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Co-expression pattern from DNA microarray experiments as a tool for operon prediction.

Chiara Sabatti1, Lars Rohlin, Min-Kyu Oh

  • 1Department of Human Genetics and Statistics and Department of Chemical Engineering, University of California, Los Angeles, CA 90095, USA.

Nucleic Acids Research
|June 28, 2002
PubMed
Summary
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Gene expression data can refine operon predictions in Escherichia coli (E. coli). Analyzing co-expression correlations improves operon identification, but experimental design is crucial for sufficient information.

Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Operons are fundamental transcriptional units in prokaryotes, crucial for understanding gene regulation.
  • Predicting operons using sequence information (e.g., intergenic distances) is a primary step in reconstructing genomic regulatory networks.
  • Existing sequence-based operon predictions require experimental validation and refinement.

Purpose of the Study:

  • To investigate the utility of gene expression array data for improving and validating operon predictions in E. coli.
  • To assess the extent to which microarray data can refine sequence-based operon predictions.
  • To identify optimal experimental designs for operon prediction using gene expression data.

Main Methods:

  • Utilized a large collection of published E. coli microarray datasets.

Related Experiment Videos

  • Employed a Bayesian classification scheme based on the correlation of expression ratios between adjacent genes.
  • Analyzed co-expression correlations and mapped them onto the E. coli genome.

    • Main Results:

    • Gene expression data significantly refined sequence-based operon predictions for genes with substantial expression level changes across experiments.
    • Co-expression correlations were mapped to provide a genomic view of potential operons.
    • A significant portion of gene pairs lacked sufficient information for operon determination due to experimental design limitations.

    Conclusions:

    • Microarray data, particularly from experiments causing global gene expression changes, can substantially improve operon prediction accuracy.
    • The effectiveness of gene expression data for operon prediction is highly dependent on the experimental design.
    • Future expression studies should prioritize conditions that induce broad changes in gene expression to maximize information for operon prediction.