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On the significance of sequence alignments when using multiple scoring matrices.

Florian Frommlet1, Andreas Futschik, Malgorzata Bogdan

  • 1Department of Medical Statistics, University of Vienna, Vienna, Austria. Florian.Frommlet@univie.ac.at

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Using multiple scoring matrices in sequence alignment can overestimate significance. This study proposes accurate corrections for p-values and E-values, accounting for the simultaneous use of various matrices like PAM and BLOSUM.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Pairwise local sequence alignment is crucial for searching biological databases.
  • Scoring matrices (e.g., PAM, BLOSUM) are essential for assessing alignment significance, typically evaluated by E-values and p-values.
  • Current methods often overlook the impact of using multiple scoring matrices, potentially overestimating alignment significance.

Purpose of the Study:

  • To investigate the multiple testing problem arising from using several scoring matrices in local sequence alignment.
  • To evaluate the accuracy of a simple Bonferroni correction for p-values.
  • To develop a more precise correction method for significance values when multiple matrices are employed.

Main Methods:

  • Extensive simulations were conducted to study the multiple testing problem.
  • A Bonferroni correction for p-values was analyzed for its accuracy.
  • Extreme value distributions were fitted to the maximum normalized scores from different matrices to propose a new correction.

Main Results:

  • The simultaneous use of multiple scoring matrices can lead to a considerable overestimation of alignment significance.
  • A simple Bonferroni correction shows limitations in accuracy for this specific problem.
  • The proposed correction method, based on extreme value distributions, offers a more accurate adjustment for p- and E-values.

Conclusions:

  • Significance values in sequence alignment are inflated when multiple scoring matrices are used without correction.
  • The developed correction factors provide a practical way to adjust reported p- and E-values from software like BLAST.
  • Accurate significance assessment is vital for reliable biological sequence analysis.