ReGAIN: A bioinformatics platform for assessing probabilistic co-occurrence between resistance genes in bacterial pathogens

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ID: 320361
2026
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Abstract
MOTIVATION: Multidrug-resistant bacterial pathogens continue to rise globally, yet scalable methods are needed to infer how resistance determinants co-occur across pathogen populations and to quantify conditional dependencies underlying co-occurrence and shared genetic context. RESULTS: We present ReGAIN (Resistance Gene Association and Inference Network), an open-source framework that applies Bayesian network structure learning to infer probabilistic, conditional dependency relationships among antibiotic resistance, heavy metal tolerance, stress response, and virulence determinants in bacteria. In contrast to pairwise co-occurrence analyses, ReGAIN reports conditional probabilities, relative risks, and absolute risk differences with confidence intervals to prioritize candidate relationships for downstream prioritization. Applied across ESKAPEE pathogens, ReGAIN recapitulated established resistance gene relationships and identified additional candidate patterns consistent with co-selection and shared genetic context. Together, these results support scalable, reproducible population-wide analysis of resistance networks for surveillance, comparative genomics and epidemiology. AVAILABILITY: ReGAIN analyses are performed using Python v3.11.5 and R v4.4.1 and is available as open-source software through Bioconda at {{https://anaconda.org/bioconda/regain-cli}}. Source code and documentation can be found at {{https://github.com/ERBringHorvath/regain_CLI}}. All genomes used in this publication were downloaded from the National Center for Biotechnology Information database. Large supplementary tables and results data from the ESKAPEE pathogen example network analyses can be downloaded from https://figshare.com/articles/dataset/ReGAIN_command_line_software_and_supplemental_figures_/28959431. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7167833615 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Elijah R Bring Horvath, Mathew G. Stein, Matthew A. Mulvey, Edgar J. Hernández, Jaclyn M. Winter
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag505
URL
https://doi.org/10.1093/bioinformatics/btag505
Keywords Keywords not found

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