Digital Biology Laboratory (DBL) is a research and education powerhouse in bioinformatics and computational biology. DBL works on development of novel computational methods, algorithms, software and information systems, as well as on broad applications of these tools and other informatics resources for various biological and medical problems. In the area of protein structure prediction and modeling, DBL develops effective computational methods for protein structure prediction and modeling, especially MOFOLD system for protein tertiary structure prediction. For high-throughput biological data analyses, DBL develops new computational techniques for analyzing large-scale biological data, including genomic sequence, gene expression, protein-protein interaction, epigenomic data, proteomic data, and phenotypic data. A number of software tools have been developed, such as a high-throughput primer/probe design tool (Primegens), a Bayesian partition tool for genotype-phenotype epistatic relationships (BHIT), and predictions of protein post-translations modifications (Musite). Several popular information systems are also developed, including SoyKB, a knowledge base for soybean translational genomics and molecular breeding, and P3DB, a plant protein phosphorylation database. DBL applies many computational methods/tools and available experimental data in next-generation sequencing analysis, protein structure prediction and modeling, gene function annotation, gene regulation study, and biological pathway analysis. DBL has collaborated with dozens of experimental labs. Its bioinformatics applications cover plants (especially soybeans), cancers, heart diseases, viruses, and bacteria.
Research at DBL has been supported by NIH, NSF, DOE, USDA, US Army, United Soybean Board, Missouri Soybean Merchandising Council, Missouri Life Science Trust Fund, Monsanto Research Fund, Cerner, and National Center for Soybean Biotechnology.

Recent Publication Highlight

  1. Chengli Xu, Xiangwu Ju, Dandan Song, Fengming Huang, Depei Tang, Zhen Zou, Chao Zhang, Trupti Joshi, Lijuan Jia, Weihai Xu, Kai-Feng Xu, Qian Wang, Yanlei Xiong, Zhenmin Guo, Xiangmei Chen, Fumin Huang, Jiantao Xu, Ying Zhong, Yi Zhu, Yi Peng, Li Wang, Xinyu Zhang, Rui Jiang, Dangsheng Li, Tao Jiang, Dong Xu, Chengyu Jiang. An association analysis between psychophysical characteristics and genome-wide gene expression changes in human adaptation to the extreme climate at the Antarctic Dome Argus. Molecular Psychiatry, in press.
  2. Zhou Li, Yingfeng Wang, Qiuming Yao, Nicholas B. Justice, Tae-Hyuk Ahn, Dong Xu, Robert L. Hettich, Jillian F. Banfield, Chongle Pan. Diverse and divergent protein post-translational modifications in two growth stages of a natural microbial community. Nature Communications. 5:4405. 2014.
  3. Trupti Joshi, Michael R. Fitzpatrick, Shiyuan Chen, Yang Liu, Hongxin Zhang, Ryan Z. Endacott, Eric C. Gaudiello, Gary Stacey, Henry T. Nguyen, Dong Xu. Soybean Knowledge Base (SoyKB): A web resource for integration of soybean translational genomics and molecular breeding. Nucleic Acids Research. 42(1):D1245-D1252, 2014
  4. Qiuming Yao, Huangyi Ge, Shangquan Wu, Ning Zhang, Wei Chen, Chunhui Xu, Jianjiong Gao, Jay J. Thelen, and Dong Xu. P3DB 3.0: From plant phosphorylation sites to protein networks. Nucleic Acids Research. 1;42(1):D1206-D1213, 2014.
  5. Robert J Schmitz, Yupeng He, Oswaldo Valdés-López, Saad M Khan, Trupti Joshi, Mark A Urich, Joseph R Nery, Brian Diers, Dong Xu, Gary Stacey, and Joseph R Ecker. Epigenome-wide inheritance of cytosine methylation variants in a recombinant inbred population. Genome Research. 23(10):1663-1674, 2013.
  6. Jingfen Zhang, Dong Xu. Fast algorithm for population-based protein structural model analysis. Proteomics, 13:221-229, 2013.