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Besides domain-specific biological problems, biologists are confronted with many computational problems. The large amount of varying, heterogeneous, and semi-structured biological data, the increasing complexity of biological applications, methods, and tools afflicted with uncertainty and missing knowledge, as well as the lacking interoperability of available tools necessitate integrative measures to enable biology workflow. In this paper we address these problems in the context of the processing and evaluation of BLAST query results. We present a new tool, called BlastQuest, which relies on database technology and provides sophisticated interactive and Web-enabled query, analysis, and visualization facilities for genomics data. The interface with the Gene Ontology and the KEGG pathway databases decisively foster the biological workflow. Finally, based on our experience with BlastQuest, we briefly sketch a new concept, called Genomics Algebra, for solving genomic data management problems from a broader perspective.

Besides domain-specific biological problems, biologists are confronted with many computational problems. For example, the exponentially growing volume of heterogeneous, semi-structured biological data that has to be processed and analyzed. Another problem is the increasing complexity of biological applications, methods, and tools afflicted with an inherent lack of biological knowledge as well as intrinsic uncertainty. A third problem is the lacking interoperability of available tools, i.e., biological tools are more or less self-contained and isolated, mostly only visualization-based, and incapable of exchanging data with each other. As a result, many challenges in biology and genomics are now challenges in computing and here especially in information management and algorithm design. This necessitates the development of an appropriate “communication interface” between biologists and computer scientists who each live in their own world but also recognize the chances for jointly solving important problems in common future research. This paper deals with the three aforementioned problems in the context of BLAST (Basic Local Alignment Search Tool) [1], a common tool for conducting similarity searches. BLAST comprises a set of similarity search algorithms that employ heuristics to detect relationships between gene sequences and that rank the computed “hits” statistically. An essential problem for the biologist is currently the processing and evaluation of BLAST query results, since a BLAST search yields its result exclusively in a textual format (e.g., ASCII, HTML, XML). This format has the benefit of being application-neutral but at the same time prevents efficient analysis. In this paper, we describe a new powerful tool called BlastQuest for managing BLAST results stemming from multiple individual queries. This tool provides the biologist with interactive and Web-enabled query, analysis, and visualization facilities beyond what is possible by current BLAST interfaces. In particular, BLAST results from multiple queries are imported, structured, and stored in a relational database to support a series of built-in analysis operations that can be used to select, browse, filter, group, order, search, and annotate sequence data efficiently and without referring to the original BLAST result files. In addition, users have the option to interact with the data through a forms-based query interface. BlastQuest also establishes connections to the Gene Ontology (GO) [10], which is a controlled vocabulary about gene and protein roles in cells, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) [16], which is a pathway database and integrates current knowledge on molecular interaction networks in biological processes. The rest of this paper is organized as follows. Section 2 briefly reviews some important biological concepts and addresses the biological workflow when working with BLAST, Gene Ontology, and the KEGG database. Section 3 emphasizes the need for tools capable of processing BLAST results and identifies their functional requirements. In Section 4, we describe our BlastQuest prototype from the system architecture and implementation perspective. An example session in Section 5 describes the main features and data analysis options of the BlastQuest system and its user interface. Section 6 evaluates the BlastQuest system and reports on our experiences with it. Section 7 discusses related work. Section 8 considers desired improvements to BlastQuest. We conclude the section with a brief description of a new, data model, language, and architecture called Genomics Algebra for integrating, processing and querying genomic information. Finally, Section 9 summarizes the paper.

In this paper we have described BlastQuest, a Web-based and interactive tool for importing and persistently storing genomic data from multiple BLAST queries in a relational database, applying DBMS functionality for processing and querying these data, and visualizing them appropriately. In addition BlastQuest supports the ability to connect sequence identities inferred from BLAST results with gene-associated biological functions described through the efforts of the Gene Ontology (GO) Consortium and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG). This type of cross-referencing has shown to be an ideal way to describe the functionality of a newly, discovered gene and helps biologists annotate and catalogue the genes in a way that is universally accepted. BlastQuest is being supported by the Interdisciplinary Center for Biotechnology Research (ICBR) at the University of Florida and has been successfully employed and tested by scientists on campus and their collaborators around the world for over eighteen months. We are now in the process of developing the next-generation BlastQuest, which addresses the limitations of our existing concept mainly with respect to the need for a more expressive and extensible representation and data model, tools to support the browsing and integration of external repositories, and a richer and more intuitive query language that can be extended with new analytical functions and that can take advantage of the new data model.