用于水平基因转移检测的基因组数据表示
Andre Jatmiko Wijaya1,2, Aleksandar Anžel1, Hugues Richard2,3
1Center for Artificial Intelligence in Public Health Research (ZKI-PH), Robert Koch Institute, Nordufer 20, 13353 Berlin, Germany.
NAR genomics and bioinformatics
|December 12, 2025
概括
机器学习模型可以更有效地检测抗微生物耐药性 (AMR) 基因的水平基因转移 (HGT). 最佳的基因组数据表示,如RCKmer,显著提高模型性能用于识别超级细菌.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 横向基因转移 (HGT) 通过移动遗传元素加速了抗菌素耐药性 (AMR) 的传播,推动了多抗药性病原体的进化.
- 检测HGT的传统方法缺乏复杂转移事件的解决方案,阻碍了减轻AMR的努力.
- 机器学习 (ML) 显示了改善HGT检测的希望,但其性能高度依赖于所选择的基因组数据表示.
研究的目的:
- 评估不同基因组数据表示对ML模型HGT检测性能的影响.
- 确定最佳的数据表示和ML模型组合,以准确检测HGT事件,特别是涉及AMR基因的事件.
- 为基因监测和AMR基因转移的识别提供最先进的模型.
主要方法:
- 使用五种不同的ML模型评估了44种不同的基因组数据表示.
- 在四个不同的数据集中测试模型性能,以评估概括能力.
- 使用F1分数和马修斯相关系数 (MCC) 等指标来量化性能.
主要成果:
- 在HGT检测中ML模型的性能受到所使用的基因组数据表示的强烈影响.
- 基于RCKmer的表示 (k=7) 与支持矢量机相结合,实现了最佳性能 (F1:0.959;MCC:0.908).
- 在多个物种数据集上训练的模型表现出优异的概括性,突出了多样化的训练数据的好处.
结论:
- 特定任务的基因组数据表示对于增强基于ML的基因组监测至关重要.
- RCKmer表示为检测HGT事件提供了一种高度有效的方法,有助于识别AMR基因转移.
- 这项研究提供了优化的模型,用于识别基因组岛屿,并了解物种之间的AMR基因流动.
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