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EpGAT:整合表观遗传学和3D基因组结构,以预测替代拼接和多化

Sudipto Baul¹, Naima Ahmed Fahmi¹, Guangyu Wang²

¹Department of Computer Science, University of Central Florida, 4328 Scorpius Street, Orlando, FL 32816, United States.

Briefings in bioinformatics

|March 2, 2026

概括

一个新的模型,EpGAT,使用3D基因组结构和表观遗传数据来预测替代拼接和多化. 这个工具有助于理解基因组组织如何影响基因调节和RNA处理.

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:

基因调控涉及3D基因组结构和RNA处理事件,如替代拼接 (AS) 和替代多基 (APA) 之间的复杂相互作用.
了解染色体相互作用,表观遗传景观和这些RNA处理动态之间的联系至关重要,但在计算上具有挑战性.
现有的工具缺乏有效地将结构性基因组数据与RNA处理动态集成的能力.

研究的目的:

开发一个集结表观遗传数据和色素相互作用的计算模型,以预测和量化AS和APA事件.
探索3D基因组组织,包括染色体循环和长距离相互作用,如何影响RNA处理.
为剖析基因组架构和转录组调节之间的复杂相互作用提供一个工具.

主要方法:

开发EpGAT,一个基于图表注意力网络的模型.
综合了表观遗传读取覆盖范围和染色体相互作用数据.
显式建模基因组的空间组织以捕捉调节影响.

主要成果:

EpGAT通过整合结构性基因组和表观遗传数据,准确地预测和量化AS和APA事件.
通过跨细胞系和跨染色体评估的模型验证证明了可概括性和可靠性.
通过识别关键的基因组特征和影响RNA处理的表观遗传因素,EpGAT提供了可解释性.

结论:

EpGAT为研究3D基因组组织,表观遗传信号和RNA处理之间的关系提供了一个强大的和可概括的框架.
该模型有助于识别监管元素,如增强剂,并绘制促进剂-增强剂连接的地图.
这项工作推进了我们对由基因组架构驱动的多层基因表达调节的理解.

关键词:

替代性的多基化.另一个替代拼接方法是拼接.注意力系数注意力系数染色素相互作用的相互作用表观遗传学是指表观遗传学.图表注意力网络图表注意力网络促进者增强者互动互动.

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