Résumé
Reference-free approaches are increasingly used to study genetic diversity without the need for genome assembly. K-mer analysis, common in clinical genomics and effective in plant studies, shows stronger phenotype associations than traditional SNP methods (Voichek & Weigel, 2020). iKISS (Inference of Significant K-mers Under Selection), a Python tool available under the GPLv3 license, integrates k-mer analysis with population genomics to explore species diversity, ancestry, and genotype-environment associations.iKISS facilitates read decomposition into k-mers and generates binary presence/absence tables. It leverages this k-mer information to identify genetic markers associated with environmental adaptation, enabling the study of polymorphisms linked to environmental gradients or phenotypic traits. Additionally, iKISS can infer population structure directly from k-mer data, providing deeper insights into genetic diversity.