NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity

Genome Biology

Method

Benjamin Clauss¹  Danya Gordin²  Ataur Katebi²  Mingyang Lu³  Kenong Su⁴  Zhaohui S. Qin⁵  Sheng Li⁶  R. Krishna M. Karuturi⁷  Vivek Kohar⁸ 

[1] Center for Theoretical Biological Physics, Northeastern University, 02115, Boston, MA, USA;Genetics Program, Graduate School of Biomedical Sciences, Tufts University, 02111, Boston, MA, USA;Department of Bioengineering

[2] , Northeastern University, 02115, Boston, MA, USA;Center for Theoretical Biological Physics, Northeastern University, 02115, Boston, MA, USA;Department of Bioengineering

[3] , Northeastern University, 02115, Boston, MA, USA;Center for Theoretical Biological Physics, Northeastern University, 02115, Boston, MA, USA;The Jackson Laboratory, 04609, Bar Harbor, ME, USA;Genetics Program, Graduate School of Biomedical Sciences, Tufts University, 02111, Boston, MA, USA;Department of Biomedical Informatics, Emory University, 30322, Atlanta, GA, USA;Department of Biostatistics and Bioinformatics, Emory University, 30322, Atlanta, GA, USA;The Jackson Laboratory for Genomic Medicine, 06032, Farmington, CT, USA;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA;The Jackson Laboratory for Genomic Medicine, 06032, Farmington, CT, USA;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA;Graduate School of Biological Sciences & Eng., University of Maine, Orono, ME, USA;The Jackson Laboratory, 04609, Bar Harbor, ME, USA;

关键词: Systems biology;    Gene regulatory networks;    Gene regulatory circuits;    Cellular state transitions;    Mathematical modeling;    Transcriptional activity;    Epithelial-mesenchymal transition;    Macrophage polarization;   

DOI  :  10.1186/s13059-022-02835-3

 received in 2022-05-06, accepted in 2022-12-05,  发布年份 2022

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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