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DeepMobilome: predicting mobile genetic elements using sequencing reads of microbiomes.

Youna Cho1, Erin Kim1, Minyoung Kim2

  • 1Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seoul 04763, Republic of Korea.

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|September 7, 2025
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DeepMobilome accurately identifies mobile genetic elements (MGEs) carrying antibiotic resistance genes (ARGs) in microbiomes. This tool overcomes limitations of existing methods, improving our understanding of ARG spread and aiding resistance control.

Keywords:
convolutional neural networksdeep learningmobile genetic elementread alignmenttarget discovery

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Mobile genetic elements (MGEs) are key drivers of antibiotic resistance gene (ARG) acquisition in microbial communities.
  • Analyzing the ARG-carrying mobilome is crucial for understanding antibiotic resistance evolution.
  • Existing MGE prediction tools struggle with metagenomic data, leading to high false positive rates.

Purpose of the Study:

  • To develop a robust method for accurately identifying MGEs within complex microbiomes.
  • To overcome the limitations of current MGE prediction tools in metagenomic analysis.
  • To enhance the understanding of ARG dissemination and support interventions against antibiotic resistance.

Main Methods:

  • Developed DeepMobilome, a novel approach utilizing a convolutional neural network.
  • Trained the model on read alignment data from sequence alignment map (SAM) files.
  • Validated DeepMobilome on simulated and real microbiome datasets, comparing performance against existing methods.

Main Results:

  • DeepMobilome achieved a high validation accuracy of 0.99.
  • Outperformed MGEfinder (F1=0.755) and ISMapper (F1=0.670) in single-genome tests with an F1-score of 0.935.
  • Successfully identified six ARG-carrying MGEs in real microbiome data, demonstrating robustness and reliability.

Conclusions:

  • DeepMobilome provides a significant advancement in identifying ARG-carrying MGEs from metagenomic data.
  • The tool offers improved accuracy and reliability compared to existing methods.
  • DeepMobilome is a valuable asset for studying ARG dissemination and developing strategies to combat antibiotic resistance.