Assistant Professor of Genetics and Biochemistry
Ph.D., Molecular Biology, 1999, University of Georgia
M.S., Computer Science, 2001, Mississippi State University

Research Interests
Biological Databases
Machine Learning and Data Mining
Molecular Recognition
Gene Function and Regulation

Email: liangjw@clemson.edu
Office: Greenwood Genetic Center
Phone: (864) 941-8120

RESEARCH ACTIVITIES

Biological Databases and Data Integration

High-throughput experiments generate large datasets, from which useful information may be extracted for understanding biological systems.  The scale and complexity of the datasets give rise to substantial challenges in data management and integration.  We have developed a model organism database called BeetleBase, and the BioStar framework for data integration in biomedical warehouses.  In the future, we will develop a comprehensive data warehouse for understanding human genetic diseases.

Machine Learning Applications in Bioinformatics

Machine learning is particularly appealing for knowledge discovery in biological data.  For many biological problems, although a number of experimental observations have been made, the underlying mechanisms remain unclear.  It is thus highly desired that machine learning techniques can be used to model the complex patterns hidden in the available data.  We have developed a machine learning approach for prediction of DNA and RNA-binding residues directly from amino acid sequences.  A web server called BindN has been constructed for online predictions.  As shown in the diagram, predictions made by BindN can provide useful information for understanding protein-nucleic acid interactions.  Recent research focuses on improving prediction accuracy through the use of evolutionary information and by applying different learning algorithms.  We are also developing machine learning approaches for several other biological problems.

Computational Molecular Biology

We develop and use computational methods to understand gene function and regulation.  Our laboratory is located at the Greenwood Genetic Center, and in collaboration with the other research groups, we are interested in phylogenetic analysis of the genes related to genetic diseases and microarray data analysis for understanding gene regulatory networks.  We are also interested in computational identification and analysis of novel transcription factors.

Putative DNA-binding residues are predicted by BindN for the mouse CREB bZIP domain (PDB: 1DH3).  Predictions are based solely on amino acid sequence information.  In this diagram, true positive predictions are indicated by red spacefill and true negatives by green wireframe, whereas false negative and false positive predictions are depicted by blue and yellow spacefill, respectively.

Wang, L., Wang, S., Li, Y., Paradesi, M.S.R. and Brown, S.J. (2007) BeetleBase: the model organism database for Tribolium castaneum. Nucleic Acids Research, 35:D476-D479 (Database issue).

Wang, L.* and Brown, S.J. (2006) Prediction of DNA-binding residues from sequence features. Journal of Bioinformatics and Computational Biology, 4:1141-1158.

Wang, L.* and Brown, S.J. (2006) BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. Nucleic Acids Research, 34:W243-W248 (including the cover figure of the Web Server issue).

Wang, L.* and Roossinck, M.J. (2006) Comparative analysis of expressed sequences reveals a conserved pattern of optimal codon usage in plants. Plant Molecular Biology, 61:699-710.

Zhang, Y. and Wang, L. (2005) The WRKY transcription factor superfamily: its origin in eukaryotes and expansion in plants. BMC Evolutionary Biology, 5:1.

Wang, L., Zhang, A. and Ramanathan, M. (2005) BioStar models of clinical and genomic data for biomedical data warehouse design. International Journal of Bioinformatics Research and Applications (IJBRA), 1:63-80.

Duran, A.L., Yang, J., Wang, L. and Sumner, L.W. (2003) Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). Bioinformatics, 19:2283-2293.