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In Silico Biology 5, 0005 (2004); ©2004, Bioinformation Systems e.V.  

Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures

Jacob Koehler1,*, Chris Rawlings1, Paul Verrier1, Rowan Mitchell1, Andre Skusa2, Alexander Ruegg2 and Stephan Philippi3

1 Rothamsted Research, BAB division, Harpenden, UK 2 University of Bielefeld, Technical Faculty, Germany 3 University of Koblenz, Department of Computer Science, Germany

* Corresponding author; Email:

Edited by H. Michael; received October 19, 2004; revised and accepted December 23, 2004; published December 31, 2004


The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added.

We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from

Keywords: graph database, ontology, Generalised Data Structures, semantic data integration