Teaching Interests

BSC478/L/578/L Molecular Biology Laboratory
BSC492/692 Introduction to Bioinformatics

Research Interests

My main research interests are bioinformatics, computational biology, functional genomics, diabetes and cancer. My current and future research is focused on developing new tools for high throughput data analysis, comparative genomics, designing biology databases, and studying new targeting molecules and their cellular signal transduction pathways for fighting cancer and diabetes.

1. High throughput data analysis

In the 21st century, biology has become the land of the “-omics” including genomics, transcriptomics, proteomics, lipidomics, metabolomics, etc. Each of these “-omics” generates a huge amount of high throughput data. My group is interested in analyzing and understanding these data using existing methods and developing new algorithms or tools. For instance, we can try to understand a protein family function or find new cancer makers by joint analyzing different data sources. We are trying to develop new algorithms or tools for microarray data clustering, marker gene identification, gene network, proteomics and lipidomics peak data processing and transformation etc.

2. Large scale genome annotation and comparative genomics

So far, there are 166 published completed genomes, 360 eukaryotic ongoing genomes and 415 prokaryotic ongoing genomes. Most of the genomes are not well annotated. Currently we are involved in the annotation of Tribolium genome. The red flour beetle Tribolium castaneum is a highly sophisticated and easily manipulated genetic model organism. Studying Tribolium will help to interpret human gene function when comparisons with the fruit fly (Drosophila) fail to do so. We are building a system for automated sequence annotation of the Tribolium genome by combining the results of sequence alignments and gene prediction programs. We will also do manual checking to make sure the annotation is correct. Comparative genomics and evolution study between Tribolium and other organisms will be studied. We are also interested in developing pipeline for large scale EST sequence retrieval, assembly, annotation, function and pathway assignment for a specific organism.

3. Biology database development

Biology information is exploding, it is important to build biology databases to manage biology data. We are currently working on a couple of databases, and one is BeetleBase: an online Tribolium genome database The long-term goals for BeetleBase are: 1) to develop a web community resource of Tribolium genetic, genomic, proteomic, pheonotypic, and developmental information; 2) to provide service to the scientific community. The other one is lipidomics information management system. Lipidomics is a very new high throughput technology. The online lipidomics information management system is to systematically track, disseminate and mine lipidomics experimental data.

4. Cancer metabolic profiling

Using gene expression and proteomics profiling to find new targets for cancer is an extremely hot area. But using metabolomics strategy to work on cancer is almost not started. It is certainly true that the metabolite products between cancer and normal tissues are different. My group is interested in finding new cancer makers or drugs to treat cancer using experimental metabolic profiling.

5. Role of PSM/SH2-B and its family members in cancer and diabetes

PSM/SH2-B is an adaptor protein which has been found to bind to many receptors such as the receptors of insulin, IGF-I, PDGF, NGF, FGF and HGF. Our early work has demonstrated it is involved in tumorigenesis, apoptosis and insulin related diabetes. Our continued work will focus on figuring out the signaling transduction pathway of PSM/SH2-B and its family members such as Lnk, APS etc. in cancer and diabetes using microarray, proteomics and metabolomics strategy.

Current Graduate Students | Lab Site

Representative Publications

Jiang, H., Deng, Y., Chen, H.S., Tao, L., Sha, Q., Chen, J., Tsai, C.J. and Zhang, S. Joint analysis of multiple microarray gene expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics, 2004, 5(1):172.

Zhu, Q.S., Deng, Y., Vanka, P., Brown, S.J., Muthukrishnan, S. and Kramer, K.J. Computational identification of novel chitinase-like proteins in the Drosophila melanogaster genome. Bioinformatics, 2004, 20:161-169.

Deng, Y., Bhattacharya, S., Ramaswamy, O.R., Tandon, R., Wang, Y., Janda, R. and Riedel H. Growth factor receptor-binding protein 10 (Grb10) as a partner of phosphatidylinositol 3-kinase in metabolic insulin action. J. Biol. Chem., 2003, 278: 39311-39322.

Yousaf, N., Deng, Y., Kang, Y.H. and Riedel, H. Four PSM/SH2-B alternative splice variants and their differential roles in mitogenesis. J. Biol. Chem., 2001, 276: 40940-40948.

Wang, J., Dai, H., Yousaf, N., Mousaif, M., Deng, Y., Boufelliga, A. and Swamy, O. R. and Riedel, H. Grb10, a positive, stimulatory signaling adapter in platelet- derived growth factor BB-, Insulin-like growth factor I-, and Insulin-mediated mitogenesis. Mole. Cell. Biol., 1999, 19: 6217-6228.