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

A - Papers appearing in refereed journals

Koehler, J., Rawlings, C. J., Verrier, P. J., Mitchell, R. A. C., Skusa, A., Ruegg, A. and Philippi, S. 2004. Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures. In Silico Biology. 5 (1), pp. 33-44.

AuthorsKoehler, J., Rawlings, C. J., Verrier, P. J., Mitchell, R. A. C., Skusa, A., Ruegg, A. and Philippi, S.
Abstract

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 http://sourceforge.net/projects/ondex/ 

Year of Publication2004
JournalIn Silico Biology
Journal citation5 (1), pp. 33-44
Web address (URL)https://content.iospress.com/articles/in-silico-biology/isb00165
Open accessPublished as non-open access
Funder project or code513
Publication process dates
Accepted23 Dec 2004
ISSN14343207

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