The power of an integrated informatic and molecular approach to type 1 diabetes research.
Recent years have witnessed an explosive growth in available biological data. This includes a tremendous quantity of sequence data (e.g., biological structures, genetic and physical maps, pathways) generated by genome and transcriptome projects focused on humans, mice, and a multitude of other species. Diabetes research stands to greatly benefit from this data, which is distributed across public and private databases and the scientific literature. The increasing quantity and complexity of this biological data necessitates use of novel bioinformatics strategies for its efficient retrieval, analysis, and interpretation. Bioinformatic capability is becoming increasingly indispensable for fast and comprehensive analysis of biological data by diabetes researchers. There is great potential for diabetes scientists and clinicians to take advantage of recent bioinformatics and knowledge discovery developments to radically transform and advance this field of research. This paper will review advances in the field of bioinformatics relevant to diabetes research and preview a new specialty diabetes database, Diabetaeta, that we are creating to serve as a central bioinformatic portal for type 1 diabetes research, as well as serving as a public repository for beta cell gene and protein expression data.
Petrovsky
Nikolai
N
Director, Diabetes and Endocrinology, Flinders Medical Centre, Bedford Park, South Australia, Australia. nikolai.petrovsky@anu.edu.au
Silva
Diego
D
D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics* / Q000378:metabolism*; D006801:Humans; D008967:Molecular Biology / Q000379:methods*; D020161:Physical Chromosome Mapping; D012106:Research