Discovering functional modules across diverse maize transcriptomes using COB, the Co-expression Browser.

Area of Science:

• Plant Genomics
• Bioinformatics
• Computational Biology

Background:

• Relating genotype to phenotype is crucial for understanding molecular variants and accelerating crop breeding.
• Large-scale gene expression data in maize offers an opportunity to study complex traits.

Purpose of the Study:

• To build and provide access to genome-wide co-expression networks in maize.
• To leverage these networks for understanding gene function and trait associations.

Main Methods:

• Constructed co-expression networks using genome-wide expression data from diverse maize accessions, tissues, and developmental stages.
• Developed a public, web-based Co-expression Browser (COB) for interactive network queries.

Main Results:

• Identified gene clusters enriched for known biological functions and novel structures within the networks.
• Demonstrated that developmental/tissue-specific networks capture unique functions compared to accession-based networks.
• Showcased COB's utility in providing functional context for metabolic pathways and validating gene lists from mapping studies.

Conclusions:

• Maize co-expression networks offer valuable insights into complex traits and gene function.
• The Co-expression Browser (COB) is a useful tool for researchers studying maize genetics and breeding.
• Integrating diverse expression data enhances the discovery of biologically relevant gene modules.