Protein Structure Prediction and Modeling: We are interested in developing effective computational methods for protein structure prediction and modeling. Our research in this area includes protein structure comparison, protein secondary structure prediction, protein fold recognition (threading), mini-threading, NMR protein structure determination, and structure-based function prediction.


High-throughput Biological Data Analyses: We are interested in developing novel computational techniques for analyzing large-scale biological data, including genomic sequence, gene expression, protein-protein interaction, sub-cellular localization, and phenotypic data. The analyses are used for experimental design (eg. microarray primer design) and predictions of gene function and biological pathway.


Computational Proteomics: We are interested in developing new computational methods for protein identification through analyzing mass spectrometry data, including mass fingerprinting and MS/MS data.


Application of Bioinformatics Methods in Biological Systems: We are interested in applying various computational methods/tools and available experimental data to study the evolution, protein structure and function, gene regulation and biological pathway through collaboration with experimentalists. Our main target systems are plants (especially Arabidopsis and soybean), bacteria (especially Synechococcus), viruses (especially SARS), yeast (Saccharomyces cerevisia), and neural systems.

Research has been supported by DOE, NSF, USDA, NIH, US Army, United Soybean Board, Missouri Soybean Merchandising Council, Missouri Life Science Trust Fund MU start-up fund, Monsanto Research Fund and National Center for Soybean Biotechnology.