ReGAIN Supplemental Figures

The rampant rise of multidrug-resistant (MDR) bacterial pathogens poses a serious health threat, necessitating innovative tools to unravel the complex genetic underpinnings of antimicrobial resistance. Despite strides in developing genomic tools for detecting resistance genes, a gap remains in analyzing organism-specific patterns of resistance gene co-occurrence. To address this deficiency, we developed the Resistance Gene Association and Inference Network (ReGAIN), a command-line platform that uses Bayesian network structure learning to identify and map resistance gene networks in bacterial pathogens. ReGAIN is able to detect resistance genes using well-established methods and offers insight into their complex interplay, critical for understanding MDR phenotypes. Focusing on bacterial pathogens, ReGAIN yields a queryable database for investigating resistance gene co-occurrence, enriching resistome analyses, and providing new insights into the dynamics of antimicrobial resistance. Furthermore, ReGAIN extends beyond antibiotic resistance genes to optionally include co-occurrence patterns among stress, heavy metal resistance, and virulence gene determinants, providing a robust overview of key gene relationships.