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Related Experiment Videos

Standardizing global gene expression analysis between laboratories and across platforms.

Theodore Bammler, Richard P Beyer, Sanchita Bhattacharya

    Nature Methods
    |April 23, 2005
    PubMed

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    Summary
    This summary is machine-generated.

    Standardized protocols and common platforms significantly improve DNA microarray reproducibility across laboratories. This enhances collaborative multi-investigator research by ensuring reliable RNA expression data comparability.

    Area of Science:

    • Genomics
    • Bioinformatics
    • Molecular Biology

    Background:

    • DNA microarrays are crucial for multi-investigator research but face challenges with inter-laboratory and inter-platform data variability.
    • Variability in RNA expression data can hinder collaborative efforts and the reliability of scientific findings.

    Purpose of the Study:

    • To identify sources of error and variability in DNA microarray experiments across different laboratories and platforms.
    • To develop and evaluate methods for accommodating data variability and improving reproducibility.

    Main Methods:

    • Seven laboratories generated RNA expression data using 12 microarray platforms and two standard RNA samples.
    • Standardized protocols for RNA labeling, hybridization, processing, data acquisition, and normalization were implemented.

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  • Analysis focused on biological themes defined by enriched Gene Ontology (GO) categories.

    • Main Results:

    • Reproducibility was good within laboratories but poor between platforms and across laboratories initially.
    • Implementing standardized protocols markedly increased inter-laboratory reproducibility.
    • Highest reproducibility was achieved when analysis was based on GO categories.

    Conclusions:

    • DNA microarray results can be made comparable across multiple laboratories.
    • The use of a common platform and standardized procedures is essential for reliable multi-laboratory microarray studies.
    • Gene Ontology category-based analysis improves the robustness of microarray data interpretation.