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Related Experiment Videos

Assessing the need for sequence-based normalization in tiling microarray experiments.

Thomas E Royce1, Joel S Rozowsky, Mark B Gerstein

  • 1Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.

Bioinformatics (Oxford, England)
|March 28, 2007
PubMed
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Sequence composition significantly impacts tiling microarray data. New normalization methods effectively address these sequence-dependent biases, improving the accuracy of identifying novel transcription and transcription factor binding sites.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Tiling microarrays offer high-resolution genomic analysis, enabling discovery of novel transcribed sequences and transcription factor binding sites.
  • Increasing probe density on microarrays necessitates accurate signal measurement at single nucleotide resolution.
  • Systematic removal of biases is crucial for maximizing the utility of tiling microarray platforms.

Purpose of the Study:

  • To investigate the influence of probe sequence composition on tiling microarray signal intensity.
  • To develop and evaluate methods for normalizing sequence-dependent biases in tiling array data.
  • To enhance the accuracy of identifying novel transcription and transcription factor binding sites using tiling microarrays.

Main Methods:

Related Experiment Videos

  • Developed three metrics to quantify sequence dependence in tiling microarray signals.
  • Evaluated existing sequence-based normalization methods using the developed metrics.
  • Applied and assessed three novel normalization techniques: linear modeling, iterative re-fitting, and sequence-space quantile normalization.

    • Main Results:

    • Probe sequence composition was found to be a significant source of bias in tiling microarray intensities.
    • Novel normalization methods demonstrated favorable performance compared to existing strategies.
    • The developed metrics effectively assessed sequence-dependent biases in array signals.

    Conclusions:

    • Sequence-based normalization is essential for accurate analysis of tiling microarray data.
    • The proposed methods improve the reliability of identifying novel transcripts and transcription factor binding sites.
    • Advancements in normalization techniques are critical for achieving single nucleotide resolution in genomic studies.