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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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Related Experiment Video

Updated: May 26, 2026

Single-Molecule Fluorescence Visualization of DNA Polymerase Dynamics at G-Quadruplexes
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Normalized Affymetrix expression data are biased by G-quadruplex formation.

Hugh P Shanahan1, Farhat N Memon, Graham J G Upton

  • 1Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK. Hugh.Shanahan@rhul.ac.uk

Nucleic Acids Research
|December 27, 2011
PubMed
Summary

Microarray probes containing guanine-rich sequences (G-stacks) can form G-quadruplexes, leading to biased gene expression data. Removing these probes improves data accuracy, even with increased noise.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Microarray analysis relies on accurate gene expression measurement.
  • Probes with G-rich sequences (G-stacks) can form G-quadruplex structures.
  • G-quadruplex formation can interfere with probe hybridization and data interpretation.

Purpose of the Study:

  • To investigate the impact of G-stack probes on microarray data quality.
  • To quantify the bias introduced by G-quadruplex-forming probes.
  • To evaluate normalization methods for mitigating G-stack probe bias.

Main Methods:

  • Analysis of a Human HG_U133A Affymetrix GeneChip dataset.
  • Application of Robust Multi-array Average (RMA) normalization.
  • Comparison with FARMS and PLIER normalization pipelines.
  • Assessment of bias in expression levels, fold change, and gene correlations.

Main Results:

  • G-stack probes introduce significant bias in expression levels, fold change, and correlations.
  • Bias increases with the number of G-stack probes per probe set.
  • Approximately 14% of probe sets were directly affected in the analyzed dataset.
  • FARMS and PLIER normalization methods partially reduced the observed bias.
  • An estimated 15% of GEO database datasets may be susceptible to this bias.

Conclusions:

  • G-stack probes pose a significant challenge to the accuracy of microarray data analysis.
  • The presence of G-stack probes can lead to erroneous gene selection and clustering.
  • Eliminating G-stack probes is recommended for affected datasets, as benefits outweigh potential noise increase.