Computational Genomics for Resistome Characterization: Current Advancements and Future Challenges Under a One Health Perspective
TL;DR
Next-generation sequencing technologies have significantly improved the characterization of ARGs across ecosystems, enabling high-resolution microbial profiling and the discovery of new variants, and integrating multi-omics approaches with computational tools improves data accuracy, reduces analysis and reporting times, and facilitates the development of predictive models.
Abstract
The resistome, defined as the complete set of antibiotic resistance genes (ARGs) present in the microbiota of a given environment, is a critical component for understanding the evolutionary dynamics of antimicrobial resistance (AMR) and its impact on human, animal, and environmental health. This review summarizes current methods and technological advances and offers a forward-looking perspective on resistome research. A systematic literature search was conducted. References on short-read and long-read sequencing, amplicon sequencing, shotgun metagenomics, and multi-omics integration were included, as were bioinformatics tools for the detection, quantification, and annotation of ARGs. The results indicate that next-generation sequencing (NGS) technologies have significantly improved the characterization of ARGs across ecosystems, enabling high-resolution microbial profiling and the discovery of new variants. Furthermore, integrating multi-omics approaches with computational tools improves data accuracy, reduces analysis and reporting times, and facilitates the development of predictive models. However, significant challenges remain, which will be key to strengthening epidemiological surveillance under the One Health approach.