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LD-SPatt: large deviations statistics for patterns on Markov chains.

G Nuel1

  • 1Laboratoire Statistique et Génome, Tour Evry 2, 523 place des terasses, 91034 Evry, France. nuel@genopole.cnrs.fr

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 22, 2005
PubMed
Summary

Large deviations theory offers a more reliable method for analyzing patterns in biological sequences than Gaussian approximations. This new approach, implemented in LD-SPatt software, excels in tail distribution events.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Markov chains are crucial for analyzing biological sequence patterns.
  • Current statistical methods, like Central Limit Theorem (CLT) approximations, struggle with tail distribution events.
  • Accurate statistical assessment is vital for identifying biologically significant patterns.

Purpose of the Study:

  • To introduce a novel approach for pattern statistics in biological sequences using large deviations theory.
  • To address the limitations of Gaussian approximations in analyzing tail distribution events.
  • To provide a reliable computational tool for pattern discovery.

Main Methods:

  • Recalled theoretical results for level 1 (empiric mean) and level 2 (empiric distribution) large deviations on Markov chains.

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  • Developed and applied algorithms based on large deviations theory, focusing on numerical implementation.
  • Implemented the algorithms in GPL software named LD-SPatt.

    • Main Results:

    • Large deviations theory provides more reliable pattern statistics compared to Gaussian approximations, both in absolute values and ranking.
    • The proposed method is at least as reliable as compound Poisson approximations.
    • LD-SPatt software demonstrates the practical application and efficiency of the large deviations approach.

    Conclusions:

    • Large deviations theory offers a superior alternative to Gaussian approximations for pattern statistics in biological sequences, especially in tail events.
    • The LD-SPatt software provides a reliable and efficient tool for researchers.
    • Further improvements and applications of this method in biological sequence analysis are promising.