增强器匹配器:使用深度卷积神经网络和可解释的AI比较DNA序列的细胞类型特定增强器活性
Luis M Solis1, William L Melendez1, Shantanu H Fuke1
1The Bioinformatics Toolsmith Laboratory, Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, United States.
NAR genomics and bioinformatics
|November 13, 2025
概括
EnhancerMatcher准确地识别了细胞类型特定的转录增强剂,使用一种新的深度学习方法,仅使用两个参考增强剂. 这种多功能工具有助于跨物种的基因调节发现.
科学领域:
- 基因组学和生物信息学
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 转录增强剂是关键的调控元素,但由于它们的可变基因组位置和与目标基因相对的方向,很难在计算上识别.
- 实验验证增强剂的有限可用性阻碍了准确的机器学习模型的开发,以提高增强剂的预测.
研究的目的:
- 开发一种新的计算工具,EnhancerMatcher,用于准确有效地识别细胞类型特定的转录增强剂.
- 克服现有方法的局限性,通过使用最小的参考数据,在各种细胞类型中实现增强剂预测.
主要方法:
- 开发了EnhancerMatcher,这是一个基于卷积神经网络 (CNN) 的工具,使用两个已确认的增强器作为序列分类的参考.
- 在CATlas项目和人类基因组控制序列的假定增强剂上训练模型.
- 通过将序列分为三胞胎来评估增强剂活性:两个已知的增强剂和第三个测试序列.
主要成果:
- 在人类测试数据上,EnhancerMatcher实现了高性能,准确率为90%,回忆率为92%和特异性为87%.
- 证明了强大的跨物种概括性,使用人类训练模型成功识别了小鼠增强剂.
- 无论数据表示大小如何,在各种单元类型中展示了一致的性能,并通过类激活地图提供了可解释性.
结论:
- 增强器Matcher是一个强大的,多功能和可通用的工具,用于增强器发现和监管序列分析.
- 基于CNN的方法有效地利用最小的参考增强剂来进行强大的细胞类型特定增强剂识别.
- 该工具能够进行跨物种预测并提供可解释的结果,这提高了它在基因组研究中的实用性.
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