Transcription regulatory networks in Caenorhabditis elegans inferred through reverse-engineering of gene expression profiles constitute biological hypotheses for metazoan development.

Differential gene expression governs the development, function and pathology of multicellular organisms. Transcription regulatory networks study differential gene expression at a systems level by mapping the interactions between regulatory proteins and target genes. While microarray transcription profiles are the most abundant data for gene expression, it remains challenging to correctly infer the underlying transcription regulatory networks.

Unraveling transcriptional control in Arabidopsis using cis-regulatory elements and coexpression networks.

Analysis of gene expression data generated by high-throughput microarray transcript profiling experiments has demonstrated that genes with an overall similar expression pattern are often enriched for similar functions. This guilt-by-association principle can be applied to define modular gene programs, identify cis-regulatory elements, or predict gene functions for unknown genes based on their coexpression neighborhood. We evaluated the potential to use Gene Ontology (GO) enrichment of a gene's coexpression neighborhood as a tool to predict its function but found overall low sensitivity scores (13%-34%).