Modern biological and chemical studies rely on life science databases as well as sophisticated software tools (e.g., homology search tools, modeling and visualization tools). These tools often have to be combined and integrated in order to support a given study. SIBIOS (System for the Integration of Bioinformatics Services) serves this purpose. The services are both life science database search services and software tools. The task engine is the core component of SIBIOS. It supports the execution of dynamic workflows that incorporate multiple bioinformatics services. The architecture of SIBIOS, the approaches to addressing the heterogeneity as well as interoperability of bioinformatics services, including data integration are presented in this paper.

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Modern biological and chemical studies rely on life science databases as well as sophisticated software tools (e.g., homology search tools, modeling and visualization tools). These tools often have to be combined and integrated in order to support a given study. SIBIOS (System for the Integration of Bioinformatics Services) serves this purpose. The services are both life science database search services and software tools. The task engine is the core component of SIBIOS. It supports the execution of dynamic workflows that incorporate multiple bioinformatics services. The architecture of SIBIOS, the approaches used to address the heterogeneity as well as interoperability of bioinformatics services, including data integration are presented in this paper.

Many biological information systems rely on relational database management systems (RDBMS) to manage high-throughput biological data. While keeping these data well archived, organized, and integrated in a common repository is still a challenging task, performing complex queries, i.e., explorative and abstract ad hoc user questions in biology, is an even formidable task often substituted by writing complicated software programs. In this work, we propose a "complex query modeling" method to address the challenge of complex querying in biological domains. Query modeling consists of four distinct but interdependent phases of activities: representation of high-level problems, transformation of problems into connected query interfaces, designing database query structures, and translating query plans into high-performing SQL statements. At each stage, we use different notations and query modeling practices. Using gene indexing project as a case study, we show that query modeling enables prototypical development of high-quality SQL solutions to an inherently abstract and vague user query question, which requires GeneChip designers to sift through millions of database records, process data in dozens of steps, and make myriads of intermediate decisions. We believe our "complex query modeling" method is applicable to other bioinformatics domains with needs for complex database queries.