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. 2002 Aug 23;3(9):RESEARCH0046.
doi: 10.1186/gb-2002-3-9-research0046. Epub 2002 Aug 23.

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

Paul T Spellman  1 , Michael Miller, Jason Stewart, Charles Troup, Ugis Sarkans, Steve Chervitz, Derek Bernhart, Gavin Sherlock, Catherine Ball, Marc Lepage, Marcin Swiatek, W L Marks, Jason Goncalves, Scott Markel, Daniel Iordan, Mohammadreza Shojatalab, Angel Pizarro, Joe White, Robert Hubley, Eric Deutsch, Martin Senger, Bruce J Aronow, Alan Robinson, Doug Bassett, Christian J Stoeckert Jr, Alvis Brazma
Affiliations

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

Paul T Spellman et al. Genome Biol. .

Abstract

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.

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Figures

Figure 1
Figure 1
A workflow diagram showing the various steps of microarray analysis. Rectangles represent physical things, diamonds represent events, ovals represent data, and rounded rectangles represent methods. (a) Workflows that lead up to a hybridized microarray. Two convergent paths (microarray production and sample preparation) are joined by the hybridization event to create a PhysicalBioAssay. (b) BioAssay manipulations.
Figure 2
Figure 2
The BioDataCube. BioDataCubes are composed of a matrix of values. (a) A two-dimensional slice of a BioDataCube for a single Bioassay. Each combination of DesignElements and QuantitationTypes is allowed a value. (b) The cube of values is a set of slices, in this view one slice for each BioAssay.
See this image and copyright information in PMC

References

    1. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, et al. Minimum information about a microarray experiment (MIAME) - toward standards for microarray data. Nat Genet. 2001;29:365–371. - PubMed
    1. MGED - Microarray Gene Expression Data Society http://www.mged.org
    1. Gene Expression Markup Language (GEML) http://www.rosettabio.com/products/conductor/geml/default.htm - PubMed
    1. Extensible Markup Language (XML) 1.0 (Second Edition) http://www.w3c.org/TR/2000/REC-xml-20001006
    1. OMG - Object management group http://www.omg.org

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