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Data bank homology search algorithm with linear computation complexity

V B Strelets1, A A Ptitsyn, L Milanesi

  • 1Supercomputer Computations Research Institute, Florida State University, Tallahassee 32306-4052.

Computer Applications in the Biosciences : CABIOS
|June 1, 1994
PubMed
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A novel algorithm enhances data bank homology searches with linear complexity and high sensitivity. This method efficiently identifies local sequence similarities in large biological databases.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Homology searching is crucial for understanding protein function and evolution.
  • Existing algorithms often face challenges with computational complexity and memory usage for large datasets.

Purpose of the Study:

  • To introduce a new algorithm for efficient data bank homology searching.
  • To address limitations of existing methods regarding speed, memory, and sensitivity.

Main Methods:

  • The algorithm calculates k-tuple realization matrices for query sequences.
  • It searches for matching k-tuples within a restricted range in database sequences.
  • The method avoids extensive k-tuple coordinate tabulation and in-memory database storage.

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Main Results:

  • The algorithm demonstrates linear computation complexity and low memory requirements.
  • It exhibits high sensitivity in detecting local region homology.
  • Testing on PIR and GenBank databases yielded positive results, with potential for selectivity improvements.

Conclusions:

  • The developed algorithm offers a computationally efficient and sensitive approach for homology searching.
  • Its practical implementation on personal computers makes it accessible for biological data analysis.
  • The method shows promise for identifying homologous sequences, exemplified by the HOX 3.1 protein search.