CisModule: de novo discovery of cis-regulatory modules by hierarchical mixture modeling.
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.
Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138, USA.
Qing
Q
Zhou
Wing H
WH
Wong
D000465:Algorithms; D000818:Animals; D001665:Binding Sites / Q000235:genetics; D004247:DNA / Q000235:genetics / Q000378:metabolism; D030541:Databases, Genetic; D004330:Drosophila / Q000235:genetics / Q000378:metabolism; D005786:Gene Expression Regulation; D008957:Models, Genetic; D009132:Muscles / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism*
D014157:Transcription Factors; D004247:DNA