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Related Experiment Videos

Large-scale gene expression data analysis: a new challenge to computational biologists.

M Q Zhang1

  • 1Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA. mzhang@cshl.org

Genome Research
|August 14, 1999
PubMed
Summary

High-density DNA arrays enable genome-wide gene expression monitoring in yeast. Analyzing this large-scale data presents a significant challenge for computational biologists seeking functional insights.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Computational Biology

Background:

  • High-density DNA arrays offer a powerful tool for genome-wide gene expression analysis.
  • Microarray technology allows interrogation of the expression patterns of thousands of genes simultaneously.
  • Saccharomyces cerevisiae (yeast) serves as a model organism for studying fundamental biological processes.

Purpose of the Study:

  • To review recent experiments utilizing DNA arrays for transcriptional regulation studies.
  • To highlight the challenges in extracting functional information from large-scale gene expression datasets.
  • To discuss the role of computational biology in analyzing complex genomic data.

Main Methods:

  • Utilizing high-density DNA arrays for genome-wide gene expression profiling.

Related Experiment Videos

  • Hybridizing complementary DNAs (cDNAs) to arrays representing the entire yeast genome.
  • Analyzing data from three recent experimental surveys focused on transcriptional regulation.

    • Main Results:

    • Demonstration of DNA arrays as a fundamental tool for advancing biological research.
    • Interrogation of expression patterns for approximately 6000 genes in Saccharomyces cerevisiae.
    • Identification of significant challenges in computational analysis of large-scale gene expression data.

    Conclusions:

    • Large-scale gene expression data from DNA arrays is revolutionizing biology.
    • Extracting meaningful functional information from such data requires sophisticated computational approaches.
    • Computational biologists face a critical challenge in interpreting complex genomic datasets.