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Structural and Computational Biology and Molecular Biophysics

Houston, Texas

A BCM research lab.
Structural and Computational Biology & Molecular Biophysics
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Han Liang, Ph.D.

Assistant Professor,Dr. Han Liang Dept. of Bioinformatics and Computational Biology

The University of Texas M.D. Anderson Cancer Center

Education:

Ph.D., Princeton University
Postdoc, The University of Chicago

Research Interest:

  • Next-generation sequencing
  • Integration of cancer genomic data
  • MicroRNA regulation in cancer
  • Evolutionary process of human tumors

Description

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 the integrative analysis of cancer genomic data and the analysis of next-generation sequencing data. Other active research topics include microRNA regulation, network-based biology and comparative analysis of mammalian genomes.

Project I

The biomarkers for cancer are conventionally based on individual genes and this practice oftem 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. The goal of this project is to develop computational methods for identification of network-based biomarkers.

Project II

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 the molecular and evolutionary mechanisms underlie the global SCNAs patterns in various cancer types and what genetic and epigenetic elements most correlate with SCNA occurrence.

Project III

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.

Selected Publications

  • Li J, Lu Y, Akbani R, Ju Z, Roebuck PL, Liu W, Yang JY, Broom BM, Verhaak RGW, Kane DW, Wakefield C, Weinstein JN, Mills GB and Liang H. TCPA: A Resource for Cancer Functional Proteomics Data. Nature Methods, in press (2013).
  • Omberg L, Ellrott K, Yuan Y, Kandoth C, Wong C, Friend S, Stuart J, Liang H and Margolin AA. Enabling Transparent and Collaborative Analysis of 12 tumor types within The Cancer Genome Atlas. Nature Genetics, in press (2013).
  • Li Y, Zhang L, Ball RL, Liang X, Li J and Liang H. Comparative Analysis on Somatic Copy-Number Alterations Across Different Types of Human Cancer Reveals Two Distinct Classes of Breakpoint Hotspots. Human Molecular Genetics, 21(22):4957-65 (2012). PubMed
  • 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 and Mills GB. Whole-exome Sequencing Combined with Functional Genomics Reveals Novel Candidate Driver Cancer Genes in Endometrial Cancer. Genome Research, 22(11):2120-29 (2012). PubMed
  • Li J, Roebuck P, Grünewald S and Liang H. SurvNet: a Web Server for Identifying Network-based Biomarkers that Most Correlate with Patient Survival Data. Nucleic Acids Research, 40(Web Server Issue):W123-126 (2012). PubMed
  • Kim YU, Liang H, Liu X, Lee J, Cho JY, Cheong JH, Kim H, Li M, Downey TJ, Sun Y, Sun J, Dyer MD, Beasley EM, Noh SH, Weinstein JN, Liu CG and Powis G. AMPKα Modulation in Cancer Progression: Multilayer Integrative Transcriptome Analysis in Asian Gastric Cancer. Cancer Research, 72(10):2512-21 (2012). PubMed
  • Yuan Y, Xu Y, Xu J, Ball RL and Liang H. Predicting Lethal Phenotype of Knockout Mouse by Integrating Comprehensive Genomic Data. Bioinformatics, 28(9):1246-1252 (2012). PubMed

For more publications, see listing in PubMed.

Contact:

Department: Department of Bioinformatics and Computational Biology

Division of Quantitative Sciences

Address: The University of Texas MD Anderson Cancer Center
1400 Pressler Street
Unit Number 1410
Houston, TX 77030

Telephone: 713-745-9815

Fax: 713-563-4242
E-mail: hliang1@mdanderson.org

Website: http://odin.mdacc.tmc.edu/~hliang1/

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