TY - JOUR

T1 - EMMA 2 - A MAGE-compliant system for the collaborative analysis and integration of microarray data

AU - Dondrup, Michael

AU - Albaum, Stefan P.

AU - Griebel, Thasso

AU - Henckel, Kolja

AU - Jünemann, Sebastian

AU - Kahlke, Tim

AU - Kleindt, Christiane K.

AU - Küster, Helge

AU - Linke, Burkhard

AU - Mertens, Dominik

AU - Mittard-Runte, Virginie

AU - Neuweger, Heiko

AU - Runte, Kai J.

AU - Tauch, Andreas

AU - Tille, Felix

AU - Pühler, Alfred

AU - Goesmann, Alexander

N1 - Funding information: [ MD and KJR acknowledge financial support by the BMBF (grant 0313805A 'GenoMik-Plus'), SA received financial support from the BMBF in the frame of the QuantPro initiative (grant 0313812). MD, HN, KH, and CKK would like to thank the International NRW Graduate School in Bioinformatics and Genome Research for providing financial support. The development of the system was supported within the Grain Legumes Integrated Project (funded by the European Commission FP6 contract No. FOOD-CT-2004-506223) and Marine Genomics Europe (European Network of Excellence FP6 contract No. COGE-CT-2004-505403). We would like to thank Duncan Temple Lang for providing the RSPerl library. The authors wish to thank Björn Fischer, Torsten Kasch, Achim Neumann, and Ralf Nolte for excellent technical support. EMMA 2.8 is the central transcriptomics server for the GenoMik-Plus initiative. GenoMik-Plus is a project funded by the German Federal Ministry of Education and Research (BMBF) to foster genomic research in microorganisms. Within the network EMMA 2 has been used to characterize the gene expression of the model organisms Sinorhizobium meliloti [35,36], Corynebacteria [37-39] and Xanthomonades [40] under many different growth conditions. EMMA 2 is also the central microarray analysis platform of the plant-genomics projects MolMyk [41] and Grain Legumes [42,43] as well as for the EU Network of Excellence Marine Genomics. Altogether, the EMMA 2 databases contain microarray data of more than 20 different organisms. In total, over 3000 microarrays provided by the microarray core facility at the CeBiTec or external project partners and commercial vendors have been processed using the system

PY - 2009/2/6

Y1 - 2009/2/6

N2 - Background: 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. Results: 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. Conclusion: 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.

AB - Background: 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. Results: 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. Conclusion: 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.

UR - http://www.scopus.com/inward/record.url?scp=61649121078&partnerID=8YFLogxK

U2 - 10.1186/1471-2105-10-50

DO - 10.1186/1471-2105-10-50

M3 - Article

C2 - 19200358

AN - SCOPUS:61649121078

VL - 10

JO - BMC BIOINFORMATICS

JF - BMC BIOINFORMATICS

SN - 1471-2105

M1 - 50

ER -