使用数百万个酵母促进体预测基因表达揭示了cis-regulatory逻辑
Tirtharaj Dash1,2, Susanne Bornelöv1,2
1Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, United Kingdom.
Bioinformatics advances
|June 26, 2025
概括
像Camformer这样的深度学习模型可以从DNA序列准确地预测基因表达. 可解释的AI揭示了转录因子动机及其相互作用如何驱动基因调节.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 基因调节是一个复杂的过程,涉及转录因子及其相互作用.
- 预测基因表达传统上使用自然发生的促进体,但深度学习现在利用来自记者分析的扩展数据集.
- 为了研究基因调节,深度学习的最佳应用仍然是一个活跃的研究领域.
研究的目的:
- 使用深度学习模型研究DNA促进序列和基因表达之间的关联.
- 在预测基因表达时识别模型性能的关键决定因素.
- 利用可解释的人工智能 (XAI) 技术,在促进器序列中发现调节信号.
主要方法:
- 在670万个序列上开发和训练了Camformer,一个残余卷积神经网络.
- 研究了270个替代模型,以了解影响预测性能的因素.
- 采用Grad-CAM和in silico mutagenesis进行可解释的AI分析学习的调节信号.
主要成果:
- 在解码促进体-表达关联 (R2 = 0.914 ± 0.003, ρ = 0.962 ± 0.002) 中,Camformer 实现了高精度,超过了以前的方法.
- 该模型成功地学习了个别的转录因子动图及其等级相互作用.
- 证明了特定的基因组合 (例如,IME1和UME6) 如何协同影响基因表达水平.
结论:
- 深度学习模型,以Camformer为例,为剖析复杂的基因调节机制提供了强大的工具.
- 可解释的AI方法可以揭示复杂的cis-regulatory逻辑,包括转录因子结合位点的协同和对抗效应.
- 这项研究为推进基因调节研究提供了一个强大的框架和开源资源.
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