SpecLoop通过转录因子合作来预测细胞类型特定的染色质循环
Lixin Ren1, Wanbiao Ma1, Yong Wang2
1Department of Applied Mathematics, School of Mathematics and Physics, University of Science and Technology Beijing, 100083, Beijing, China.
Computers in biology and medicine
|February 29, 2024
概括
机器学习工具SpecLoop通过分析转录因子合作来预测细胞类型特定的染色质循环. 它整合了多omics数据,揭示了驱动基因调节和细胞命运的关键转录因子相互作用.
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:
- 细胞类型特定的染色质环 (CSCLs) 对于调节基因表达和确定细胞身份至关重要.
- 建立CSCL的确切机制,特别是转录因子 (TF) 合作的作用,尚未完全理解.
研究的目的:
- 开发和验证一个机器学习框架,SpecLoop,用于预测CSCLs.
- 通过使用综合的多学科数据,调查TF合作对CSCL形成的贡献.
主要方法:
- SpecLoop 框架使用网络规范化来整合多种omics数据:基因表达,染色质可访问性,DNA序列,蛋白质与蛋白质相互作用和TF结合动机.
- 高分辨率的Hi-C数据作为预测7种人类细胞类型中CSCLs的基准.
- 性能是使用接收器操作特征下的面积 (AUROC) 和精度召回 (AUPR) 曲线下的面积来评估的.
主要成果:
- 在7种细胞类型 (GM12878,IMR90,HeLa-S3,K562,HUVEC,HMEC,NHEK) 中,SpecLoop在预测CSCL方面取得了很高的准确性,AUROC值从0.8645到0.9852,AUPR值从0.8654到0.9734.
- 该框架在预测远程CSCL方面表现更好.
- SpecLoop成功地确定了特定的TF复合体,这些复合体对CSCL形成具有强大的预测能力.
结论:
- SpecLoop框架通过整合多主题数据有效预测CSCL,并突出了TF合作在它们形成中的关键作用.
- 这项研究提供了与CSCLs相关的TF和TF对的系统探索,进步了我们对基因调节的理解.
- SpecLoop是公开的,这有助于进一步研究染色体组织和基因调节.
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