Biological systems respond to environmental perturbations and to a large
diversity of compounds through gene interactions, and these genetic factors
comprise complex networks. In particular, a wide variety of gene co-expression
networks have been constructed in recent years thanks to the dramatic increase
of experimental information obtained with techniques, such as microarrays and
RNA sequencing. These networks allow the identification of groups of
co-expressed genes that can function in the same process and, in turn, these
networks may be related to biological functions of industrial, medical and
academic interest. In this study, gene co-expression networks for 17 bacterial
organisms from the COLOMBOS database were analyzed via weighted gene
co-expression network analysis and clustered into modules of genes with similar
expression patterns for each species. These networks were analyzed to determine
relevant modules through a hypergeometric approach based on a set of
transcription factors and enzymes for each genome. The richest modules were
characterized using PFAM families and KEGG metabolic maps. Additionally, we
conducted a Gene Ontology analysis for enrichment of biological functions.
Finally, we identified modules that shared similarity through all the studied
organisms by using comparative genomics.
