2022,
Genome Biology.
DOI:
10.1186/s13059-022-02835-3
NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity
Kenong Su
, Ataur Katebi
, Vivek Kohar
, Benjamin Clauss
, Danya Gordin
, Zhaohui S. Qin
, R. Krishna M. Karuturi
, Sheng Li
, Mingyang Lu
Abstract:
Abstract A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators’ activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.
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2022-12-31 14:45:00
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#paper doi: 10.1186/s13059-022-02835-3 Genome Biology, 2022, NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity。如何构建基因表达调控网络始终是系统生物学面临的重要课题。当前的基因调控网络构造方法普遍基于基因表达数据，而转录因子的功能往往体现在表达之外；此外，当前常用的基因表达调控网络构建的数学/统计方法擅长关注相关性而非因果性；这些缺陷使得当前对基因调控网络的构造效果不佳。本文从文献整理的转录因子和靶基因数据库出发，借助基因表达数据和GSEA提出了一种新的评估某过程中TF活性的策略。在评估的基础上使用互信息完成了基因表达调控网络的构造。本文中结合数据库中转录因子——靶基因关系和基因表达数据进行的转录因子活性定量方法是值得借鉴的。