预测基于CRISPR-Cas9的表观基因组编辑的影响
Sanjit Singh Batra1,2, Alan Cabrera1,3, Jeffrey P Spence1,4
1Equally contributing authors.
bioRxiv : the preprint server for biology
|October 24, 2023
概括
机器学习模型可以从基因基因修饰中预测基因表达,在细胞类型中实现高精度. 然而,从工程表观遗传编辑中预测表达变化是具有挑战性的,突出了需要更好的表观基因组编辑数据集和因果模型的需求.
科学领域:
- 基因组学就是基因组学.
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
- 计算生物学 计算生物学
背景情况:
- 表观遗传调节和哺乳动物转录之间的功能联系尚未完全理解.
- 基因组转化后修改 (PTMs) 在编排基因表达方面发挥着至关重要的作用.
研究的目的:
- 开发机器学习模型来预测基因表达的基因组 PTMs.
- 通过实验验证模型对基因素乙化 (H3K27ac) 和基因表达的预测.
- 评估模型在预测来自工程表观遗传修饰的表达变化的性能.
主要方法:
- 在13种ENCODE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.
- 利用dCas9-p300系统在HEK293T和K562细胞系中进行向性素乙化.
- 执行MNase-seq以映射HEK293T细胞中的核细胞占用率.
主要成果:
- 机器学习模型实现了高的转录组宽相关性 (0.70-0.79) 来预测来自内源性基因素PTMs的基因表达.
- 模型准确地预测了已知的关联,例如在转录开始部位 (TSS) 附近的H3K27ac增加了表达.
- 模型显示,与预测内源性表达相比,在工程H3K27ac沉积后,在单个基因内排列折叠变化的能力下降.
结论:
- 机器学习可以有效地从内源性表观遗传状态预测基因表达.
- 预测工程表观遗传修饰产生的表达变化需要进一步开发表观基因组编辑工具和因果模型.
- 改进的数据集和模型对于理解和控制健康应用的人类表观基因组至关重要.
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