MobiRes: An integrative pipeline for resistome risk prediction through mobilome profiling

Antimicrobial resistance (AMR) poses a significant global health challenge, with the environment serving as a crucial reservoir and conduit for resistance determinants. Although antibiotic resistance genes (ARGs) have been extensively studied in environmental contexts, systematic approaches for assessing and prioritizing the risks associated with mobile genetic elements (MGEs), such as plasmids, phages, transposons, and integrative elements (IEs), remain unclear. To address this gap, we present MobiRes, an open-source computational framework designed to predict resistome risk by integrating information from the mobilome and microbiome. The pipeline was evaluated using a wide range of publicly available metagenomic datasets spanning diverse environments, including wastewater, poultry, soil, sediments, and human fecal samples. To validate the framework, statistical analyses and machine learning models were applied to evaluate the role of MGEs in driving ARG dissemination. The pipeline identified transposons as the dominant MGE class while capturing environment-specific variation in plasmid, phage, and IE -associated ARGs. Transposon-associated ARGs showed the most consistent environmental differentiation (ANOVA p = 0.0017; Kruskal-Wallis p = 0.018), whereas plasmid and phage-associated ARGs varied moderately (p = 0.015-0.040) and IE-associated ARGs remained comparatively stable across environments (p > 0.05). The Random Forest (RF) model achieved an AUC of 0.82, and subsequent feature importance and SHapley Additive exPlanations (SHAP) analyses revealed that transposon abundance is the primary factor driving ARG dissemination across diverse environments. By integrating host, mobility, and ecological factors, MobiRes provides a scalable and One Health-oriented framework for comprehensive AMR risk assessment. This pipeline is publicly available at https://github.com/santhiyakc17/MobiRes_Pipeline.