Zoonomix: A Pipeline for Assessing Zoonotic Potential and Antibiotic Resistance in Bacterial Genomes

Introduction: The increasing emergence of zoonotic pathogens and antimicrobial resistance (AMR) highlights the need for rapid and accurate computational tools to assess the zoonotic potential of bacterial strains. In this study, we present Zoonomix, a bioinformatics pipeline designed to detect and rank genes associated with pathogenicity, virulence, and antibiotic resistance, thereby enabling risk assessment for zoonotic transmission. Methods: Zoonomix integrates a curated database of ~25,000 genes related to adherence, biofilm formation, efflux pumps, exotoxins, resistance, integrative and conjugative elements (ICEs), and secretion systems (T3SS, T4SS, and T6SS). It uses BLASTN and a scoring algorithm to assess pathogenicity and HGT risk, classifying bacterial strains into low, moderate, or high risk, with insights into antibiotic resistance migration. Results: When analyzing 60 whole genome sequences of both zoonotic and non-zoonotic bacterial species using the Zoonomix pipeline, over 90% of the results were accurately classified in accordance with existing literature. Notably, the pipeline predicted a potential future zoonotic and pathogenic capability for bacterial species such as A. pleuropneumoniae and M. haemolytica. Discussion: Zoonomix offers a comprehensive framework for assessing zoonotic potential and antibiotic resistance by integrating genomics, bioinformatics, and predictive analytics. Its ability to detect current gene status, identify mutation-prone genes, summarize mutation hotspots, and flag horizontal gene transfer events make it a valuable tool for disease surveillance and outbreak prevention. conclusion: Zoonomix is a scalable, open-source tool for assessing zoonotic potential and AMR risk in bacterial genomes. By detecting key genes, predicting future mutations, and flagging ICE-mediated resistance transfer, it offers valuable insights for genomic epidemiology and public health surveillance. The open-source pipeline is available at https://github.com/Umeshkumarku1/ZoonomiX. Conclusion: Zoonomix is a scalable, open-source bioinformatics pipeline designed to assess the zoonotic potential and antimicrobial resistance (AMR) risk in bacterial whole genome sequences. By detecting key genes associated with zoonosis, identifying markers that predict the future pathogenic or zoonotic potential of bacteria, and flagging integrative conjugative element (ICEs)- mediated resistance gene transfer mechanisms, the tool provides comprehensive insights into bacterial threats. The pipeline's source code and documentation are freely available for the research community at the following GitHub repository: https://github.com/Umeshkumarku1/ZoonomiX.

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Publication Details

Journal
Current Biotechnology
Published
2025-11-22
DOI
https://doi.org/10.2174/0122115501397209251118052633
Primary Topic
Zoonotic diseases and public health
Type
article
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article

Zoonomix: A Pipeline for Assessing Zoonotic Potential and Antibiotic Resistance in Bacterial Genomes

R. K. Karwasra, Umeshkumar KU
Current Biotechnology
Zoonotic diseases and public health
article

Zoonomix: A Pipeline for Assessing Zoonotic Potential and Antibiotic Resistance in Bacterial Genomes

R. K. Karwasra, Umeshkumar KU
article en

Abstract

Introduction: The increasing emergence of zoonotic pathogens and antimicrobial resistance (AMR) highlights the need for rapid and accurate computational tools to assess the zoonotic potential of bacterial strains. In this study, we present Zoonomix, a bioinformatics pipeline designed to detect and rank genes associated with pathogenicity, virulence, and antibiotic resistance, thereby enabling risk assessment for zoonotic transmission. Methods: Zoonomix integrates a curated database of ~25,000 genes related to adherence, biofilm formation, efflux pumps, exotoxins, resistance, integrative and conjugative elements (ICEs), and secretion systems (T3SS, T4SS, and T6SS). It uses BLASTN and a scoring algorithm to assess pathogenicity and HGT risk, classifying bacterial strains into low, moderate, or high risk, with insights into antibiotic resistance migration. Results: When analyzing 60 whole genome sequences of both zoonotic and non-zoonotic bacterial species using the Zoonomix pipeline, over 90% of the results were accurately classified in accordance with existing literature. Notably, the pipeline predicted a potential future zoonotic and pathogenic capability for bacterial species such as A. pleuropneumoniae and M. haemolytica. Discussion: Zoonomix offers a comprehensive framework for assessing zoonotic potential and antibiotic resistance by integrating genomics, bioinformatics, and predictive analytics. Its ability to detect current gene status, identify mutation-prone genes, summarize mutation hotspots, and flag horizontal gene transfer events make it a valuable tool for disease surveillance and outbreak prevention. conclusion: Zoonomix is a scalable, open-source tool for assessing zoonotic potential and AMR risk in bacterial genomes. By detecting key genes, predicting future mutations, and flagging ICE-mediated resistance transfer, it offers valuable insights for genomic epidemiology and public health surveillance. The open-source pipeline is available at https://github.com/Umeshkumarku1/ZoonomiX. Conclusion: Zoonomix is a scalable, open-source bioinformatics pipeline designed to assess the zoonotic potential and antimicrobial resistance (AMR) risk in bacterial whole genome sequences. By detecting key genes associated with zoonosis, identifying markers that predict the future pathogenic or zoonotic potential of bacteria, and flagging integrative conjugative element (ICEs)- mediated resistance gene transfer mechanisms, the tool provides comprehensive insights into bacterial threats. The pipeline's source code and documentation are freely available for the research community at the following GitHub repository: https://github.com/Umeshkumarku1/ZoonomiX.

Current BiotechnologyVol. 15
NIIT University (IN), University of Rajasthan (IN)
Openalex Percentile: Top 53%
Zoonotic diseases and public health
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