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

• Type of Material: Article
• Authors: Kenong Su, Emory University; Ataur Katebi, Northeastern University; Vivek Kohar, The Jackson Laboratory, Bar Harbor; Benjamin Clauss, Northeastern University; Danya Gordin, Northeastern University; Zhaohui Qin, Emory University; Krishna M Karuturi, The Jackson Laboratory for Genomic Medicine; Sheng Li, The Jackson Laboratory for Genomic Medicine; Mingyang Lu, Northeastern University
• Language: English
• Date: 2022-12-27
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2022
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13059-022-02835-3
• Title of Journal or Parent Work: GENOME BIOLOGY
• Volume: 23
• Issue: 1
• Start Page: 270
• End Page: 270
• Grant/Funding Information: The study is supported by startup funds from The Jackson Laboratory and Northeastern University, by the National Cancer Institute of the National Institutes of Health under Award Number P30CA034196, and by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM128717.
• Keywords: Macrophage polarization; Systems biology; PLURIPOTENCY; Genetics & Heredity; Epithelial-mesenchymal transition; Life Sciences & Biomedicine; Gene regulatory circuits; DATABASE; Mathematical modeling; INFERENCE; Biotechnology & Applied Microbiology; Cellular state transitions; Transcriptional activity; Science & Technology; Gene regulatory networks; MODEL; CANCER; PHENOTYPE; SET ENRICHMENT
• Research Categories: Biology, Bioinformatics; Biology, Biostatistics; Engineering, Biomedical

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.