Abstract
Plasmids, integral to bacterial evolution, pose challenges in their genome classification due to incomplete assembly data. While next-generation sequencing has improved plasmid classification, challenges persist in accurately assembling complete plasmid genomes. This study presents a novel plasmid classification methodology based on complete genome similarity, utilizing three metrics: nucleotide composition, gene occurrence, and structural dissimilarity. Tested on a local Klebsiella pneumoniae population, the method outperforms pMLST and PlasmidFinder, distinguishing plasmids even in fusion cases. Applied across diverse bacterial populations, this reference-free approach proves adaptable, offering a valuable tool for monitoring plasmid mobility and diversity. Third-generation sequencing advancements provide a comprehensive understanding of plasmid dynamics, which is essential for addressing antibiotic resistance and bacterial pathogenicity.
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Acknowledgments.
This work was supported by a grant project from the Czech Science Foundation [GA23-05845S].
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Vitkova, H., Nykrynova, M., Bezdicek, M., Lengerova, M. (2024). Unveiling Diversity: Classification of Klebsiella Pneumoniae Plasmids from Long-read Assemblies. In: Rojas, I., Ortuño, F., Rojas, F., Herrera, L.J., Valenzuela, O. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2024. Lecture Notes in Computer Science(), vol 14849. Springer, Cham. https://doi.org/10.1007/978-3-031-64636-2_24
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