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Related Experiment Videos

Computing expectation values for RNA motifs using discrete convolutions.

André Lambert1, Matthieu Legendre, Jean-Fred Fontaine

  • 1CNRS UMR 6207, Université de la Méditerranée, Luminy Case 907, 13288 Marseille cedex 9, France. lambert@cpt.univ-mrs.fr

BMC Bioinformatics
|May 17, 2005
PubMed
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Computational biologists can now accurately estimate RNA motif Expectation values (E-values) using discrete convolutions. This fast method avoids lengthy simulations for RNA motif discovery and analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Expectation values (E-values) are crucial for assessing the statistical significance of database search results in computational biology.
  • Estimating E-values for RNA motifs with variable helix spans, defined by lod-score profiles, is challenging due to non-normal score distributions.
  • Previous methods for E-value estimation required extensive simulations, limiting efficiency.

Purpose of the Study:

  • To develop an accurate and efficient method for computing E-values for RNA motifs.
  • To address the limitations of normal score distribution assumptions and lengthy simulations in E-value estimation.
  • To provide a robust method applicable to complex RNA motifs and various search algorithms.

Main Methods:

Related Experiment Videos

  • Introduction of discrete convolutions for estimating score distributions of lod-score profiles.
  • Application of the method to single-strand and helical elements, including combinations into complex motifs.
  • Validation of accuracy with and without pseudocounts in lod-score profiles.

    • Main Results:

    • Discrete convolutions provide accurate and fast estimations of score distributions for RNA motif profiles.
    • The method accurately estimates scores for individual elements and combined complex motifs.
    • Estimated score distributions are readily converted into E-values, showing good agreement with simulations.

    Conclusions:

    • The discrete convolution method offers an efficient and accurate alternative to simulations for E-value computation.
    • This approach is implemented in ERPIN software and applicable to ungapped profile searches with independent columns.
    • The findings facilitate more reliable RNA motif discovery and analysis in bioinformatics.