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A cis-regulatory logic simulator.

Robert D Zeigler1, Jason Gertz, Barak A Cohen

  • 1Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA. rdzeigle@wustl.edu <rdzeigle@wustl.edu>

BMC Bioinformatics
|July 31, 2007
PubMed
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We developed a gene expression simulator to generate test data for computational gene regulation studies. This tool aids in developing and comparing methods for predicting gene expression from promoter sequences.

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Predicting gene expression from promoter sequences is a key goal in computational gene regulation.
  • Developing computational methods is hindered by the lack of validated cis-regulatory interaction data.
  • There is a need for reliable test expression data with known cis-regulatory interactions.

Purpose of the Study:

  • To develop a gene expression simulator for generating test data.
  • To facilitate the development and comparison of computational methods for gene regulation.

Main Methods:

  • Developed a gene expression simulator that models user-defined cis-regulatory site interactions.
  • Incorporated additive, cooperative, competitive, and synergistic interactions.

Related Experiment Videos

  • Allowed for constraints on element spacing, distance, orientation, and added Gaussian noise.
  • Included a data transformation to simulate real promoter expression shapes.

    • Main Results:

    • The simulator generates expression data based on specified cis-regulatory interactions.
    • Simulated data showed good agreement with predicted regulatory modules from real expression data.
    • Presented useful data sets for testing new gene expression prediction methodologies.

    Conclusions:

    • A flexible gene expression simulator was developed, rapidly generating simulated promoters and transcriptional output.
    • The simulator's data accurately reproduces experimental data when appropriate rules are applied.
    • Simulated data sets will enable direct comparison of computational strategies for gene expression prediction.