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Search algorithm for pattern match analysis of nucleic acid sequences.

R Harr, M Häggström, P Gustafsson

    Nucleic Acids Research
    |May 11, 1983
    PubMed
    Summary
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    A novel pattern-matching algorithm identifies genetic information in nucleic acid sequences by analyzing base composition. This bioinformatics tool accurately detects biological patterns like promoters and splice sites in DNA sequences.

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Genetic information is encoded in nucleic acid sequences.
    • Identifying functional elements like promoters and splice sites is crucial for understanding gene regulation and function.
    • Statistical patterns of nucleotide bases within DNA sequences can indicate functional regions.

    Purpose of the Study:

    • To develop a new pattern-match type search algorithm for identifying biological information in nucleic acid sequences.
    • To leverage the statistical occurrence of bases to detect functional DNA elements.
    • To evaluate the algorithm's efficacy in identifying known biological sequences.

    Main Methods:

    • Developed a pattern-match algorithm analyzing the statistical occurrence of four bases (A, T, C, G) in nucleic acid sequences.

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  • Compared known statistical patterns of functional elements (e.g., promoters) against unknown sequences.
  • Calculated the statistical significance of matches at all positions within the target sequence.

    • Main Results:

    • The algorithm successfully identified 44 out of 54 published prokaryotic promoters with high accuracy.
    • It detected all functional promoters (tet, anti-tet, p4, bla, ori) in plasmid pBR322, except for p5.
    • Five out of six potential donor and acceptor splice sites were identified in a human HLA genomic sequence.

    Conclusions:

    • The developed algorithm is effective for identifying functional elements in nucleic acid sequences.
    • It demonstrates significant potential for applications in genomics and bioinformatics research.
    • The algorithm shows promise for accurate detection of promoters and splice sites across different organisms and sequence types.