Han Liang, PhD
Assistant Professor, Bioinformatics and Computational Biology
University of Texas MD Anderson Cancer Center
Next-generation sequencing has become a revolutionary tool for cancer research. However, there is still a big gap between complete catalogues of genomic alternations identified from NGS studies and “actionable” shortlists for further functional investigation. Using my two recent studies (RNA-seq in gastric cancer and exome-seq in endometrial cancer), I will discuss how to identify “driver” genes underlying the tumorigenesis. In the first study, we performed a comprehensive analysis on the whole-transcriptome of gastric cancer, and developed a multilayer and integrative analytic framework for identifying potential therapeutic targets from RNA-seq data. In the second study, we developed an integrated systems-biology approach to identifying driver somatic mutations from whole-exome sequencing data, which combines bioinformatics prioritization, a high-throughput approach to generating mutants and high-through cell viability assays.
Dr. Liang received Ph.D. training from the Quantitative and Computational Biology program at Princeton University, which provides graduate education in the interface of biology, the physical sciences, and computational science. His PhD thesis is about RNA informatics on translation termination and alternative splicing. As a postdoctoral researcher, Dr. Liang completed three years of research on computational and evolutionary genomics at the University of Chicago, where his research was about microRNA regulation and gene duplication. Currently, Dr. Liang's research interests include the analysis of next-generation sequencing data, the integration of cancer genomics data, microRNA regulation, and the evolutionary process of tumor cells.