Most or all data are structured. These files are the hardest to set up & maintain, and require specific knowledge by a searcher, but they are the easiest to use when doing analysis or integration. Data is categorized by specific fields, and so, by knowing the fields one should be able to capture all the relevant data, quite easily. The searchability of a relational database is totally dependent on how well the database has been structured. For example, in a relational database management system (RDBMS), indices may be created, and the user doesn't have to query against the index. The user still queries against logical relations, and the system automatically determines if it is faster to use the indices to answer a query. The user is thus insulated from worrying about various details, such as physical organization of data on disk, the exact location of the data, tuning the representation for better performance and choosing the best plan for evaluating a query. This declarative querying paradigm has been a huge success for relational DBMSs, and today commercial RDBMSs manage terabytes of data & allow very complex querying on these databases. Database management systems can provide similar benefits to the life sciences community, just as it did three decades ago to the business data management community. Many of the data sets that are used in life sciences are growing at an astonishing rate, such as sequence data.