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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Contributions of the EMERALD project to assessing and improving microarray data quality.

Vidar Beisvåg1, Audrey Kauffmann, James Malone

  • 1Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.

Biotechniques
|January 15, 2011
PubMed
Summary

The EMERALD project developed tools to assess and improve microarray data quality, enhancing statistical analysis and data sharing. These methods, including the arrayQualityMetrics software, promote reliable gene expression research.

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Genomics

Background:

  • Minimum Information About a Microarray Experiment (MIAME) standards improved data value in public repositories.
  • Limited methods existed for assessing microarray data quality and normalization procedures.
  • Assessing and enhancing microarray data quality is crucial for reliable biological interpretation.

Purpose of the Study:

  • To develop and disseminate approaches for assessing and improving microarray data quality.
  • To enhance the power of statistical analyses and facilitate joint analysis of multiple datasets.
  • To standardize the description of data transformations and gene expression analysis software.

Main Methods:

  • Development of quality metrics tools for assessing microarray data.
  • Application of tools to demonstrate the impact of removing poor-quality data.
  • Creation of ontologies for describing data transformations and gene expression analysis software.
  • Advocacy for external reference standards in microarray hybridizations.

Main Results:

  • Quality metrics tools were developed and disseminated via publications and the arrayQualityMetrics software package.
  • Demonstrated that removing poor-quality data improves statistical analysis power.
  • Developed ontologies for data transformations and gene expression analysis software.
  • Established the Molecular Methods (MolMeth) database for microarray protocols.

Conclusions:

  • The EMERALD project successfully delivered approaches to assess and enhance microarray data quality.
  • Dissemination through workshops and software has equipped the community with tools for better data analysis.
  • Standardized ontologies and reference standards will further improve data reproducibility and comparability.