Regulatory element discovery using tree-structured models

Computational discovery of transcriptional regulatory regions in DNA sequences provides an efficient way to broaden our understanding of how cellular processes are controlled. We formulate the regulatory element discovery problem in the regression framework with regulatory regions treated as predictor variables and gene expression levels as responses. We use regression tree models to identify structural relationships between predictors and responses. The regression tree methodology is extended to handle multiple responses from different experiments by modifying the split function. We apply this method to two data sets of the yeast Saccharomyces cerevisiae. The method successfully identifies most of regulatory motifs that are known to control gene transcription under the given experimental conditions. Our method also suggests several putative motifs that present novel regulatory motifs.