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Related Experiment Videos

Quantifying the relationship between co-expression, co-regulation and gene function.

Dominic J Allocco1, Isaac S Kohane, Atul J Butte

  • 1Informatics Program, Children's Hospital, Boston, MA, USA. allocco@chip.org

BMC Bioinformatics
|April 1, 2004
PubMed
Summary
This summary is machine-generated.

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Genes with similar mRNA expression and function are likely co-regulated. High expression similarity (correlation > 0.84) increases the chance of sharing a common transcription factor, improving gene regulatory mechanism prediction.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Similar mRNA expression and function suggest shared gene regulation.
  • Quantifying shared regulatory mechanisms across genes has been challenging.
  • Previous methods lacked a general approach for large-scale analysis.

Purpose of the Study:

  • To quantify the likelihood of genes sharing common regulatory mechanisms.
  • To investigate the relationship between gene expression similarity, function, and transcription factor binding.
  • To develop a predictive model for co-regulated genes.

Main Methods:

  • Utilized genome-wide binding analysis data.
  • Integrated mRNA expression profiles from 611 microarrays.
  • Incorporated existing functional gene annotations.

Related Experiment Videos

  • Developed a probabilistic model to assess shared transcription factor binding.

    • Main Results:

    • Strongly correlated mRNA expression profiles predict shared transcription factor binding.
    • High expression similarity (correlation > 0.84) is required for >50% chance of shared binding.
    • Genes with similar functions are also more likely to share transcription factor binders.
    • Combining expression and functional data yielded a superior predictive model.

    Conclusions:

    • mRNA expression and functional annotations can estimate the probability of shared gene regulatory mechanisms.
    • Current data sources identify only a subset of co-regulated genes.
    • This approach provides a quantitative framework for understanding gene regulation.