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%). This indicates that for many functional categories, coexpression alone performs poorly to infer known biological gene functions. However, integration of cis-regulatory elements shows that 46% of the gene coexpression neighborhoods are enriched for one or more motifs, providing a valuable complementary source to functionally annotate genes. Through the integration of coexpression data, GO annotations, and a set of known cis-regulatory elements combined with a novel set of evolutionarily conserved plant motifs, we could link many genes and motifs to specific biological functions. Application of our coexpression framework extended with cis-regulatory element analysis on transcriptome data from the cell cycle-related transcription factor OBP1 yielded several coexpressed modules associated with specific cis-regulatory elements. Moreover, our analysis strongly suggests a feed-forward regulatory interaction between OBP1 and the E2F pathway. The ATCOECIS resource (http://bioinformatics.psb.ugent.be/ATCOECIS/) makes it possible to query coexpression data and GO and cis-regulatory element annotations and to submit user-defined gene sets for motif analysis, providing an access point to unravel the regulatory code underlying transcriptional control in Arabidopsis (Arabidopsis thaliana).