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DNA Microarrays02:34

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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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Evolving DNA motifs to predict GeneChip probe performance.

Wb Langdon1, Ap Harrison

  • 1Department of Computer Science, King's College London, Strand, London, WC2R 2LS, UK. wlangdon@essex.ac.uk

Algorithms for Molecular Biology : AMB
|March 21, 2009
PubMed
Summary

This study introduces a method to assess probe quality on Affymetrix High Density Oligonucleotide Arrays (HDONA) using gene expression correlations. Automatically generated DNA sequence motifs improve prediction of poor DNA sequences.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Affymetrix High Density Oligonucleotide Arrays (HDONA) measure thousands of genes using millions of probes.
  • Assessing probe quality is crucial for reliable gene expression analysis.
  • Existing methods for probe quality assessment can be labor-intensive.

Purpose of the Study:

  • To develop an automated method for evaluating the quality of individual probes on HG-U133A arrays.
  • To leverage large-scale gene expression data for probe quality assessment.
  • To compare the performance of automatically generated sequence motifs against human-designed ones.

Main Methods:

  • Utilized gene expression correlation analysis across 6685 human tissue samples from the NCBI GEO database.
  • Employed strongly typed genetic programming to generate regular expressions from a Backus-Naur form (BNF) context-free grammar.
  • Correlated probe measurements for the same gene to identify low-quality probes.

Main Results:

  • Identified that low correlation between measurements for the same gene indicates a poor probe.
  • Successfully generated regular expressions (REs) automatically from a context-free grammar.
  • The automatically produced motif demonstrated superior performance in predicting poor DNA sequences compared to a human-generated RE.

Conclusions:

  • Automated probe quality assessment using gene expression correlations is feasible and effective.
  • Automatically generated DNA sequence motifs can outperform human-curated motifs.
  • The findings suggest that runs of Cytosine and Guanine, and their mixtures, should be avoided in DNA sequences for improved quality.