Interpreting antimicrobial resistance from bacterial whole-genome sequencing: prediction tools, database fragmentation, analytical trade-offs, and harmonized reporting

Whole-genome sequencing (WGS) has become a critical component of antimicrobial resistance (AMR) surveillance because it can characterize bacterial lineages, resistance determinants, and, when sequence resolution is sufficient, the mobile genetic elements that mediate dissemination. However, the practical value of WGS-based AMR inference remains constrained by fragmentation across AMR databases, inconsistent nomenclature, variable curation practices, and differences in analytical thresholds and reporting rules. Consequently, the same isolate may yield discordant resistome outputs across tools, limiting reproducibility, cross-study comparability, and surveillance integration. This review focuses on the interpretation of bacterial WGS data for AMR detection, with emphasis on AMR prediction tools, reference databases, read-mapping and assembly-based workflows, genotype–phenotype discordance, validation strategies, and harmonized reporting. General bioinformatics steps, including quality control, assembly, and polishing, are discussed only where they directly affect AMR inference, such as small-variant detection, plasmid reconstruction, and mobile genetic element context. The review further evaluates major AMR resources with respect to scope, curation depth, evidence models, updating practices, and interoperability across clinical and One Health applications. Rather than advocating a single universal database, we argue that the field would benefit more from federated harmonization based on shared ontologies, transparent provenance, versioned crosswalks, and benchmarked reporting standards. Within this context, AMR-GenoLink is introduced as a proposed reference framework for interoperable ingestion, standardized reporting, and provenance-aware integration of WGS-derived AMR evidence across human, animal, and environmental domains. The framework separates genomic feature detection from resistance interpretation, phenotype-linked validation, and evidence-proportionate reporting. Overall, this review argues that reliable WGS-based AMR interpretation is increasingly constrained not only by limitations in resistance-gene detection but also by insufficient harmonization across databases, analytical workflows, validation standards, and reporting frameworks.