Regulation and functional roles revealed by clustering of microarray expression data of Escherichia coli genes.

Abstract

Microarray data can be used to identify co-expressed genes that may play a role in the same biological process. An enormous amount of gene expression data is currently available online in repositories for several organisms. Therefore, the analysis and interpretation of this information could help us organize, make sense and notice the knowledge that it contains which represents a major challenge in the postgenomic era. Here, we grouped genes of Escherichia coli K-12 via expression data to infer meaningful transcriptional regulatory information, namely functionally relevant clusters, which were validated with curated transcriptional regulatory information in RegulonDB. Our method is based on the assumption that co-expressed genes reflect functional units provided by their genetic structure, i.e. the arrangement of the genes, their regulation, and their participation in defined biological processes. We showed that co-expressed genes are involved in the same metabolic pathways and type of regulation (through transcription factors, σ-factors, allosteric regulation or microRNA regulation) and are helpful for identifying novel transcriptional regulatory interactions. .