Background: In many important crops genomic studies are generating large amounts of data
from cDNA sequencing and RNA expression experiments. Genomic data is complementing the efforts
at improving production of new plant varieties with resistance to major worldwide biotic problems,
facing the climate change challenge and pursuing the quest for better quality. After the initial
exploratory phase of genome sequencing and functional characterization of genes of interest, a postgenomics
phase is pointing towards the understanding of the organism function as a whole, through
Objective: To develop a Software Architecture that facilitates Gene Networks inference from highthroughput
gene expression data collected from microarray experiments.
Method: A pipeline architecture was designed and constructed for data mining that was validated using
known pathways for starch and sucrose metabolism in plants.
Results: The pipeline provides the support for functional annotations of both putative homologs and
new genes, allowing as well the identification of novel co-expressed gene clusters related to metabolic
Conclusion: Our approach can be transferred between organisms, taking advantage of the open and
adaptable platform in R language, and visualization of gene expression networks that can be easily
incorporated for web access.