DeepMobilome:使用对微生物组的测序阅读来预测移动遗传元素.
Youna Cho1, Erin Kim1, Minyoung Kim2
1Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seoul 04763, Republic of Korea.
Briefings in bioinformatics
|September 7, 2025
概括
在微生物组中,DeepMobilome准确地识别了携带抗生素耐药性基因 (ARG) 的移动遗传元素 (MGE). 该工具克服了现有方法的局限性,改善了我们对ARG传播的理解,并有助于抵抗控制.
科学领域:
- 微生物学 微生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 移动遗传元素 (MGE) 是微生物群落中抗生素耐药性基因 (ARG) 获取的关键驱动因素.
- 分析携带ARG的细胞组对于理解抗生素耐药性的演变至关重要.
- 现有的MGE预测工具与元基因组数据作斗争,导致高假阳性率.
研究的目的:
- 开发一种可靠的方法,在复杂的微生物组中准确识别MGEs.
- 克服目前MGE预测工具在元基因组分析中的局限性.
- 提高对ARG传播的理解,并支持针对抗生素耐药性的干预措施.
主要方法:
- 开发了DeepMobilome,一种使用卷积神经网络的新方法.
- 在从序列对齐图 (SAM) 文件中读取对齐数据上训练模型.
- 在模拟和真实微生物组数据集上验证了DeepMobilome,将性能与现有方法进行比较.
主要成果:
- DeepMobilome 实现了 0.99.9 的高验证准确度.
- 在单基因组测试中表现优于MGEfinder (F1=0.755) 和ISMapper (F1=0.670),F1得分为0.935.
- 在真实微生物组数据中成功识别了六种携带ARG的MGEs,证明了强度和可靠性.
结论:
- DeepMobilome在从元基因组数据中识别携带ARG的MGEs方面取得了重大进展.
- 与现有方法相比,该工具提供了更好的准确性和可靠性.
- 在研究ARG传播和制定打击抗生素耐药性的策略方面,DeepMobilome是一个宝贵的资产.
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