DeepMobilome: predicting mobile genetic elements using sequencing reads of microbiomes

Abstract

Motivation: Mobile genetic elements (MGEs) play an important role in facilitating the acquisition of antibiotic resistance genes (ARGs) within microbial communities, significantly impacting the evolution of antibiotic resistance. Understanding the mechanism and trajectory of ARG acquisition requires a comprehensive analysis of the ARG-carrying mobilome-a collective set of MGEs carrying ARGs. However, identifying the mobilome within complex microbiomes poses considerable challenges. Existing MGE prediction methods, designed primarily for single genomes, exhibit substantial limitations when applied to metagenomic data, often producing high false positive rates in identifying target MGEs from metagenome sequencing data. Results: 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.