Maize network analysis revealed gene modules involved in development, nutrients utilization, metabolism, and stress...

Area of Science:

• Plant genomics
• Bioinformatics
• Systems biology

Background:

• Big data in biology presents challenges for extracting biological insights.
• Maize has abundant public transcriptome data, but a comprehensive analysis is missing.

Purpose of the Study:

• To construct a maize gene co-expression network using massive RNA-seq data.
• To identify gene modules associated with various plant processes and agronomic traits.
• To compare gene association patterns with Arabidopsis to understand evolutionary divergence.

Main Methods:

• Construction of a maize gene co-expression network using the graphical Gaussian model.
• Analysis of massive RNA-seq data to identify gene modules.
• Comparison of the maize network with an established Arabidopsis network.

Main Results:

• A network of 20,269 genes organized into 964 modules involved in diverse plant processes.
• Identification of modules related to inflorescence development, nutrient uptake, metabolism, and stress response.
• Enrichment of domestication genes in the inflorescence development module, suggesting roles in plant architecture and kernel structure.
• Discovery of transcription factors as potential regulators within modules.
• Revealed conserved and divergent gene association patterns between maize and Arabidopsis.

Conclusions:

• The maize gene co-expression network provides insights into module organization across species and evolutionary divergence.
• Highlighted modules offer valuable resources for maize gene functional studies and potential applications.