使用染色体深度学习模型从基因标记预测基因表达,取决于基因标记功能,调节距离和细胞状态
Alan E Murphy1,2, Aydan Askarova1,2, Boris Lenhard3
1UK Dementia Research Institute at Imperial College London, 86 Wood Lane, London W12 0BZ, UK.
Nucleic acids research
|December 11, 2024
概括
基因标记活性和基因表达是相关的,但细胞状态和基因组位置很重要. 机器学习模型现在考虑这些因素来预测转录并揭示生物学见解.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 从基因标记中预测基因表达对于理解表观遗传学至关重要.
- 之前的机器学习模型忽略了细胞状态,基因素标记功能和远端调节元素.
- 需要更全面的模型来整合这些因素,以获得更深入的生物学洞察力.
研究的目的:
- 综合调查七种组分体标记和11种细胞类型和多种细胞状态的基因表达之间的关系.
- 开发和应用先进的深度学习模型 (卷积和基于注意力的模型) 来预测来自激素标记活动的转录,无论是在促进器和远程调节元件上.
- 通过in silico扰动测试,探索这些模型的实用性,以发现新的生物学见解,包括功能和疾病相关的位置.
主要方法:
- 利用了大规模的数据集,包括11种不同的细胞类型的7个组素标记.
- 采用卷积神经网络和基于注意力的深度学习模型来预测转录活动.
- 在近端 (促进体) 和远端调节元件中研究了基因素标记活性.
- 在体中进行的基因素标记扰动测试,以评估功能影响.
主要成果:
- 证明了基因组标记功能,基因组距离和细胞状态共同影响基因组标记和转录之间的关系.
- 发现没有一个单一的基因组标记能够在所有基因组和细胞环境中一致预测基因表达.
- 通过in silico扰动实验确定了特定的功能和与疾病相关的基因组位置.
- 建立了一个利用染色体深度学习模型产生新生物学发现的框架.
结论:
- 基因标记活性和基因表达之间的关系是复杂的,并且取决于环境,受细胞状态和基因组位置的影响.
- 从表观遗传数据中准确预测转录状态,需要考虑多个因素的整体方法.
- 深度学习模型提供了一个强大的平台,用于剖析表观遗传调节,并发现新的生物学见解,包括潜在的治疗点.
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