The overall objective of the Bioinformatics is to provide support to scientific data analysis for researchers and to promote the application of modern computational approaches to solving basic biological problems. In general, the facilities required for scientific data processing are, routine laboratory data processing and analysis using established software systems & service protocols (biological sequence cleaning, assembly, annotation & categorization), implementation & application of statistical & heuristic algorithms to explorative analysis of experimental data for particular problem solving & knowledge discovery, development of scientific software for data processing, data management & data publication as integral components of research projects in the foundation, training in bioinformatics software utilization & data analysis & assisting manuscript writing & grant applications on related bioinformatics topics.

Database management systems are designed to support large volumes of data storage, data processing, data querying & most recently, data mining and knowledge discovery activities. Rapid increase in computing power and advances in data management techniques in recent decades have led many researchers to pursue knowledge discovery with databases & database management systems as their primary computing platform. A recent trend of general database research in this direction has been the incorporation of domain semantics into the representation and management of data. Biological data are often characterized as having large volumes, complex structures, high dimensionality, evolving biological concepts and insufficient data modeling practices. These characteristics require database researchers and developers to make many special considerations while developing biological databases & database systems. If the central task of bioinformatics is the computational analysis of biological sequences, structures, and relationships, it is crucial that biological sequences & all associated data be accurately captured, annotated & maintained, even in the face of rapid growth & frequent updates. It is also critical to be able to retrieve data of interest from multiple distributed heterogeneous data sources in a timely manner, and precisely enough to be able effectively to separate them from the distracting noise of irrelevant, unreliable or insignificant data.