My research mainly focuses on developing and applying computational and statistical approaches to integrate and interpret large-scale biological “omics” data, with the goal of identifying disease mechanisms, biomarkers and treatment targets.
I focus on 1) developing computational methods to systematically and accurately characterize the genomics and the transcriptomics of cancer using high throughput sequencing technology, and developing integrative approaches that help understand the etiology of cancer. Translate of genomics into therapeutics and diagnostics reinforce its potential for personalizing medicine.
2) computational analysis from genomic sequences to other post-genomic data, including both DNA and RNA sequences, protein profiling, and epigenetic profiling, in an ongoing effort to find hidden treasures. With the development of next-generation sequencing, our understanding has been advanced through the use of a variety of platforms: methy-seq, ChIP-seq, exome-seq and RNA-seq. The large amount of publicly available next-generation sequencing data, such as datasets from TCGA and ENCODE, has created enormous opportunities for researchers to conduct genomic analysis beyond the traditional sequencing analysis. Transforming genomic information into biomedical and biological knowledge requires creative and innovative computational methods for all aspects of genomics.