Design and implementation of microarray gene expression markup language (MAGE-ML)

By P. Spellman, Michael Miller, Jason E. Stewart, Charles Troup, Ugis Sarkans, S. Chervitz, Derek Bernhart, Gavin Sherlock, C. Ball, M. Lepage and 16 more. Genome Biology, 2002

491 citations recorded by the source corpus.

BackgroundMeaningful exchange of microarray data is currently difficult because it is rare that published data provide sufficient information depth or are even in the same format from one publication to another. Only when data can be easily exchanged will the entire biological community be able to derive the full benefit from such microarray studies.ResultsTo this end we have developed three key ingredients towards standardizing the storage and exchange of microarray data. First, we have created a minimal information for the annotation of a microarray experiment (MIAME)-compliant conceptualization of microarray experiments modeled using the unified modeling language (UML) named MAGE-OM (microarray gene expression object model). Second, we have translated MAGE-OM into an XML-based data format, MAGE-ML, to facilitate the exchange of data. Third, some of us are now using MAGE (or its progenitors) in data production settings. Finally, we have developed a freely available software tool kit (MAGE-STK) that eases the integration of MAGE-ML into end users' systems.ConclusionsMAGE will help microarray data producers and users to exchange information by providing a common platform for data exchange, and MAGE-STK will make the adoption of MAGE easier.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dc8117e6e206110312ce9dae6b51bad2cdb22641

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Citations in the corpus

Sharing Detailed Research Data Is Associated with Increased Citation Rate (2007, methodology): “Standards have been developed to specify minimal required data elements (MIAME[29] for microarray data, REMARK[30] for prognostic study details), consistent data encoding (MAGE-ML[31] for microarray data), and semantic models (BRIDG (www.bridgpro-ject.org) for study protocol details).”
FlyMine: an integrated database for Drosophila and Anopheles genomics (2007, methodology): “Currently, we can load any data that conform to several different formats: GFF3 [30] for genome annotation and genomic features (for example, Dnase I footprints, microarray oligonucleotide and genome tiling amplimers), PSI-MI [31,32] for data describing protein interactions or complexes, MAGE [33,34”
ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments (2010, background): “The replacement of existing MAGE-OM centric architecture (11) with MAGE-TAB-based infrastructure is ongoing and data migration is underway.”
Human protein reference database as a discovery resource for proteomics (2004, methodology): “The use of eXtensible Markup Language (XML) as a standard for implementation of gene expression microarray data has already been adopted by the microarray community (13) and will soon be adopted for proteomic data as well (14).”
MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics (2006, unknown): “These efforts resulted in a proposed standard for the MIAME [14] and the Microarray Gene Expression Markup Language [15].”
The Ontology for Biomedical Investigations (2016, background): “For many years, different formats to describe experiments have been developed in isolation, as each research community tended to focus on a specific methodology[1, 2].”
A common open representation of mass spectrometry data and its application to proteomics research (2004, background): “Although it is desirable to maintain a certain level of flexibility, too much freedom can lead to the formation of 'dialects,' as has occurred with microarray gene expression (MAGE) suit”