Regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent a map of potential transcriptional regulation. A consistent analysis of a large number of public maize transcriptome datasets including >6000 RNA-Seq samples was used to generate 45 co- expression based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, tissue-and-genotype, etc). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes.
These are the processed datasets used to create networks (raw and filtered expression tables) and predicted interactions
Sponsorship: This study was funded by grants from the National Science Foundation (IOS-1546899 and IOS- 1733633). This work is supported in part by Michigan State University and the National Science Foundation Research Traineeship Program (DGE-1828149) to FGC. No conflict of interest declared.