To understand the transcriptional regulatory network in eukaryotic cells, it is 
essential to identify functional cis-regulatory sequences that interact with 
trans-acting factors. A number of algorithms have been developed to predict 
common cis-regulatory elements for co-regulated genes with similar expression 
patterns. However, previous methods usually deal with disjoint gene groups 
partitioned or clustered by arbitrary cutoffs, which might cause information 
losses. To preclude the defining step of gene set, we adopted enrichment 
analysis and termed the method binding site enrichment analysis (BSEA). BSEA was 
first applied for publicly available ChIP-on-chip data of c-MYC, MAX and E2F 
transcription factors, identifying significant enrichment for signatures of 
corresponding factors and potential co-activators. Using time-scaled expression 
profiling of 3T3-L1 adipogenesis, we observed enrichment for signatures of known 
adipogenic factors such as C/EBPalpha, C/EBPbeta and PPARgamma, temporally 
coincident with previous reports. BSEA was also applied to tissue-specific 
expression profiles of human and mouse, identifying well-known tissue-specific 
transcription factors such as HNF-4 in liver and MEF-2 in heart along with other 
putative tissue-specific regulators. With extended versatility coping with 
various kinds of microarray dataset, BSEA can identify key regulators for global 
microarray data in which transcriptional regulation plays a major role. As a 
generalized method, BSEA would help to elucidate the transcriptional regulatory 
networks, the primary challenges in functional genomics.