使用ML组合动机,CRE活动和基因表达数据,大大提高了组织特定TF网络地图的准确性
Wooseok J Jung1, Sandeep Acharya2, Daniel P Ruskin3
1Department of Computer Science and Engineering, Washington University, St Louis, MO.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
甲基网通过整合TF动机,结合数据和基因表达,准确地重建组织特异性的转录因子网络. 与现有方法相比,这种新的方法显著改善了直接,功能性基因标的识别.
科学领域:
- 基因组学就是基因组学.
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 转录因子 (TF) 网络对于理解基因调节至关重要,但目前仅使用TF动机或基因表达数据的重建方法存在局限性.
- 现有的方法很难识别实际的TF绑定站点,并区分直接和间接的监管关系.
- 准确的TF网络必须是组织特异性的,因为TF活动和表达在不同人体组织中的差异.
研究的目的:
- 开发一种新的,监督集体学习方法,用于重建准确的,组织特定的TF网络.
- 整合多种数据类型,包括TF动机,TF结合位置,cis调节元 (CRE) 活性和基因表达数据.
- 加强直接,功能性TF基因标的识别,并提高转录性调节图的实用性.
主要方法:
- 引入了METANets (动机表达TF协会网络),一个监督集体学习框架.
- 利用XGBoost模型预测CREs中的TF结合,并结合了线性 (LASSO) 和非线性 (BART) 回归模型的特征.
- 在来自36个人类组织的组织特异性和聚合RNA-seq数据 (GTEx) 上训练模型,以捕获TF动机,TF结合和CRE活性.
主要成果:
- 在重建TF网络时,METANets显著优于现有的单纯动机和单纯表达式方法.
- 该方法成功地确定了更直接的,功能性的TF目标,通过ChIP-seq数据和基因本体学丰富验证.
- 使用表达量性特征位点 (eQTLs) 的组织特异性分析证实了METANets捕获组织特异性调节模式的能力.
结论:
- 通过整合互补的数据类型,METANets提供了一种强大而准确的方法来重建组织特定的TF网络.
- 开发的方法提高了转录调节图的准确性和实用性,用于研究人类组织中TF介导调节.
- 对于研究人员来说,METANets提供了一个宝贵的资源,研究各种人类组织中复杂的基因调节机制.
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