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Related Experiment Videos

Pattern recognition in genetic sequences.

P H Sellers1

  • 1The Rockefeller University, New York, New York 10021.

Proceedings of the National Academy of Sciences of the United States of America
|July 1, 1979
PubMed
Summary

This study introduces a novel algorithm to identify pattern similarities in biological sequences using evolutionary distances. The method efficiently finds maximum local similarity between sequence pairs, aiding in computer-based sequence analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Identifying similarities in biological sequences is crucial for understanding molecular function and evolution.
  • Existing methods may not efficiently detect local similarities across evolutionary distances.

Purpose of the Study:

  • To develop and present an algorithm for detecting pattern similarities between two finite biological sequences.
  • To provide a method for finding pairs of intervals with maximum local similarity based on evolutionary distances.

Main Methods:

  • The algorithm operates in the metric space of evolutionary distances.
  • It identifies all pairs of intervals (one from each sequence) exhibiting maximum local similarity.
  • The computational complexity is on the order of mn, where m and n are sequence lengths.

Main Results:

  • A new algorithm for sequence similarity detection is announced.
  • The algorithm efficiently computes local similarity scores between segments of biological sequences.
  • It provides a comprehensive list of maximally similar interval pairs.

Conclusions:

  • The developed algorithm offers an effective computational approach for detecting similarities in biological sequences.
  • This method can be applied to analyze protein and nucleic acid sequences.
  • It enhances the capabilities of computer-aided biological sequence comparison.

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