MeMo: a flexible approach to metabolomic data management

Dr Irena Spasic

Manchester Centre for Integrative Systems Biology, The University of Manchester, Manchester, UK.

MeMo is a data model designed to capture information about the metabolomics studies in yeast, covering the overall experimental cycle, including growth, sample preparation and analytical experiments. Information about specific conditions, protocols and parameters used in experiments (i.e. the metadata) is needed to interpret the experimental results and support their comparison and reproducibility. By the very nature of high-throughput metabolomics studies, the data themselves come in huge volumes. MeMo has been implemented using relational database (RDB) technology as a practical solution for handling the sheer volume and complexity of the metabolomic data effectively and efficiently. We combined the advantages of a RDB with those of XML, such as scalability and self-descriptiveness, which are used to simplify the relational schema and facilitate the extensibility of the model necessitated by the dynamic nature of metabolomics (new methods and techniques used). A modular approach is used where different metadata modules (implemented as separate XML schemas) can be plugged into the relational schema. Pierre, software that generates a database interface from its schema, was used to configure and deploy an interface to MeMo. The interface generated supports interactive as well as programmatic access to the data. The web interface supports capabilities familiar from public access interfaces to bioinformatics databases: (1) Browse: provides an entry point to the database allowing the user to browse and explore the data by following the links provided, (2) Simple Search: provides a set of canned queries, (3) Advanced Search: enables the filtering of data, and (4) Expert Search: allows end users to query the database directly provided they are familiar with SQL. The web service enables programmatic access to a set of canned queries. We will illustrate two current uses of MeMo for yeast metabolomics: (1) metabolomic footprinting as a strategy for functional genomics, and (2) the effects of growth rate on the metabolite levels.