Combination of a data warehouse concept with web services for the establishment of the Pseudomonas systems biology database SYSTOMONAS
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doi:10.2390/biecoll-jib-2007-48
November 13, 2006
January 25, 2007
Claudia Choi, Richard Münch, Boyke Bunk, Jens Barthelmes, Christian Ebeling, Dietmar Schomburg, Max Schobert, Dieter Jahn
Correspondence should be addressed to:
Dieter Jahn
Institut für Mikrobiologie, Technische Universität Braunschweig, Germany
ed.sb-ut@nullnhaj.d
Abstract
Systems biology requires the integration of data from various sources and their combined interpretation using different bioinformatics tools. Integration of different biological databases, however, is often problematic due to their semantic and structural diversity. Moreover, necessary continuous updates of both the structure and content of a database provide further challenges for an integration process. We established the novel database SYSTOMONAS for SYSTems biology of pseudOMONAS by integrating heterogeneous data from highly different external resources including BioCyc, BRENDA, ENZYME, Pseudomonas Genome Database v2, KEGG, and PRODORIC. For this purpose we combined a data warehouse concept with the advantages of web services. This hybrid approach benefits from the fast performance and data consistency provided by the data warehouse system and from the up-to-dateness ensured by use of dynamic web services. The data warehouse part is realized by ETL processes (Extract, Transform, Load), during which data are checked for consistency and standardized to ensure their integrity. While accessing SYSTOMONAS via the internet, parts of the data warehouse content are dynamically enriched using the web service part of the system via SOAP (originally for Simple Object Access Protocol) interfaces with BRENDA, KEGG and PRODORIC. SYSTOMONAS is designed to integrate in-house experimental high-throughput data with up-to-date information available in the mentioned public databases. SYSTOMONAS also serves as a repository for the prediction of metabolic and regulatory networks. SYSTOMONAS is accessible at http://www.systomonas.de.