Olga Novikova, Assistant Professor, SUNY Buffalo State University
Bioinformatics Training for AMR Detection in a One Health Context
Abstract
Antimicrobial resistance (AMR) represents a critical global health challenge that transcends traditional disciplinary boundaries, demanding an integrative approach to understanding, detecting, and mitigating resistance mechanisms. We have designed a professional development course to address this need by offering a comprehensive bioinformatics training program that equips healthcare professionals, researchers, and public health practitioners with advanced computational skills for AMR detection and analysis.
The course is uniquely structured within the One Health framework, emphasizing the interconnected nature of human, animal, and environmental health in AMR surveillance. Participants will gain expertise in cutting-edge bioinformatic tools and methodologies for genomic analysis, resistance gene identification, and molecular epidemiology across multiple ecological domains. The key learning objectives of our course are: (1) Master genomic and metagenomic sequencing data analysis techniques specific to AMR detection; (2) Utilize advanced bioinformatic tools for resistance gene identification and characterization; (3) Implement comprehensive computational workflows for tracking AMR transmission across human, animal, and environmental interfaces; (4) Develop skills in integrating multi-omics data for holistic resistance mechanism understanding.
The curriculum integrates theoretical instruction with hands-on computational training, utilizing real-world datasets and state-of-the-art bioinformatics platforms. Participants will engage with open-source tools such as ResFinder, ARG-ANNOT, and CARD (Comprehensive Antibiotic Resistance Database), learning to navigate complex genomic landscapes and extract meaningful resistance insights. By bridging computational skills, microbiological knowledge, and ecological perspectives, this course represents a critical intervention in professional education addressing the multifaceted challenge of antimicrobial resistance through an interdisciplinary, data-driven approach.
The course is uniquely structured within the One Health framework, emphasizing the interconnected nature of human, animal, and environmental health in AMR surveillance. Participants will gain expertise in cutting-edge bioinformatic tools and methodologies for genomic analysis, resistance gene identification, and molecular epidemiology across multiple ecological domains. The key learning objectives of our course are: (1) Master genomic and metagenomic sequencing data analysis techniques specific to AMR detection; (2) Utilize advanced bioinformatic tools for resistance gene identification and characterization; (3) Implement comprehensive computational workflows for tracking AMR transmission across human, animal, and environmental interfaces; (4) Develop skills in integrating multi-omics data for holistic resistance mechanism understanding.
The curriculum integrates theoretical instruction with hands-on computational training, utilizing real-world datasets and state-of-the-art bioinformatics platforms. Participants will engage with open-source tools such as ResFinder, ARG-ANNOT, and CARD (Comprehensive Antibiotic Resistance Database), learning to navigate complex genomic landscapes and extract meaningful resistance insights. By bridging computational skills, microbiological knowledge, and ecological perspectives, this course represents a critical intervention in professional education addressing the multifaceted challenge of antimicrobial resistance through an interdisciplinary, data-driven approach.