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Objectives. This study validates a high-throughput workflow using Oxford Nanopore Technology (ONT) to generate complete, closed circular microbial chromosomes and plasmids. We demonstrate its superior resolution for genomic epidemiology over traditional, fragmentedread methods like Multi-Locus Sequence Typing (MLST), Single Nucleotide Polymorphism (SNP) analysis, and core/whole-genome MLST (cg/wgMLST). Materials and methods. We performed hybrid ONT long-read and Illumina short-read sequencing on 50 clinical isolates and a public reference set (n = 90). Genomes were assembled and circularized using an autocycler and polished with Medaka. We assessed genome relatedness and typing resolution using sourmash, pling, and the novel k-mer tool lexicmap, benchmarking them against traditional in-silico MLST, SNP, and cg/wgMLST analyses. Results and discussion. The hybrid workflow consistently generated complete circular chromosomes, plasmids, and phages. lexicmap demonstrated significantly higher discriminatory power than traditional typing, resolving transmission clusters missed by cgMLST. Complete genome structures revealed the precise location of resistance genes and mobile elements, uncovering complex structural variations (e.g., inversions, transpositions, duplications) invisible to SNP-based methods. This unambiguous view of genome architecture is critical for understanding pathogen evolution, outbreak dynamics, and genetic mobilization. Conclusions. Integrating ONT sequencing with novel bioinformatic tools represents a paradigm shift, moving beyond fragmented SNP or gene-based comparisons to a holistic, structureaware understanding of the genome. Generating complete closed circular chromosomes is now a feasible, high-throughput reality, offering unprecedented resolution for clinical diagnostics, genomic surveillance, and evolutionary studies. Funding source: This work was carried out through the "Nucleu" Program within the National Research, Development and Innovation Plan 2022-2027, with the support of the Ministry of Research, Innovation and Digitalization (MCID), project no. 23 44.