Abstract
BackgroundUnderstanding 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.ResultsThe 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.ConclusionOur 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.
Highlights
Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes
The interface has been designed to be highly user-friendly and interactive. It aids the user in Microarray Experiment (MIAME) compliant annotation of microarray experiments while hiding the complexity of the underlying object
We have studied the transcriptome of nitrogen-fixing root nodules induced by Sinorhizobium meliloti
Summary
Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Instead of being focused on single or small sets of genes of interest the microarray technology provides a holistic view on gene expression of an organism due to the analysis of thousands of genes in a single experiment. This high-throughput technique is used in functional genomics due to its wide range of applications, relative cost-efficiency, and its potential to support genome-wide studies [3]. To standardize the annotation of microarray data, the Minimum Information About a Microarray Experiment (MIAME) [5] recommendation, the Microarray Gene Expression Markup Language (MAGE-ML) format for data interchange [6], and ontologies such as the MGED ontology [7] have been proposed
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