at the population level during the first year of the pandemic, before mass vaccination. We analyzed a total of 330,246 high quality SARS-CoV-2 genome assemblies sampled across 143 countries and all major continents. Strikingly, we found that specific mutational patterns in SARS-CoV-2 diversify T cell epitopes in an HLA supertype-dependent manner. In fact, we observed that proline residues are preferentially removed from the proteome of prevalent mutants, leading to a predicted global loss of SARS-CoV-2 T cell epitopes in individuals expressing HLA-B alleles of the B7 supertype family. In addition, we show that this predicted global loss of epitopes is largely driven by a dominant C-to-U mutation type at the RNA level. These results indicate that B7 supertype-associated epitopes, including the most immunodominant ones, were more likely to escape CD8+ T cell immunosurveillance during the first year of the pandemic. Together, our study lays the foundation to help understand how SARS-CoV-2 mutants shape the repertoire of T cell targets and T cell immunity across human populations. The proposed theoretical framework has implications in viral evolution, disease severity, vaccine resistance and herd immunity.

immunology↗