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Related Experiment Videos

A graph-based approach to systematically reconstruct human transcriptional regulatory modules.

Xifeng Yan1, Michael R Mehan, Yu Huang

  • 1IBM T. J. Watson Research Center, Hawthorne, NY, USA.

Bioinformatics (Oxford, England)
|July 25, 2007
PubMed
Summary
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Identifying transcription regulatory modules is crucial for understanding gene regulation. This study introduces a graph-based data-mining approach to find frequently coexpressed gene clusters across multiple microarray datasets, improving the accuracy of identifying these modules.

Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Reconstructing transcription regulatory modules is key to studying gene regulation.
  • Coexpression clusters from single microarray datasets often yield false positives.
  • Genes coexpressed across multiple datasets are more likely to form true transcription modules.

Purpose of the Study:

  • To develop an efficient and systematic method for identifying frequent coexpression clusters across diverse microarray datasets.
  • To improve the accuracy of transcription module reconstruction by leveraging data from multiple experimental conditions.
  • To provide a computational tool for analyzing large-scale gene expression data.

Main Methods:

  • Modeled each microarray dataset as a coexpression graph.

Related Experiment Videos

  • Developed a graph-mining approach to find frequently densely connected vertex sets across a subset of datasets.
  • Utilized graph partitioning and a Neighbor Association Summary Graph to optimize the search space.
  • Applied the method to 105 human microarray datasets.

    • Main Results:

    • Identified a large number of potential transcription modules activated under various conditions.
    • Demonstrated that the recurrence of coexpression clusters significantly increases their likelihood of being a true transcription module.
    • Validated findings using ChIP-chip data, confirming the method's effectiveness.

    Conclusions:

    • The proposed graph-based data-mining approach efficiently reconstructs transcription regulatory modules.
    • This method effectively exploits accumulated microarray data for gene regulation studies.
    • The algorithm is adaptable for approximate network module mining in other biological networks.