NGS has transformed microbiological workflows, offering high-resolution characterization of AMR mechanisms in ways that were previously fragmented and labor-intensive. Traditionally, microbiologists relied on multiple complex techniques, including phenotypic assays, PCR-based resistance gene detection, multilocus sequence typing (MLST), pulsed-field gel electrophoresis (PFGE), and plasmid profiling, to identify resistance genes, virulence factors, and epidemiological relationships (Calero-Cáceres et al., 2023). These approaches, while informative, were time-consuming and often lacked the resolution needed to track AMR evolution and transmission in real time.

The advent of WGS has streamlined these processes, providing a single, comprehensive method for detecting antimicrobial resistance genes (ARGs), mobile genetic elements (MGEs), virulence traits, clonal relatedness, and phylogenetic profiles in one sequencing run. WGS is now a cornerstone of AMR surveillance in clinical settings, facilitating outbreak detection, strain characterization, and retrospective epidemiological investigations (Waddington et al., 2022). Meanwhile, shotgun metagenomics broadens the scope of AMR surveillance, allowing for the detection of resistance determinants directly from complex environmental and community samples, capturing AMR dynamics in unculturable bacteria and understudied reservoirs (Pillay et al., 2022). Together, these technologies are reshaping AMR monitoring across human, animal, and environmental health sectors, providing an unprecedented opportunity to trace resistance evolution and dissemination holistically.

Despite significant advancements, genomics-based AMR surveillance remains fragmented, predominantly focused on clinical settings with insufficient integration across One Health compartments. WGS and metagenomics have yet to be fully incorporated into veterinary and environmental health programs, largely due to technical and financial barriers, including high sequencing costs, bioinformatics complexity, and the absence of standardized workflows for data analysis and resistance gene annotation (Calero-Cáceres et al., 2024). Additionally, inconsistencies in sequencing protocols across laboratories and countries hinder cross-sector comparability, limiting the ability to track resistance transmission across geographic and ecological boundaries (Ristori et al., 2024).

To maximize the impact of WGS and metagenomics in AMR surveillance, a globally coordinated and harmonized framework is needed. Standardizing sequencing methodologies, bioinformatics pipelines, and data-sharing platforms will facilitate interoperability across human, animal, and environmental health sectors (Calero-Cáceres et al., 2024). Addressing resource constraints in low- and middle-income countries (LMICs) through investments in capacity-building, real-time data integration, and public-private partnerships will be crucial to bridging existing gaps and establishing a truly comprehensive AMR monitoring system (Calero-Cáceres, 2024).

The global challenge of AMR extends beyond clinical settings. While hospital-based surveillance has provided critical insights into AMR evolution, its scope remains narrow, capturing only a fraction of the broader resistance landscape. The environmental and animal reservoirs of AMR—wastewater, agricultural runoff, food, wildlife, and airborne microbiota—are under-monitored, limiting our ability to track resistance dissemination effectively (Endale et al., 2023). Moreover, the lack of standardized sampling methodologies, sequencing approaches, and data-sharing frameworks creates gaps in surveillance, hindering global efforts to mitigate resistance spread (Black et al., 2020).

Expanding surveillance beyond hospitals requires a dual strategy: (i) WGS of bacterial isolates from clinical and animal settings and (ii) Shotgun metagenomics for complex matrices in environmental and community settings. While WGS provides high-resolution data on priority pathogens and emerging resistance mechanisms, metagenomics enables the identification of resistance hotspots and reservoirs of dissemination, both of which are critical to One Health AMR surveillance (Black et al., 2020).

WGS is emerging as the most comprehensive approach for tracking antimicrobial-resistant bacteria in human and animal health, providing high-resolution detection of genetic resistance determinants, MGEs, and insights into strain evolution (Figure 1). Its implementation in AMR surveillance provides a robust framework for identifying high-risk strains, tracing transmission pathways, and monitoring the emergence of novel resistance mechanisms. Prioritizing WGS for WHO Priority Pathogens and newly emerging resistant bacteria is essential, particularly for strains exhibiting multidrug-resistant (MDR) or extensively drug-resistant (XDR) phenotypes (Calero-Cáceres et al., 2023; Almutairy, 2024; World Health Organization, 2024).

WGS enables precise characterization of genetic determinants associated with resistance dissemination, including plasmids, integrons, transposons, prophages, and mutations known to confer antimicrobial resistance (Algarni et al., 2022). Integrating phylogenetic analysis and strain typing improves our understanding of epidemiological linkages, delineates transmission routes, identifies reservoirs, and highlights bacterial populations facilitating resistance gene transfer. However, WGS alone cannot characterize unculturable microbial reservoirs, underscoring the complementary need for metagenomics to capture the broader resistome across complex ecosystems.

While WGS provides high-resolution insights into individual resistant bacteria, metagenomics enables a more comprehensive analysis of AMR within complex microbial communities, overcoming the limitations of culture-dependent methods (Pillay et al., 2022). By directly sequencing environmental DNA, metagenomics allows for the detection of resistance determinants in diverse reservoirs, including wastewater, food production systems, livestock environments, airborne microbiota, migratory animals, and vectors such as rodents and insects (Duarte et al., 2021). These compartments serve as critical hotspots for resistance gene exchange, driving the emergence and dissemination of novel resistance mechanisms across One Health sectors (Figure 1).

Beyond antimicrobial resistance gene (ARG) identification, metagenomic analysis facilitates the functional annotation of virulence factors, the epidemiological tracking of high-priority pathogens, and the surveillance of AMR trends at a broader ecological scale. Such data are essential for pinpointing transmission hotspots and assessing risks associated with environmental and community exposures, enabling early intervention before clinical outbreaks occur.

Integrating metagenomics with isolate-based WGS further strengthens surveillance efforts by providing complementary evidence that isolates alone cannot, supporting early detection of emerging resistance threats. Therefore, standardization of sequencing depth, bioinformatics pipelines, and data-sharing protocols across sectors will be essential for optimizing the effectiveness of metagenomics in AMR monitoring and interventions globally (Calero-Cáceres et al., 2024).

The reliability of genomic AMR surveillance depends on well-defined sequencing protocols, encompassing sample processing, sequencing methodology, and bioinformatics workflows (Figure 1). For isolate-based WGS, high-quality genomic DNA extraction, rigorous quality control, and standardized library preparation are crucial for accurate resistance detection (Sherry et al., 2023). Short-read sequencing platforms (e.g., Illumina) offer high accuracy for single nucleotide polymorphism (SNP) detection, ARG annotation, phylogenetic analysis, and genome-wide association studies (GWAS). Long-read technologies (e.g., Oxford Nanopore, PacBio) facilitate complete genome assemblies, precise plasmid reconstruction, and structural variation analysis (Bejaoui et al., 2025). Sequencing depth depends on study objectives: ≥100 × coverage is needed for precise SNP detection, plasmid tracking, and outbreak investigations, whereas 30–50 × coverage suffices for broader epidemiological surveillance and resistance gene identification.

For metagenomic surveillance, optimized DNA extraction methods are critical due to the complexity and variability of environmental samples, where resistance genes may be present at low abundances (Calero-Cáceres et al., 2024). Library preparation must minimize biases related to mixed microbial communities, and sequencing platform selection should balance resolution, accuracy, and cost-efficiency (Poulsen et al., 2022). Short reads offer affordable, high-throughput characterization of complex resistomes, whereas long reads enhance detection of MGEs, including plasmids and integrons. Sequencing depth must balance sensitivity for rare resistance genes with financial feasibility.

Regardless of approach, standardized bioinformatics pipelines are essential for consistent ARG annotation, strain typing, and comparative genomic analyses (Bogaerts et al., 2021). Integration with globally accessible AMR databases (e.g., NCBI, Pathogenwatch, ResFinder) will enhance data interoperability and facilitate real-time monitoring across sectors. Global harmonization of these technical elements is critical for robust AMR genomic surveillance, enabling early identification and response to emerging resistance threats (Argimón et al., 2021; Florensa et al., 2022).