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Genome-wide experiments to map the DNA-binding locations oftranscription-associated factors (TFs) have shown that the number of genesbound by a TF far exceeds the number of possible direct target genes.Distinguishing functional from non-functional binding is therefore a majorchallenge in the study of transcriptional regulation. We hypothesized thatfunctional targets can be discovered by correlating binding and expressionprofiles across multiple experimental conditions. To test this hypothesis, weobtained ChIP-seq and RNA-seq data from matching cell types from the humanENCODE resource, considered promoter-proximal and distal cumulative regulatorymodels to map binding sites to genes, and used a combination of linear andnon-linear measures to correlate binding and expression data. We found that ahigh degree of correlation between a gene's TF-binding and expression profileswas significantly more predictive of the gene being differentially expressedupon knockdown of that TF, compared to using binding sites in the cell type ofinterest only. Remarkably, TF targets predicted from correlation across acompendium of cell types were also predictive of functional targets in othercell types. Finally, correlation across a time course of ChIP-seq and RNA-seqexperiments was also predictive of functional TF targets in that tissue.

Functional transcription factor target discovery via compendia of binding and expression profiles