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

Standard

MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. / Spasić, Irena; Dunn, Warwick B.; Velarde, Giles et al.

In: BMC Bioinformatics, Vol. 7, 281, 05.06.2006.

Research output: Contribution to journal › Article › peer-review

Harvard

Spasić, I, Dunn, WB, Velarde, G, Tseng, A, Jenkins, H, Hardy, N, Oliver, SG & Kell, DB 2006, 'MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics', BMC Bioinformatics, vol. 7, 281. https://doi.org/10.1186/1471-2105-7-281

APA

Spasić, I., Dunn, W. B., Velarde, G., Tseng, A., Jenkins, H., Hardy, N., Oliver, S. G., & Kell, D. B. (2006). MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics, 7, [281]. https://doi.org/10.1186/1471-2105-7-281

Vancouver

Spasić I, Dunn WB, Velarde G, Tseng A, Jenkins H, Hardy N et al. MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics. 2006 Jun 5;7:281. doi: 10.1186/1471-2105-7-281

Author

Spasić, Irena ; Dunn, Warwick B. ; Velarde, Giles et al. / MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. In: BMC Bioinformatics. 2006 ; Vol. 7.

Bibtex - Download

@article{c8e6226786394a8c9fc4dcae12cf925c,
title = "MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics",
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.",
author = "Irena Spasi{\'c} and Dunn, {Warwick B.} and Giles Velarde and Andy Tseng and Helen Jenkins and Nigel Hardy and Oliver, {Stephen G.} and Kell, {Douglas B.}",
note = "Spasic, I., Dunn, WB., Velarde, G., Tseng, A., Jenkins, H., Hardy, N., Oliver, SG., Kell, DB., MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics, BMC BIOINFORMATICS, 5, 2006, 7, 281",
year = "2006",
month = jun,
day = "5",
doi = "10.1186/1471-2105-7-281",
language = "English",
volume = "7",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "Springer Nature",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics

AU - Spasić, Irena

AU - Dunn, Warwick B.

AU - Velarde, Giles

AU - Tseng, Andy

AU - Jenkins, Helen

AU - Hardy, Nigel

AU - Oliver, Stephen G.

AU - Kell, Douglas B.

N1 - Spasic, I., Dunn, WB., Velarde, G., Tseng, A., Jenkins, H., Hardy, N., Oliver, SG., Kell, DB., MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics, BMC BIOINFORMATICS, 5, 2006, 7, 281

PY - 2006/6/5

Y1 - 2006/6/5

N2 - 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.

AB - 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.

U2 - 10.1186/1471-2105-7-281

DO - 10.1186/1471-2105-7-281

M3 - Article

VL - 7

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

M1 - 281

ER -

IRUS-UK
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