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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2006-06-05

AUTHORS

Irena Spasić, Warwick B Dunn, Giles Velarde, Andy Tseng, Helen Jenkins, Nigel Hardy, Stephen G Oliver, Douglas B Kell

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/. CONCLUSION: 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. More... »

PAGES

281-281

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-7-281

DOI

http://dx.doi.org/10.1186/1471-2105-7-281

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052212053

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16753052


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