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Related Experiment Videos

Visualizing associations between genome sequences and gene expression data using genome-mean expression profiles.

D Y Chiang1, P O Brown, M B Eisen

  • 1Dept. of Molecular and Cell Biology, U. of California, Berkeley, CA 94720, USA.

Bioinformatics (Oxford, England)
|July 27, 2001
PubMed
Summary
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This study introduces genome-mean expression profiles (GMEPs) to analyze sequence motifs and gene expression. GMEPs reveal relationships between DNA sequences and transcriptional regulation, aiding in the identification of transcription factor binding sites.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Understanding transcriptional regulation is crucial for deciphering gene expression.
  • Existing methods often identify sequence motifs based on co-expressed genes.
  • Integrating genome sequences with expression data offers new insights.

Purpose of the Study:

  • To develop an alternative approach for evaluating sequence motifs' transcriptional information.
  • To introduce genome-mean expression profiles (GMEPs) for analyzing sequence-expression relationships.
  • To characterize the transcriptional importance of specific sequence motifs.

Main Methods:

  • Computing genome-mean expression profiles (GMEPs) for sequence motifs.
  • Analyzing GMEPs from 519 whole-genome microarray experiments in Saccharomyces cerevisiae.

Related Experiment Videos

  • Utilizing hierarchical clustering of GMEPs to group motifs.

    • Main Results:

    • A significant correlation was found between GMEPs of reverse complementary motifs.
    • Hierarchical clustering identified motif clusters corresponding to known transcription factor binding sites.
    • GMEP patterns reflected the known activities of transcription factors.

    Conclusions:

    • GMEPs are valuable for visualizing sequence-expression relationships and assessing motif importance.
    • The approach supports the link between GMEPs and transcriptional regulation.
    • GMEPs aid in identifying and characterizing transcription factor binding sites.