The human genome encodes 1500-2000 different transcription factors (TFs). 
ChIP-seq is revealing the global binding profiles of a fraction of TFs in a 
fraction of their biological contexts. These data show that the majority of TFs 
bind directly next to a large number of context-relevant target genes, that most 
binding is distal, and that binding is context specific. Because of the effort 
and cost involved, ChIP-seq is seldom used in search of novel TF function. Such 
exploration is instead done using expression perturbation and genetic screens. 
Here we propose a comprehensive computational framework for transcription factor 
function prediction. We curate 332 high-quality nonredundant TF binding motifs 
that represent all major DNA binding domains, and improve cross-species 
conserved binding site prediction to obtain 3.3 million conserved, mostly 
distal, binding site predictions. We combine these with 2.4 million facts about 
all human and mouse gene functions, in a novel statistical framework, in search 
of enrichments of particular motifs next to groups of target genes of particular 
functions. Rigorous parameter tuning and a harsh null are used to minimize false 
positives. Our novel PRISM (predicting regulatory information from single 
motifs) approach obtains 2543 TF function predictions in a large variety of 
contexts, at a false discovery rate of 16%. The predictions are highly enriched 
for validated TF roles, and 45 of 67 (67%) tested binding site regions in five 
different contexts act as enhancers in functionally matched cells.