Computational Biology & Bioinformatics
The complete nucleotide sequence information already exists for the genomes of many organisms, including human. This sequence data is only useful if a researcher can readily (i) access and visualize the data, (ii) connect the data with other sequences and biological data, and (iii) extract meaningful patterns and information.
To transform this genome sequence data into biological knowledge will require research that includes: computational biology; a science of finding meaningful patterns in biological data and bioinformatics; the information system engineering to acquire, manage, and visualize biological data.
Computational biology and bioinformatics are highly interdisciplinary fields that bring aspects of computer science, mathematics, information science, and physics to biology. Computational analysis of genomes (the complete set of genes in a organism) and proteomes (the complete set of proteins in a organism) will be a fruitful field of research for long into the future. It will provide a needed infrastructure to support diverse experimental approaches to understanding and applying biological phenomena. At present, computational molecular biology focuses on several areas, including biological sequence comparative analysis, gene and feature finding, protein classification, and computational protein structure. Some current interests of researchers in the Genome Science and Technology Graduate Program include:
- high-throughput genome sequence production;
- high-throughput sequence analysis tools and pipelines;
- whole-genome gene finding, gene and protein modeling, and genome annotation;
- advanced bioinformation systems and laboratory information systems;
- applications of high-performance biocomputing;
- modeling of carcinogen modification of DNA;
- sequence analysis of long genome regions that are conserved between mouse and man;
- microbial genome analysis;
- protein threading; and
- other computational research in molecular biology.
There are also a number of opportunities to collaborate with researchers to develop approaches that complement and extend existing experimental approaches and technologies.