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Exact computation of pattern probabilities in random sequences generated by Markov chains.

J Kleffe1, U Langbecker

  • 1Central Institute of Molecular Biology, Academy of Sciences of the GDR, Berlin Buch.

Computer Applications in the Biosciences : CABIOS
|October 1, 1990
PubMed
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We developed a rigorous algorithm to calculate probabilities of patterns in stochastic sequences using Markov chain models. This method accurately determines pattern occurrences and probabilities, improving sequence analysis.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Sequence Analysis

Background:

  • Macromolecular sequence patterns are analyzed by comparing their occurrence probabilities in random sequences.
  • Current probability calculations often lack mathematical rigor.
  • Stochastic sequence analysis is crucial for understanding biological functions.

Purpose of the Study:

  • To develop a rigorous algorithm for computing exact probabilities of patterns in stochastic sequences.
  • To apply the algorithm to analyze multiple pattern occurrences and wild-card characters.
  • To investigate nucleotide composition and pattern probabilities in the SV40 genome.

Main Methods:

  • Developed an algorithm for exact probability computation based on Markov chain models.

Related Experiment Videos

  • Applied the algorithm to calculate probabilities for single or multiple patterns (P, Q).
  • Extended the method to handle patterns with wild-card characters and calculate exact occurrence counts.

    • Main Results:

    • The algorithm provides exact probabilities for stochastic sequences following Markov chains.
    • It accurately calculates the probability of a sequence containing specific patterns, including wild-cards.
    • Analysis of the SV40 genome revealed pattern probabilities consistent with dinucleotide composition but not mononucleotide composition.

    Conclusions:

    • The developed algorithm offers a rigorous approach to sequence pattern probability calculation.
    • This method enhances the analysis of complex biological sequences and genomic data.
    • Findings on the SV40 genome highlight the importance of dinucleotide composition in nucleotide run probabilities.