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

Area of Science:

• Systems Biology
• Genomics
• Metabolomics

Background:

• Genome sequencing reveals significant gaps in understanding gene function, particularly in model organisms like S. cerevisiae.
• Large-scale metabolomic studies offer insights into cellular behavior but require structured data for analysis.
• Existing metabolomic data lacks machine-usable formats, hindering the exploration of gene-function relationships.

Purpose of the Study:

• To develop a formal model for representing metabolomic data and associated metadata.
• To create a structured and machine-usable format for metabolomic data.
• To facilitate the exploration of links between genes and their functions within a systems biology context.

Main Methods:

• Developed MeMo, a formal model for metabolomic data and metadata.
• Implemented MeMo using a hybrid approach combining SQL and XML for a relational database.
• Utilized relational database technology for efficient data processing and XML for schema simplification and extensibility.

Main Results:

• MeMo provides a practical solution for managing the volume and complexity of metabolomic data.
• The model integrates metabolomic data with genomic databases through physical and semantic linking.
• Ontological annotation supports semantic integration, and automatic conversion ensures compatibility with other data formats.

Conclusions:

• MeMo leverages mature relational database technology and the scalability of XML for effective metabolomic data management.
• The model addresses data integration challenges crucial for advancing systems biology research.
• MeMo facilitates the exploration of gene functions by structuring and annotating complex metabolomic datasets.