Maize tissue GRN

A tissue-specific gene regulatory network for maize🌽

Understanding how organisms regulate gene expression is one of the most important and complex challenges in biology, particularly in eukaryotes with large genomes. Plant biologists have long been at the forefront of research in gene regulation, from Gregor Mendel to Barbara McClintock.

Maize (Zea mays) was McClintock’s plant model of choice, and has served as a model organism for over a hundred years. Maize is also of substantial economic significance in the United States. In recent years, emerging technologies have provided the maize research community with many new datasets that can be used to investigate genome-wide expression changes. The datasets can be mined to construct meaningful depictions of regulatory networks, including Gene Regulatory Networks (GRNs).

In this study, we have constructed and validated maize GRNs from RNA-Seq expression data for leaf, root, SAM and seed tissue using a machine learning algorithm. This study builds on our prior work and generated GRNs with 2241 TFs and provided a high enough level of resolution to reveal the spatial variation of gene regulation.

Citation coming up.

Tissue and Pattern(needed):

as TF as target
Optional sets:


Website features:

  1. Predicted “TF-Target” regulatory interactions can be searched and downloaded from “Search” page. Users can use “TF” (By TF) or “Target” (By Target) to query database.
  2. Database can be queried by AGPv2/v3 gene IDs. The new AGPv4 IDs should be converted to AGPv3 using “ID convert”.
  3. Original data and source code can be accessed from “Download” page.


  1. Query genes in the “Gene ID” box (less than 5 genes). Genes should be separated by comma, space or new line.
  2. Choose the tissue to search (leaf, root, SAM or seed). At least one is required.
  3. Choose “by TF” if query genes are TFs and search for their putative targets; choose “by target” if query genes are targets and search for putative TF regulators.
  4. “Summary” will return a result table based selected parameters.
    • Double click numbers will show putative targets/TFs for the chosen category.
    • Double click gene ID or tissue will show interactive Venn diagram. Venn diagram only works between 2 to 4 intersections. Double click intersection regions will return overlap gene IDs.
    • Searched result can be downloaded in SIF format and import into Cytoscape for further analysis.
  5. User can choose to exhibit top X hits (X < 99) in details for each gene.


  • Gene information is based on AGPv3.31.
  • The BLASTP best hit Arabidopsis (TAIR10) results with annotations are included.
  • Double click gene IDs will redirect to external databases for easy mining (GRASSIUS, MaizeGDB or Araport).
  • The result table can be downloaded as tab-delimited (tsv) file.

Options: v3->v4
show description

Contact information:

Department of Biological Science, Florida State University, Tallahassee, United States

Dr. Karen M. McGinnis
Office: 2019 King Life Sciences
Lab: King Life Sciences
E-mail: mcginnis@bio.fsu.edu

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