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

By Irena Spasic, Warwick B. Dunn, Giles Velarde, A. Tseng, Helen Jenkins, N. Hardy, S. Oliver, D. B. Kell. BMC Bioinformatics, 2006

514 citations recorded by the source corpus.

BackgroundThe 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.DescriptionMeMo 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/.ConclusionThe 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a82b41bcd6399c62e477dadb924ded73b6bf7eb0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Protein–protein interaction sites are hot spots for disease‐associated nonsynonymous SNPs (2012, methodology): “, 2010], SNP3D [Yue et al., 2006], and SNAP [Bromberg and Rost, 2007] and reviewed in Jordan et al.”
The human phylome (2007, background): “Several databases have been developed that employ such algorithms to derive orthology relationships from automatically reconstructed trees [79-81].”
FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix (2009, background): “One alternative to building large gene trees that we have explored is to identify orthology groups and to build smaller trees for these subfamilies, without building larger trees ( Dehal and Boore, 2006 ).”
TreeFam: 2008 Update (2007, methodology): “In addition to the databases mentioned above, many other databases provide animal gene families on the genome-wide scale, such as PANTHER (9), Phylofacts (10), PhIGs (11) and SYSTERS (12).”