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EMMA 2 – A MAGE-compliant system for the collaborative analysis and integration of microarray data

Michael Dondrup1*, Stefan P Albaum1, Thasso Griebel2, Kolja Henckel15, Sebastian Jünemann1, Tim Kahlke3, Christiane K Kleindt45, Helge Küster7, Burkhard Linke1, Dominik Mertens1, Virginie Mittard-Runte1, Heiko Neuweger15, Kai J Runte1, Andreas Tauch4, Felix Tille1, Alfred Pühler46 and Alexander Goesmann1

Author Affiliations

1 Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany

2 Lehrstuhl für Bioinformatik, Friedrich-Schiller-Universität Jena, 07743 Jena, Germany

3 Institute for Medical Biology, Department of Molecular Biotechnology, Medical Faculty, University of Tromsø, 9037 Tromsø, Norway

4 Institute for Genome Research and Systems Biology, Bielefeld University, Bielefeld, Germany

5 International NRW Graduate School in Bioinformatics and Genome Research, Center for Biotechnology, Bielefeld University, Bielefeld, Germany

6 Fakultät für Biologie, Bielefeld University, Bielefeld, Germany

7 Institute for Plant Genetics, Unit IV – Plant Genomics, Leibniz Universität Hannover, 30419 Hannover, Germany

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BMC Bioinformatics 2009, 10:50  doi:10.1186/1471-2105-10-50

Published: 6 February 2009



Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems.


The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services.


Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.