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预测基于CRISPR-Cas9的表观基因组编辑的影响

Sanjit Singh Batra¹, Alan Cabrera², Jeffrey P Spence³

¹Computer Science Division, University of California, Berkeley, United States.

|January 12, 2026

概括

机器学习模型预测基因表达的基因组修改,但通过表观基因组编辑控制表达需要更好的数据集和因果模型用于人类健康应用.

科学领域:

基因组学和表观遗传学
计算生物学计算生物学
分子生物学分子生物学

背景情况:

表观遗传调节控制了哺乳动物的转录,但直接的功能联系尚未完全理解.
基因组转化后修饰 (PTMs) 是影响基因表达的关键表观遗传标记.

研究的目的:

开发机器学习模型,利用ENCODE数据预测基因表达的基因表达.
以实验验证模型预测,特别是H3K27ac的作用,使用表观基因组编辑工具.
评估模型在预测表达变化从内源特征与实验性扰动的表达性能.

主要方法:

在13种人类细胞类型的表观基因组和转录基因组数据上训练机器学习模型.
在HEK293T细胞中利用MNase-seq进行全基因组核体占用映射.
在HEK293T和K562细胞中使用dCas9-p300系统进行向基乙化 (H3K27ac).

主要成果:

模型实现了高相关性 (∼0.70-0.79) 预测基因表达从细胞类型跨细胞组PTMs.
已验证的已知关联,例如在转录开始地点附近的H3K27ac增强表达.
模型在表观基因组编辑后准确排列了相对基因表达变化,但与细胞类型预测相比,对基因内折叠变化的准确性降低了.

结论:

机器学习有效地预测来自内源性表观遗传状态的基因表达.
从表观基因组编辑扰乱中预测表达变化更具挑战性,这表明需要改进数据集和因果模型.
在表观基因组编辑数据集和建模方面的进步对于理解和控制健康应用的人类表观基因组至关重要.

关键词:

计算生物学是计算生物学.基因表达的基因表达方式人类人类人类人类人类人类人类机器学习是机器学习.核细胞的占用率核细胞的占用率干扰是一种干扰.翻译后的修改翻译后的修改系统生物学系统生物学

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