Identification of transcriptional regulators using binding site enrichment analysis.

@article{citeulike:2192591, abstract = {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.}, address = {Division of Metabolic Disease, Center for Biomedical Science, National Institute of Health, Nokbun-dong 5, Eunpyung-gu, Seoul 122-701, South Korea.}, author = {Kim, T. M. and Jung, M. H. }, citeulike-article-id = {2192591}, issn = {1386-6338}, journal = {In Silico Biol}, keywords = {transcription\_factor}, number = {6}, pages = {531--544}, posted-at = {2008-05-15 16:03:14}, priority = {4}, title = {Identification of transcriptional regulators using binding site enrichment analysis.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/17518763}, volume = {6}, year = {2006} }

TY - JOUR ID - citeulike:2192591 L3 - citeulike-article-id:2192591 TI - Identification of transcriptional regulators using binding site enrichment analysis. JF - In Silico Biol VL - 6 IS - 6 SP - 531 EP - 544 AD - Division of Metabolic Disease, Center for Biomedical Science, National Institute of Health, Nokbun-dong 5, Eunpyung-gu, Seoul 122-701, South Korea. SN - 1386-6338 N2 - 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. KW - transcription_factor AU - Kim, TM AU - Jung, MH PY - 2006/// UR - http://view.ncbi.nlm.nih.gov/pubmed/17518763 ER -