Dong Xu

Dong Xu

Chair, Associate Professor
Department of Computer Science, Bioinformatics

Web site: Digital Biology Laboratory
Office address: 201 Engineering Building West, University of Missouri
Office phone: (573) 882-7064
Fax: (573) 882-8318

Research Interest

Protein structure prediction, high-throughput biological data analyses, computational proteomics, in silico studies of plant, microbes, and neural systems.


The research focus of Digital Biology Laboratory (DBL) is Bioinformatics and Computational Biology. We are interested in various topics including protein structure prediction, high-throughput biological data analyses, computational proteomics, and in silico studies of plant and microbes.

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 (e.g. 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 Rhizobium and Synechococcus), viruses (especially SARS and flu virus), and yeast (Saccharomyces cerevisia).

Selected Publications