MetaRanker: precise profiling of antibiotic resistome risk in metagenomes by integrating abundance and genetic co-occurrence

ABSTRACT The proliferation of antibiotic resistance genes (ARGs) in environmental microbiomes represents a major and growing threat to public health, creating a critical demand for precise and efficient tools to monitor resistance risk. Current approaches often depend on contig-based quantification or lack comprehensive risk indices, which compromises their accuracy and utility. To address this, we developed MetaRanker ( https://github.com/SteamedFish6/MetaRanker ), a computational pipeline that assesses resistome risk by integrating the abundance of ARGs, mobile genetic elements (MGEs), and virulence factors (VFs)—calculated directly from sequencing reads—with their genetic co-occurrence on contigs into a unified risk index (RI). This index reflects the potential for horizontal transfer and pathogen emergence. Evaluated using in silico and diverse real-world metagenomes ( n = 353), MetaRanker demonstrated superior accuracy and stronger discriminatory power than existing methods. Its optimized compact database (29.6 MB) and alignment strategy reduced runtime by over 50% in comparison to MetaCompare 2.0 under identical hardware configurations (32 CPU cores, 128 GB RAM). Practical applications confirmed that MetaRanker effectively discriminates risk levels across environments (e.g., hospital wastewater versus natural soil) and quantifies risk mitigation through wastewater treatment. As a robust, lightweight, and sequencing-platform-agnostic tool, MetaRanker offers a powerful solution for comprehensive environmental resistome surveillance and evidence-based risk management. IMPORTANCE The environmental reservoir of antibiotic resistance is a key contributor to the global health crisis of antimicrobial resistance. Effective surveillance and risk assessment of complex microbial communities are essential for prioritizing interventions and safeguarding public health. However, existing methods often provide fragmented or computationally demanding analyses, limiting their practical application for large-scale environmental monitoring. The significance of our work lies in developing MetaRanker, which overcomes these barriers by delivering a fast, accurate, and integrated metric of resistome risk. By simultaneously accounting for the abundance, mobility potential, and pathogenicity linkage of resistance determinants, MetaRanker enables a more realistic threat assessment. This tool empowers researchers and public health officials to track resistance hotspots, evaluate the impact of human activities such as waste disposal, and monitor the effectiveness of mitigation strategies, ultimately supporting data-driven decisions to curb the environmental spread of resistance.