Extrachromosomal elements of bacterial cells such as plasmids are notorious for their importance in evolution and adaptation to changing ecology. However, high-resolution population-wide analysis of plasmids has only become accessible recently with the advent of scalable long-read sequencing technology. Current typing methods for the classification of plasmids remain limited in their scope which motivated us to develop a computationally efficient approach to simultaneously recognize novel types and classify plasmids into previously identified groups. Here, we introduce mge-cluster that can easily handle thousands of input sequences which are compressed using a unitig representation in a de Bruijn graph. Our approach offers a faster runtime than existing algorithms, with moderate memory usage, and enables an intuitive visualization, classification and clustering scheme that users can explore interactively within a single framework. Mge-cluster platform for plasmid analysis can be easily distributed and replicated, enabling a consistent labelling of plasmids across past, present, and future sequence collections. We underscore the advantages of our approach by analysing a population-wide plasmid data set obtained from the opportunistic pathogen Escherichia coli, studying the prevalence of the colistin resistance gene mcr-1.1 within the plasmid population, and describing an instance of resistance plasmid transmission within a hospital environment.
© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.