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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data.

Michael Dondrup¹, Stefan P Albaum, Thasso Griebel

¹Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de

BMC Bioinformatics

|February 10, 2009

Summary

EMMA 2 software enhances microarray data analysis by fully supporting MAGE-ML standards and integrating diverse genomic data. This facilitates collaborative research and is scalable for future transcriptomics methods.

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Area of Science:

Genomics
Bioinformatics
Transcriptional Regulation

Background:

Genome-wide microarray studies are crucial for understanding gene relationships.
Standardization efforts like MIAME and MAGE-ML aim to improve data annotation.
Existing software often lacks full standard support and extensibility, hindering integrated analysis.

Purpose of the Study:

To present EMMA 2, a software system designed to address limitations in microarray data analysis software.
To provide comprehensive analysis functions for various microarray types (spotted, Agilent, Affymetrix, NimbleGen).
To leverage modern data integration techniques and support for MAGE-ML and ontologies.

Main Methods:

Utilizes a model-driven approach for full MAGE object model implementation.
Integrates genomic annotation and external knowledge using open standards.
Employs R and Bioconductor packages for analysis, with compute cluster support.

Main Results:

EMMA 2 offers comprehensive analysis for spotted and synthesized oligo arrays.
The system is based on the full MAGE object model, ensuring compatibility.
Data integration is achieved via SOAP-based web services.

Conclusions:

The model-driven approach ensures flexibility and compatibility in microarray data analysis.
EMMA 2 facilitates collaborative analysis in distributed environments and international consortia.
The software's scalability supports future high-throughput sequencing-based transcriptomics methods.