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

Paul T. Spellman, Michael Miller, Jason Stewart, Charles Troup, Ugis Sarkans, Steve Chervitz, Derek Bernhart, Gavin Sherlock, Catherine Ball, Marc Lepage, Marcin Swiatek, Wl Marks, Jason Goncalves, Scott Markel, Daniel Iordan, Mohammadreza Shojatalab, Angel Pizarro, Joe White, Robert Hubley, Eric DeutschMartin Senger, Bruce J. Aronow, Alan Robinson, Doug Bassett, Christian J. Stoeckert, Alvis Brazma

Résultats de recherche: Contribution à un journalArticleRevue par des pairs

381 Citations (Scopus)

Résumé

Background: Meaningful 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. Results: To 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. Conclusions: MAGE 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.

langue originaleAnglais
Numéro d'articleresearch0046.1
journalGenome biology
Volume3
Numéro de publication9
Les DOIs
étatPublié - 1 août 2002
Modification externeOui

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