The successful sequencing of the genoma of various species leads to a great amount of data that need to be managed and analyzed. With the increasing popularity of high-throughput sequencing technologies, such data require the design of flexible scalable, efficient algorithms and enterprise data structures to be manipulated by both biologists and computational scientists; this emerging scenario requires flexible, scalable, efficient algorithms and enterprise data structures. This chapter focuses on the design of large scale database-driven applications for genomic and proteomic data; it is largely believed that biological databases are similar to any standard database-drive application; however, a number of different and increasingly complex challenges arises. In particular, while standard databases are used just to manage information, in biology, they represent a main source for further computational analysis, which frequently focuses on the identification of relations and properties of a network of entities. The analysis starts from the first text-based storage approach and ends with new insights on object relational mapping for biological data.
Time and Space Efficient Algorithms for Two-Party Authenticated Data Structures