利用转录因子的物理接近来增强基因调节的推断
Xiaoqing Huang1, Aamir R Hullur2, Elham Jafari2
1Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, United States.
Bioinformatics (Oxford, England)
|July 15, 2025
概括
我们介绍了GRIP,一种新的基因调节推断方法,它考虑了转录因子 (TF) 的物理近距离. 通过识别在蛋白质-蛋白质相互作用网络中接近的TFs,GRIP提高了准确性,提高了对基因调节的理解.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 基因调节推断对于理解细胞功能,如分化和应激反应至关重要.
- 目前的方法使用基因表达和TF结合,但忽视了TF物理接近.
- 转录因子 (TFs) 必须在物理上接近以有效调节基因表达.
研究的目的:
- 开发一种新的基因调节推断方法,该方法结合了转录因子 (TF) 之间的物理接近.
- 提高基因调节网络重建的准确性和生物相关性.
主要方法:
- 开发了GRIP (通过考虑TF近距离进行基因调控推断),一种使用蛋白质与蛋白质相互作用 (PPI) 网络距离来建模TF近距离的方法.
- 设计了一个布尔凸程序来识别解释目标基因表达的TF,并且在PPI网络中是近位的.
- 提出了一种高效的算法来解决模型的理论保证,并对PBMC 3k scMultiome-seq数据进行了验证.
主要成果:
- 在推断细胞类型特定的基因调控方面,GRIP优于最先进的方法 (SCENIC+,DirectNet,Pando,CellOracle).
- 由GRIP推断的TF显示出更好的预测能力,更近的物理距离,以及与PCHiC数据更好的一致性.
- 证明了GRIP在识别生物学上相关的TF目标基因相互作用方面的有效性.
结论:
- 通过整合TF物理近距离,GRIP提供了一种更准确,更有生物学依据的基因调控推断方法.
- 该方法增强了对基因调节机制和网络结构的理解.
- GRIP代表了系统生物学在剖析复杂的细胞过程中的重大进步.
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