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MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics

Spasic, Irena, Dunn, Warwick B., Velarde, Giles, Tseng, Andy, Jenkins, Helen, Hardy, Nigel, Oliver, Stephen G. and Kell, Douglas B. 2006. MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics 7 , 281. 10.1186/1471-2105-7-281

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Abstract

Background: The genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5-6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions. Description: MeMo is a formal model for representing metabolomic data and the associated metadata. Two predominant platforms (SQL and XML) are used to encode the model. MeMo has been implemented as a relational database using a hybrid approach combining the advantages of the two technologies. It represents a practical solution for handling the sheer volume and complexity of the metabolomic data effectively and efficiently. The MeMo model and the associated software are available at http://dbkgroup.org/memo/. Conclusions: The maturity of relational database technology is used to support efficient data processing. The scalability and self-descriptiveness of XML are used to simplify the relational schema and facilitate the extensibility of the model necessitated by the creation of new experimental techniques. Special consideration is given to data integration issues as part of the systems biology agenda. MeMo has been physically integrated and cross-linked to related metabolomic and genomic databases. Semantic integration with other relevant databases has been supported through ontological annotation. Compatibility with other data formats is supported by automatic conversion.

Item Type: Article
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
Uncontrolled Keywords: Spasic; Metabolomics; Systems biology; Genomics; Data
Additional Information: 16 pp.
Publisher: Biomed Central
ISSN: 1471-2105
Last Modified: 15 Nov 2013 09:25
URI: http://orca-mwe.cf.ac.uk/id/eprint/6215

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