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DNAMAT: an efficient graphic matrix sequence homology algorithm and its application to structural analysis.

R Unger1, D Harel, J L Sussman

  • 1Department of Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.

Computer Applications in the Biosciences : CABIOS
|December 1, 1986
PubMed
Summary
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We developed a fast algorithm for sequence homology graphic matrix representation, improving speed and utility for DNA and RNA analysis. This method aids in identifying common structural features across related sequences.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence homology analysis is crucial for understanding biological relationships.
  • Existing methods for graphic matrix representation can be computationally intensive.

Purpose of the Study:

  • To present a fast algorithm for generating graphic matrix representations of sequence homology.
  • To introduce an extension for analyzing multiple related DNA or RNA sequences to identify common structural features.

Main Methods:

  • The core algorithm utilizes lexicographical ordering of sequence fragments for efficient matrix generation.
  • The DNAMAT program, based on this algorithm, has undergone extensive testing.
  • A novel approach involves 'summing' multiple dot-matrices to reveal consensus structural elements.

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Main Results:

  • The algorithm significantly increases speed compared to naive methods while retaining essential functionalities.
  • The DNAMAT program has been validated over three years of use, proving its utility.
  • The extension method successfully identified common structural features in transfer RNA (tRNA) sequences.

Conclusions:

  • The presented algorithm offers a computationally efficient tool for sequence homology visualization.
  • The extension provides a powerful method for comparative analysis of nucleic acid sequences.
  • This approach facilitates the discovery of conserved structural patterns in biological sequences.