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.