CisModule: de novo discovery of cis-regulatory modules by hierarchical mixture modeling.

@article{citeulike:2064285, abstract = {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 de novo 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 de novo 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, Q. and Wong, W. H. }, citeulike-article-id = {2064285}, doi = {http://dx.doi.org/10.1073/pnas.0402858101}, issn = {0027-8424}, journal = {Proc Natl Acad Sci U S A}, keywords = {ab\_initio, cis\_regulatory\_elements, clustering, method, prediction}, month = {August}, number = {33}, pages = {12114--12119}, posted-at = {2007-12-05 22:46:33}, priority = {0}, 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 TI - CisModule: de novo discovery of cis-regulatory modules by hierarchical mixture modeling. JF - Proc Natl Acad Sci U S A VL - 101 IS - 33 SP - 12114 EP - 12119 AD - Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138, USA. SN - 0027-8424 N2 - 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 de novo 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 de novo 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. KW - ab_initio KW - cis_regulatory_elements KW - clustering KW - method KW - prediction AU - Zhou, Q AU - Wong, WH PY - 2004/08/17/ UR - http://dx.doi.org/10.1073/pnas.0402858101 ER -