Building high-confidence gene regulatory networks by integrating validated TF-target gene interactions using ConnecTF

Authors: HUANG, JI - New York University; KATARI, MANPREET - New York University; JUANG, CHE-LUN - New York University; CORUZZI, GLORIA - New York University; Brooks, Matthew

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 9/24/2022
Publication Date: 9/9/2023
Citation: Huang, J., Katari, M.S., Juang, C., Coruzzi, G.M., Brooks, M.D. 2023. Building high-confidence gene regulatory networks by integrating validated TF-target gene interactions using ConnecTF. In: Kaufman, K., Vandepoele, K., editors. Plant Gene Regulatory Networks. 2nd edition. New York, NY: Humana. p. 195-220. https://doi.org/10.1007/978-1-0716-3354-0.
DOI: https://doi.org/10.1007/978-1-0716-3354-0

Interpretive Summary:

Technical Abstract: Many experimental methodologies are now available to identify or predict the target genes of transcription factor (TF) in plants. These include TF-target gene binding assays performed in vivo and in vitro, methods to identify regulated targets in mutants, transgenics, or isolated plant cells, and computational approaches to infer TF-target gene interactions from the regulatory elements or gene expression changes across treatments. While each of these approaches has now been applied to a large number of TFs from many species, the inherent limitations to any individual method necessitates that multiple data types are integrated to build the most accurate representation of the gene regulatory networks operating in plants. To make the most common of these analyses available to the broader research community, we have developed the ConnecTF web platform. In this chapter, we describe how ConnecTF can be used to integrate validated and predicted TF-target gene interactions in order to dissect the regulatory role of TFs in developmental and stress response pathways. Using as our example KN1 and RA1, two well-characterized maize TFs involved in developing floral tissue, we demonstrate how ConnecTF can be used to 1) compare the target genes between TFs, 2) identify direct vs. indirect targets by combining TF-binding and TF-regulation datasets, 3) chart and visualize network paths between TFs and their downstream targets, and 4) prune inferred user-networks for high-confidence predicted interactions using validated TF-target gene data. Finally, we provide instructions for setting up a private version of ConnecTF that enables research groups to store and analyze their own TF-target gene interaction datasets.