Genome-wide partial correlation analysis of Escherichia coli microarray data

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.

Gene networks as a tool to understand transcriptional regulation

Gene regulatory networks, or simply gene networks (GNs), have shown to be a promising approach that the bioinformatics community has been developing for studying regulatory mechanisms in biological systems. GNs are built from the genome-wide high-throughput gene expression data that are often available from DNA microarray experiments. Conceptually, GNs are (un)directed graphs, where the nodes correspond to the genes and a link between a pair of genes denotes a regulatory interaction that occurs at transcriptional level. In the present study, we had two objectives: 1) to develop a framework for GN reconstruction based on a Bayesian network model that captures direct interactions between genes through nonparametric regression with B-splines, and 2) to demonstrate the potential of GNs in the analysis of expression data of a real biological system, the yeast pheromone response pathway. Our framework also included a number of search schemes to learn the network. We present an intuitive notion of GN theory as well as the detailed mathematical foundations of the model. A comprehensive analysis of the consistency of the model when tested with biological data was done through the analysis of the GNs inferred for the yeast pheromone pathway. Our results agree fairly well with what was expected based on the literature, and we developed some hypotheses about this system. Using this analysis, we intended to provide a guide on how GNs can be effectively used to study transcriptional regulation. We also discussed the limitations of GNs and the future direction of network analysis for genomic data. The software is available upon request.