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Extracting information from cDNA arrays.

Hanspeter Herzel1, Dieter Beule, Szymon Kielbasa

  • 1Institute for Theoretical Biology, Humboldt-University, Invalidenstr. 43, D-10115 Berlin, Germany.

Chaos (Woodbury, N.Y.)
|June 5, 2003
PubMed
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This study quantifies gene expression measurement reproducibility using DNA arrays, finding high correlation (0.98). It also explores clustering algorithms for identifying co-regulated genes and transcription factor binding sites.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • High-density DNA arrays enable simultaneous measurement of thousands of gene expression levels.
  • Monitoring gene expression profiles is crucial for understanding cellular processes.

Purpose of the Study:

  • To quantify the reproducibility of gene expression measurements using spotted cDNA arrays.
  • To analyze the impact of preprocessing, distance measures, and clustering algorithms on gene expression time series clustering.
  • To discuss algorithms for identifying transcription factor binding sites from co-regulated gene clusters.

Main Methods:

  • Utilized spotted cDNA arrays for gene expression profiling.
  • Employed a dilution series of a mouse liver probe to assess measurement reproducibility.

Related Experiment Videos

  • Applied various preprocessing techniques, distance measures, and clustering algorithms to human fibroblast gene expression time series data.

    • Main Results:

    • Achieved correlation coefficients of 0.98 for both repeated measurements on the same filter and filter-to-filter comparisons.
    • Identified saturation effects at high intensities and noise/outshining at low intensities as key limitations.
    • Evaluated the influence of different analytical approaches on the clustering of co-regulated genes.

    Conclusions:

    • Spotted cDNA arrays offer reproducible gene expression measurements, with high correlation coefficients.
    • The choice of preprocessing, distance measure, and clustering algorithm significantly impacts the identification of co-regulated genes.
    • The study lays groundwork for identifying transcription factor binding sites through analysis of gene expression clusters.