Mge-cluster: a reference-free approach for typing bacterial plasmids

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.

Automated summary

Extrachromosomal elements of bacterial cells, such as plasmids, play a crucial role in evolution and adaptation. However, the classification of plasmids is still limited, which motivated the development of an efficient approach called mge-cluster that can recognize novel plasmid types and classify them into previously identified groups. This approach offers faster runtime, moderate memory usage, and an intuitive visualization, classification, and clustering scheme within a single framework. By analyzing a population-wide plasmid data set from Escherichia coli, the study highlights the prevalence of the colistin resistance gene and describes plasmid transmission in a hospital environment.