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Related Experiment Videos

Statistics of large-scale sequence searching

R Spang1, M Vingron

  • 1Deutsches Krebsforschungszentrum (DKFZ), Theoretische Bioinformatik, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.

Bioinformatics (Oxford, England)
|June 6, 1998
PubMed
Summary
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Statistical significance of database search scores is improved by accounting for database properties. A new semi-random model with an "effective database size" parameter corrects discrepancies in p-value computations for sequence similarity searches.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Standard sequence alignment tools like BLAST and FASTA rely on statistical significance of similarity scores.
  • Accurate p-value computation is challenging in database searches due to multiple comparisons and database characteristics.
  • Existing models often assume purely random data, failing to capture real-world database complexities.

Purpose of the Study:

  • To address the limitations of current statistical models for sequence database searches.
  • To improve the accuracy of p-value calculations for similarity scores in large biological databases.
  • To introduce a more realistic statistical framework for evaluating search results.

Main Methods:

  • Extensive simulations of database searches were performed on the SWISS-PROT protein database (Release 31.0).

Related Experiment Videos

  • A novel semi-random statistical model was developed to better represent real databases.
  • The model incorporates an "effective database size" parameter to account for database-specific statistical properties.

    • Main Results:

    • A discrepancy was observed between theoretical predictions and empirical distributions of similarity scores.
    • The proposed semi-random model demonstrated improved accuracy in p-value computation compared to purely random models.
    • The "effective database size" parameter effectively captures database-specific statistical properties.

    Conclusions:

    • The developed semi-random model provides a more accurate assessment of statistical significance for database search results.
    • Accounting for database properties like sequence length distribution and repeated patterns is crucial for reliable p-value estimation.
    • This approach enhances the credibility of findings from large-scale sequence similarity searches.