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Combining sequence and time series expression data to learn transcriptional modules.

Anshul Kundaje1, Manuel Middendorf, Feng Gao

  • 1Department of Computer Science, Columbia University, New York 10027, USA. abk2001@cs.columbia.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 19, 2006
PubMed
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This study introduces a novel generative probabilistic model to cluster genes into transcriptional modules by integrating gene expression and motif data. The method effectively identifies gene sets with coordinated expression and regulatory elements, validated in yeast cell cycle studies.

Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Clustering genes into transcriptional modules aids understanding of gene regulation.
  • Integrating diverse data types like gene expression and motif data can improve module discovery.

Purpose of the Study:

  • To develop a generative probabilistic model for clustering genes into transcriptional modules.
  • To combine time series gene expression data and genome-wide motif data for enhanced module identification.
  • To associate regulatory elements with identified gene modules.

Main Methods:

  • A generative probabilistic model using expectation maximization was developed.
  • The model integrates time series gene expression profiles and motif occurrence counts in promoter regions.

Related Experiment Videos

  • Statistical splines were used to model the smooth dependence of expression on time.
  • Jensen-Shannon entropy was employed to identify significant motifs associated with each module.

    • Main Results:

    • The algorithm successfully clustered genes into modules with coherent expression patterns and similar motif compositions.
    • The model demonstrated interpretability, showing contributions from both sequence and expression data.
    • Validation on yeast cell cycle data identified modules linked to known cell cycle transcription factors.
    • The method effectively interpolates between sequence and expression data for module assignment.

    Conclusions:

    • The developed model provides a robust framework for discovering transcriptional modules by integrating gene expression and motif data.
    • This approach enhances the understanding of gene regulatory mechanisms.
    • The method is applicable to various organisms and experimental conditions with available time series expression and motif data.