Augmenting bacterial similarity measures using a graph-based genome representation

Relationships between bacterial taxa are traditionally defined using 16S rRNA nucleotide similarity or average nucleotide identity. Improvements in sequencing technology provide additional pairwise information on genome sequences, which may provide valuable information on genomic relationships. Mapping orthologous gene locations between genome pairs, known as synteny, is typically implemented in the discovery of new species and has not been systematically applied to bacterial genomes. Using a data set of 378 bacterial genomes, we developed and tested a new measure of synteny similarity between a pair of genomes, which was scaled onto 16S rRNA distance using covariance matrices. Based on the input gene functions used (i.e., core, antibiotic resistance, and virulence), we observed varying topological arrangements of bacterial relationship networks by applying (i) complete linkage hierarchical clustering and (ii) K-nearest neighbor graph structures to synteny-scaled 16S data. Our metric improved clustering quality comparatively to state-of-the-art average nucleotide identity metrics while preserving clustering assignments for the highest similarity relationships. Our findings indicate that syntenic relationships provide more granular and interpretable relationships for within-genera taxa compared to pairwise similarity measures, particularly in functional contexts.IMPORTANCEGiven the prevalence and necessity of the 16S rRNA measure in bacterial identification and analysis, this additional analysis adds a functional and synteny-based layer to the identification of relatives and clustering of bacteria genomes. It is also of computational interest to model the bacterial genome as a graph structure, which presents new avenues of genomic analysis for bacteria and their closely related strains and species. Given the prevalence and necessity of the 16S rRNA measure in bacterial identification and analysis, this additional analysis adds a functional and synteny-based layer to the identification of relatives and clustering of bacteria genomes. It is also of computational interest to model the bacterial genome as a graph structure, which presents new avenues of genomic analysis for bacteria and their closely related strains and species.