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Comparison of sequence-dependent tiling array normalization approaches.

Ho-Ryun Chung1, Martin Vingron

  • 1Max-Planck-Institut für Molekulare Genetik, Department of Computational Molecular Biology, Berlin, Germany. chung@molgen.mpg.de

BMC Bioinformatics
|July 2, 2009
PubMed
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Sequence-dependent normalization for tiling microarrays offers limited improvement for detecting DNA or RNA fragments. A simple, sequence-independent method, aided by control experiments, performs comparably well.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Tiling microarrays are increasingly used for DNA/RNA detection.
  • Probe sequence affects hybridization affinity, introducing bias.
  • Existing normalization methods aim to correct sequence-dependent biases.

Purpose of the Study:

  • To evaluate sequence-dependent normalization procedures for tiling microarrays.
  • To compare their performance against a sequence-independent approach.
  • To determine if observed improvements are due to normalization or peak detection algorithms.

Main Methods:

  • Compared three sequence-dependent normalization methods with a naïve sequence-independent method.
  • Utilized normalized intensity values as input for a single peak detection algorithm.

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  • Employed a "spike-in" dataset as a benchmark for performance evaluation.

    • Main Results:

    • Sequence-dependent normalization did not outperform the naïve sequence-independent approach.
    • The benefit of sequence-dependent normalization methods appears limited.
    • The naïve approach effectively removed sequence-dependent intensity components using control hybridization.

    Conclusions:

    • Sequence-dependent normalization offers minimal improvement in detecting enriched DNA/RNA fragments.
    • The effectiveness of the naïve approach relies on control experiments and proper intensity scaling.
    • The choice of peak detection algorithm may be more critical than normalization strategy.