Motivation: The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards.
Results: Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation.
Availability: The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do).
Contact: Stoeckrt@pcbi.upenn.edu
Supplementary Information: Supplementary data are available at Bioinformatics online.
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Database (Oxford)
April 2012
Department of Genetics, Center for Bioinformatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments.
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Lombardi Comprehensive Cancer Center, Georgetown University Medical Center,Washington, DC, USA.
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View Article and Find Full Text PDFComp Funct Genomics
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Department of Genetics and Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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View Article and Find Full Text PDFMethods Enzymol
December 2006
Department of Genetics, Center for Bioinformatics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
Consistent annotation of studies using microarrays is critical to optimal management and use of microarray data. Ontologies provide defined and structured terminology suited for this purpose. The Gene Ontology (GO) has aided the analysis of expression studies greatly by providing consistent functional annotation of array sequence features.
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