Gene expression profiles of 400 siRNA knocked down on HUVEC

Gene regulatory networks inferred from RNA abundance data have generated significant interest. Several high-profile studies have suggested that gene networks may be useful for identifying molecular pathways related to development, human disease and drug action. However despite, this potential, gene network approaches are used relatively infrequently and their use generally requires input from specialist bioinformaticians. To make their use and evaluation more accessible to non-specialist bioinformaticians and to laboratory scientists, we have assembled a comprehensive but easily used suite of tools for the generation and evaluation of gene regulatory networks, applicable to both microarray and RNA-seq data. We illustrate the use of these tools with two previously unpublished microarray datasets generated in primary human endothelial cells by 400 siRNA-mediated knockdowns. Using these tools and data sets, we reverse-engineered a range of different types of transcriptional regulatory network model, and compared these networks with known molecular pathways reported in the literature. In this study, we generated microarray data from human umbilical vein endothelial cells (HUVECs) transfected with 400 siRNAs and infered gene networks from them by using five different methods.