CisModule: De novo discovery of cis-regulatory modules by hierarchical mixture modeling Export

@article{citeulike:2064285, abstract = {10.1073/pnas.0402858101 The regulatory information for a eukaryotic gene is encoded in cis-regulatory modules. The binding sites for a set of interacting transcription factors have the tendency to colocalize to the same modules. Current motif discovery methods do not take advantage of this knowledge. We propose a hierarchical mixture approach to model the cis-regulatory module structure. Based on the model, a new motif-module discovery algorithm, CisModule, is developed for the Bayesian inference of module locations and within-module motif sites. Dynamic programming-like recursions are developed to reduce the computational complexity from exponential to linear in sequence length. By using both simulated and real data sets, we demonstrate that CisModule is not only accurate in predicting modules but also more sensitive in detecting motif patterns and binding sites than standard motif discovery methods are.}, address = {Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138, USA.}, author = {Zhou, Qing and Wong, Wing H.}, citeulike-article-id = {2064285}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0402858101}, citeulike-linkout-1 = {http://www.pnas.org/content/101/33/12114.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/101/33/12114.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/15297614}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=15297614}, day = {17}, doi = {10.1073/pnas.0402858101}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {bioinfo, crm, hmm, tf, tool}, month = {August}, number = {33}, pages = {12114--12119}, posted-at = {2009-10-30 14:00:29}, priority = {2}, title = {CisModule: De novo discovery of cis-regulatory modules by hierarchical mixture modeling}, url = {http://dx.doi.org/10.1073/pnas.0402858101}, volume = {101}, year = {2004} }

TY - JOUR ID - citeulike:2064285 L3 - citeulike-article-id:2064285 N2 - 10.1073/pnas.0402858101 The regulatory information for a eukaryotic gene is encoded in cis-regulatory modules. The binding sites for a set of interacting transcription factors have the tendency to colocalize to the same modules. Current motif discovery methods do not take advantage of this knowledge. We propose a hierarchical mixture approach to model the cis-regulatory module structure. Based on the model, a new motif-module discovery algorithm, CisModule, is developed for the Bayesian inference of module locations and within-module motif sites. Dynamic programming-like recursions are developed to reduce the computational complexity from exponential to linear in sequence length. By using both simulated and real data sets, we demonstrate that CisModule is not only accurate in predicting modules but also more sensitive in detecting motif patterns and binding sites than standard motif discovery methods are. IS - 33 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 0027-8424 AD - Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138, USA. EP - 12119 TI - CisModule: De novo discovery of cis-regulatory modules by hierarchical mixture modeling VL - 101 SP - 12114 KW - bioinfo KW - crm KW - hmm KW - tf KW - tool AU - Zhou, Qing AU - Wong, Wing PY - 2004/08/17/ UR - http://dx.doi.org/10.1073/pnas.0402858101 ER -