Dr. Han Liang
The University of Texas MD Anderson Cancer Center
Department of Bioinformatics and Computational Biology
- Next-generation sequencing
- Integration of cancer genomic data
- MicroRNA regulation
- Evolutionary process of human tumors
The overall goal of my research is to understand the genetic and molecular mechanisms of human cancers through the analysis and interpretation of high-throughput genomic data. We are also interested in developing novel computational methods and bioinformatic tools for such a purpose. In particular, we focus on integrative analysis of cancer genomic data and analysis of next-generation sequencing data. Other active research topics include microRNA regulation, network-based biology and comparative analysis of mammalian genomes.
The biomarkers for cancer are conventionally based on individual genes, and this practice often makes it hard to interpret the underlying mechanism. The availability of various biological networks, such as gene regulatory and protein interaction networks, has allowed us to use sub-network as biomarkers.
Somatic copy-number alterations (SCNAs) play a crucial role in the development of human cancers. Taking advantage of recently available SCNA data in many cancer types, we wonder what molecular and evolutionary mechanisms underlie the global SCNAs patterns in various cancer types and what genetic and epigenetic elements most correlate with SCNA occurrence.
Alternative splicing is a crucial regulatory mechanism for producing different protein products from the same gene locus, which is largely controlled by splicing sites. However, little is known about the adaptivity and plasticity of splicing regulation during the evolution of modern human populations. The goal of this project is to discover the splicing genes with population-specific adaptation through analyzing the 1000 Genome Project data.
Liang H*, Cheung LWT, Li J, Ju Z, Yu S, Stemke-Hale K, Dogruluk T, Lu Y, Liu X, Gu C, Guo W, Scherer SE, Carter H, Westin SN, Dyer MD, Verhaak RGW, Zhang F, Karchin R, Liu GC, Lu KH, Broaddus RR, Scott KL, Hennessy BT, Mills FB. Whole-exome Sequencing Combined with Functional Genomics Reveals Novel Candidate Driver Cancer Genes in Endometrial Cancer. Genome Res (in press) (*corresponding author)
Li J, Roebuck P, Grunewald S, Liang H. SurvNet: a web server for identifying network-based biomarkers that most correlate with patient survival data. Nucleic Acids Res, 40 (W1): W123-126 (2012). PubMed
Kim YH*, Liang H*, Liu X, Lee JS, Cho JY, Cheong JH, Kim H, Li M, Downey TJ, Dyer MD, Sun Y, Sun J, Beasley EM, Chung HC, Noh SH, Weinstein JN, Liu CG, Powis G. AMPKα Modulation in Cancer Progression: Multilayer Integrative Analysis of the Whole Transcriptome in Asian Gastric Cancer. Cancer Res, 72(10):2512-21 (2012). PubMed (*co-corresponding author)
Yuan Y, Xu Y, Xu J, Ball RL, Liang H. Predicting the lethal phenotype of the knockout mouse by integrating comprehensive genomic data. Bioinformatics, 28(9):1246-52 (2012). PubMed
Ji Y, Xu Y, Zhang Q, Tsui KW, Yuan Y, Norris C Jr, Liang S, Liang H. BM-Map: Bayesian mapping of multireads for next-generation sequencing data. Biometrics, 67(4):1215-24 (2011). PubMed