ABSTRACT
Transcriptional control is an essential regulatory mechanism employed by bacteria. Much about transcriptional regulation remains to be discovered, even for the most widely studied bacterium, Escherichia coli. In the present study, we made a genome-wide low-order partial correlation analysis of E. coli microarray data with the purpose of recovering regulatory interactions from transcriptome data. As a result, we produced whole genome transcription factor regulation and co-regulation graphs using the predicted interactions, and we demonstrated how they can be used to investigate regulation and biological function. We concluded that partial correlation analysis can be employed as a method to predict putative regulatory interactions from expression data, as a complementary approach to transcription factor binding site tools and other tools designed to detect co-regulated genes.
Subject(s)
Escherichia coli/genetics , Genome, Bacterial/genetics , Oligonucleotide Array Sequence Analysis , Databases, Genetic , Gene Expression Regulation, Bacterial , Gene Regulatory Networks , Transcription Factors/metabolism , Transcription, GeneticABSTRACT
Transcriptional control is an essential regulatory mechanism employed by bacteria. Much about transcriptional regulation remains to be discovered, even for the most widely studied bacterium, Escherichia coli. In the present study, we made a genome-wide low-order partial correlation analysis of E. coli microarray data with the purpose of recovering regulatory interactions from transcriptome data. As a result, we produced whole genome transcription factor regulation and co-regulation graphs using the predicted interactions, and we demonstrated how they can be used to investigate regulation and biological function. We concluded that partial correlation analysis can be employed as a method to predict putative regulatory interactions from expression data, as a complementary approach to transcription factor binding site tools and other tools designed to detect co-regulated genes.