We develop statistical and computational methodologies for genomic data analysis and integration, with the aim to understand systems-level gene regulatory mechanisms. More specifically, our research has been focused on epigenomics. A multi-cellular organism contains only one genome, but different cell types contain different epigenomic patterns: chromatin structure, histone modification, and DNA methylations. These epigenomic marks are important for regulating protein-DNA binding activities and gene transcription. We have developed algorithms to analyze tiling array data, thereby probing genome-scale chromatin structure at high resolutions. We also integrate epigenomic information with other data such as genotypic information and RNA expression in order to estimate the direct regulatory effects of epigenetic factors. We are currently investigating the role of epigenetic effects in cancer cells.
Another area of interest is to build predictive models for important biological pathways. We study how cell behavior is changed under perturbation of regulatory networks, with particular interest in studying how phenotypic diversity can arise in genomically identical cell populations.