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Finding regulatory elements using joint likelihoods for sequence and expression profile data.

I Holmes1, W J Bruno

  • 1Theoretical Biology & Biophysics, Los Alamos National Laboratory, NM 87545, USA. ihh@fruitfly.org

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|September 8, 2000
PubMed
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This study introduces a unified computational approach to identify gene promoter sequences and improve gene clustering simultaneously. The developed kimono program offers a novel method for analyzing sequence-expression data.

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Gene promoter identification often relies on clustering gene expression data, assuming prior clustering accuracy.
  • This approach has limitations as incorrect clustering can hinder accurate promoter discovery.

Purpose of the Study:

  • To develop a unified computational framework for simultaneously identifying promoter sequences and improving gene clustering.
  • To present a novel sequence-expression model that jointly analyzes sequence motifs and gene expression levels.

Main Methods:

  • Developed a likelihood function for a "sequence-expression" model.
  • Employed Gibbs sampling and Expectation/Maximization algorithms for parameter estimation.
  • Created a software program named kimono to implement the developed algorithm.

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Main Results:

  • The study presents a novel sequence-expression model for joint analysis of promoter sequences and gene expression.
  • An algorithm using Gibbs sampling and Expectation/Maximization is described for parameter estimation.
  • A freely available software, kimono, has been developed to implement this approach.

Conclusions:

  • A unified approach to promoter discovery and gene clustering offers theoretical advantages.
  • The kimono program provides a new computational tool for analyzing sequence-expression data.
  • This method has the potential to improve the accuracy of both promoter identification and gene clustering.