DeepMobilome: predicting mobile genetic elements using sequencing reads of microbiomes

To address these challenges, we developed DeepMobilome, a novel approach for accurately identifying target MGEs within the microbiome. DeepMobilome leverages a convolutional neural network trained on read alignment data derived from sequence alignment map (SAM) files, providing superior accuracy in detecting MGEs. Trained on 364 647 cases, DeepMobilome achieved a high validation accuracy of 0.99. DeepMobilome consistently outperformed existing methods in discerning the presence of target MGE sequences across diverse test sets. In single-genome test scenarios, DeepMobilome showed an F1-score of 0.935, compared to 0.755 and 0.670 for MGEfinder and ISMapper, respectively, demonstrating its substantial improvements in prediction accuracy. Extensive evaluations across simulated microbiomes further validated the robustness and reliability of DeepMobilome in practical applications. In real microbiome data, DeepMobilome successfully identified six ARG-carrying MGEs across diverse populations. By addressing the limitations of current methods, DeepMobilome offers a powerful tool for advancing our understanding of ARG dissemination and supports targeted interventions in combating antibiotic resistance.