Using nuclear factor-κB (NF-κB) ChIP-Seq data, we present a framework for 
iterative learning of regulatory networks. For every possible transcription 
factor-binding site (TFBS)-putatively regulated gene pair, the relative distance 
and orientation are calculated to learn which TFBSs are most likely to regulate 
a given gene. Weighted TFBS contributions to putative gene regulation are 
integrated to derive an NF-κB gene network. A de novo motif enrichment analysis 
uncovers secondary TFBSs (AP1, SP1) at characteristic distances from NF-κB/RelA 
TFBSs. Comparison with experimental ENCODE ChIP-Seq data indicates that 
experimental TFBSs highly correlate with predicted sites. We observe that 
RelA-SP1-enriched promoters have distinct expression profiles from that of 
RelA-AP1 and are enriched in introns, CpG islands and DNase accessible sites. 
Sixteen novel NF-κB/RelA-regulated genes and TFBSs were experimentally 
validated, including TANK, a negative feedback gene whose expression is 
NF-κB/RelA dependent and requires a functional interaction with the AP1 TFBSs. 
Our probabilistic method yields more accurate NF-κB/RelA-regulated networks than 
a traditional, distance-based approach, confirmed by both analysis of gene 
expression and increased informativity of Genome Ontology annotations. Our 
analysis provides new insights into how co-occurring TFBSs and local chromatin 
context orchestrate activation of NF-κB/RelA sub-pathways differing in 
biological function and temporal expression patterns.