PanGene-O-Meter: Intra-Species Diversity Based on Gene-Content

Abstract

BackgroundBacterial genome evolution is shaped extensively by horizontal gene transfer, generating gene presence-absence variation that represents a major axis of functional diversity yet is largely invisible to core-genome-based similarity metrics. While genome-wide average nucleotide identity (ANI) remains the most widely used measure of similarity, it considers only the alignable fraction of compared genomes and therefore fails to capture gene-content differences that can have profound phenotypic consequences even among nearly identical strains. ResultsHere we introduce PanGene-O-Meter, a computational framework that quantifies gene-content similarity (GCS) between bacterial genomes by leveraging pan-genome structure and orthology group relationships. Applying PanGene-O-Meter to four large datasets spanning Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Helicobacter pylori, we show that GCS captures substantial functional diversity hidden within high-ANI genome pairs, and that gene-content-based trees resolve substructure within sequence types that core-genome analyses cannot. We further introduce GeneContRep, an efficient greedy algorithm for gene-content-based genome dereplication, and validate its utility on 1,000 Klebsiella pneumoniae genomes from two major multidrug-resistant lineages (ST258 and ST307) with matched experimental antimicrobial resistance phenotypes. GeneContRep preserves substantially more clinically relevant diversity than standard ANI-based dereplication, recovering carbapenemase variants, capsule locus types, and resistance determinants that are systematically lost under conventional ANI thresholds. ConclusionsPanGene-O-Meter provides an efficient, complementary, and biologically interpretable framework for quantifying intra-species bacterial diversity, with broad applications in comparative genomics, epidemiological surveillance, and the principled selection of representative genomes for functional studies. PanGene-O-Meter is available at: https://github.com/HaimAshk/PanGene-O-Meter.