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. Ming Liu, Zhiqian Zhou, Penghui Shang, and Dong Xu. Fuzzified Image Enhancement for Deep Learning in Iris Recognition. IEEE Transactions on Fuzzy Systems. In press.
  2. Xiaoyue Feng, Hao Zhang, Yijie Ren, Penghui Shang, Yi Zhu, Yanchun Liang, Renchu Guan, and Dong Xu. Pubmender: Deep Learning Based Recommender System for Biomedical Publication Venue. Journal of Medical Internet Research. In press.
  3. Duolin Wang, Yanchun Liang, Dong Xu. Capsule Network for Protein Post-Translational Modification Site Prediction. Bioinformatics., 2018.
  4. Duolin Wang, Juexin Wang, Yuexu Jiang, Yanchun Liang, Dong Xu. BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-expression Analysis. Journal of Molecular Biology. 429:446–453, 2017.
  5. Md Shakhawat Hossain, Taiji Kawakatsu, Kyung Do Kim, Ning Zhang, Cuong Nguyen, Saad Khan, Josef Batek, Trupti Joshi, Jeremy Schmutz, Jane Grimwood, Robert Schmitz, Dong Xu, Scott A Jackson, Joseph Ecker, Gary Stacey. Divergent cytosine DNA methylation patterns in single-cell, soybean root hairs. New Phytologist. DOI: 10.1111/nph.14421, 2017.
  6. Duolin Wang, Shuai Zeng, Chunhui Xu, Wangren Qiu, Yanchun Liang, Trupti Joshi, Dong Xu. MusiteDeep: A Deep-learning Framework for General and Kinase-specific Phosphorylation Site Prediction. Bioinformatics. 33(24):3909-3916, 2017.
  7. Weizhong Lin and Dong Xu. Imbalanced Multi-label Learning for Identifying Antimicrobial Peptides and Their Functional Types. Bioinformatics. 32(24):3745-3752, 2016.
  8. Matthew J. Salie, Ning Zhang, Veronika Lancikova, Dong Xu, Jay J. Thelen. A Family of Negative Regulators Targets the Committed Step of De Novo Fatty Acid Biosynthesis. Plant Cell. 28(9): 2312–2325, 2016.
  9. Mary Galli, Arjun Khakhar, Zefu Lu, Zongliang Chen, Sidharth Sen, Trupti Joshi, Jennifer L. Nemhauser, Robert J. Schmitz, and Andrea Gallavotti. The DNA binding landscape of the maize auxin response factor family. Nature Communications. Nature Communications. 2018. Volume 9, Article number: 4526 (2018).
  10. Majumder K, Wang J, Boftsi M, Fuller MS, Rede JE, Joshi T, Pintel DJ. Parvovirus minute virus of mice interacts with sites of cellular DNA damage to establish and amplify its lytic infection. eLife. 2018;7:e37750.