Integration of Heterogeneous Datasets for the Prediction of Directly Regulated Genes.

Abstract

Estrogen Receptor is a master transcriptional regulator in breast cancer and is an archetype of a molecular therapeutic target. Experiments have been performed to map ER binding sites on a genome-wide basis using various chromatin immunoprecipitation (ChIP) techniques. Lin and Vega (2007) applied the ChIP-PET strategy to map ER binding sites in MCF-7 cancer cells and found that only 5% of the ER binding sites were within the proximal gene promoter regions, while the majority were mapped further away from genes. In order to understand the ER impact on regulation, we integrated various datasets and explore the association between binding sites and regulated genes from the aspects of their distance, the binding strength and the concentration of the binding regions. We identified some important factors which contribute to the direct regulation and tentatively proposed a score function for genes to measure their potential to be directly regulated. The numerical results have been shown between control gene group and expressed gene group and are compared by the Receiver Operating Characteristic (ROC) curve analysis.