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.