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Applicability of the multiple alignment algorithm for detection of weak patterns: periodically distributed DNA

A Bolshoy1, I Ioshikhes, E N Trifonov

  • 1Department of Membranes Research and Biophysics, Weizmann Institute of Science, Rehovot, Israel. bmbolsho@dapsas1.weizmann.ac.il

Computer Applications in the Biosciences : CABIOS
|October 1, 1996
PubMed
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This study verifies a multiple sequence alignment algorithm for detecting weak DNA patterns. The method successfully identifies nucleosome DNA positioning patterns, even with minimal signal in a large dataset.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Nucleosome DNA positioning patterns are inherently weak and difficult to detect.
  • Existing alignment algorithms struggle with the low pattern/background ratio characteristic of these patterns.
  • Verification of the efficiency and sensitivity of these alignment procedures is crucial.

Purpose of the Study:

  • To present and verify a novel method for validating a multiple sequence alignment algorithm.
  • To assess the algorithm's ability to detect weak, degenerated DNA patterns.
  • To confirm the algorithm's effectiveness in identifying nucleosome DNA positioning patterns.

Main Methods:

  • Developed a simulation approach to generate a database of sequences with a hidden periodic pattern.

Related Experiment Videos

  • The pattern was simulated as weak oscillations of AA and TT dinucleotide occurrences.
  • Applied a statistical multicycle alignment procedure to extract the hidden pattern from the simulated database.

    • Main Results:

    • The alignment procedure successfully recovered the simulated nucleosome DNA positioning pattern with high accuracy.
    • The method demonstrated robustness, detecting the pattern even when only a few signal dinucleotides were present per sequence.
    • A collection of 204 sequences was sufficient to detect the hidden pattern, with as few as three dinucleotides per sequence indicating the signal.

    Conclusions:

    • The statistical multicycle alignment algorithm is effective and sensitive for detecting weak DNA patterns like nucleosome positioning.
    • The simulation method provides a reliable way to verify the performance of sequence alignment algorithms.
    • This approach enhances the ability to identify subtle biological patterns within large genomic datasets.