Augmenting Bacterial Similarity Measures Using a Graph-Based Genome Representation.

Area of Science:

• Genomics and Bioinformatics
• Microbial Ecology
• Computational Biology

Background:

• Bacterial taxa relationships are traditionally assessed using 16S rRNA nucleotide similarity or average nucleotide identity (ANI).
• Advancements in sequencing technology enable detailed pairwise genome comparisons, offering potential for refined relationship assessments.
• Synteny, the mapping of orthologous gene locations, is underexplored for systematic bacterial genome relationship analysis.

Approach:

• Developed and validated a novel synteny similarity measure for bacterial genomes using a dataset of 378 genomes.
• Scaled synteny similarity onto 16S rRNA distance via covariance matrices.
• Applied complete linkage hierarchical clustering and K-nearest neighbors (KNN) graph structures to synteny-scaled data, analyzing core, antibiotic resistance, and virulence genes.

Key Points:

• The new synteny-based metric demonstrated improved clustering quality compared to state-of-the-art ANI metrics.
• Synteny analysis preserved clustering assignments for highly similar bacterial relationships.
• Functional contexts (core, antibiotic resistance, virulence genes) influenced topological arrangements in bacterial relationship networks.

Conclusions:

• Syntenic relationships offer more granular and interpretable insights into within-genera bacterial taxa compared to pairwise similarity measures.
• This approach enhances understanding of genomic relationships, particularly when considering specific functional gene sets.
• Synteny analysis represents a promising advancement for bacterial systematics and comparative genomics.