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Construction of citrus gene coexpression networks from microarray data using random matrix theory.

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Gene coexpression networks were built for sweet orange (Citrus sinensis) to aid gene function annotation. This analysis identified functional modules and potential gene targets for citrus research.

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Area of Science:

  • Plant genomics
  • Bioinformatics
  • Functional genomics

Background:

  • Citrus genome sequencing presents challenges for gene function annotation.
  • Gene coexpression analysis offers a method for functional annotation using microarray data.

Purpose of the Study:

  • To construct and analyze gene coexpression networks in sweet orange (Citrus sinensis).
  • To identify functional modules and potential gene targets for citrus research.

Main Methods:

  • Utilized 230 sweet orange microarrays to build seven coexpression networks (condition-independent and dependent).
  • Applied the Markov Cluster Algorithm to partition networks into functional modules.
  • Enriched Gene Ontology (GO) and KEGG pathway terms were identified in modules.
  • Verified networks using independent expression data.

Main Results:

  • Developed seven coexpression networks with 37,633 edges and 6,256 nodes, covering over 52% of measurable citrus genes.
  • Identified 343 significantly enriched GO biological process terms and 60 KEGG pathway terms across functional modules.
  • Networks demonstrated reliability through independent verification.

Conclusions:

  • The constructed coexpression networks provide valuable insights into citrus gene function.
  • This study offers novel targets for future functional analyses in citrus species.