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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

Significance of gapped sequence alignments.

Lee A Newberg1

  • 1Center for Bioinformatics, Wadsworth Center, New York State Department of Health, Albany, New York 12201-0509, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 1, 2008
PubMed
Summary

Accurately calculating the statistical significance of extreme sequence alignment scores is challenging. This study introduces an importance-sampling method for precise p-value approximation in biological sequence alignment.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Modeling

Background:

  • Statistical significance of extreme sequence alignment scores is crucial for biological applications.
  • Current methods for approximating these p-values are often difficult and imprecise.
  • Accurate assessment is vital for interpreting biological sequence similarity.

Purpose of the Study:

  • To develop a precise method for approximating the statistical significance of extreme sequence alignment scores.
  • To apply this novel technique to local sequence alignment of nucleic acid and amino acid sequences.
  • To investigate the distribution of extreme value statistics in local alignment models.

Main Methods:

  • Utilizing importance-sampling probability distributions to draw random samples.
  • Applying the technique to pairwise local sequence alignment for sequences up to 1000 in length.
  • Employing the BLOSUM62 scoring system for amino acid sequence alignment.

Main Results:

  • Computed an extremely low p-value (3.4 +/- 0.3) x 10(-1314) for a local alignment score of 6000 between two 1000-length sequences using BLOSUM62.
  • Demonstrated that the extreme value significance statistic for the examined local alignment model does not follow a Gumbel distribution.
  • Developed a web server for accessible application of the method.

Conclusions:

  • The importance-sampling approach provides a precise method for calculating extreme sequence alignment score significance.
  • The findings challenge assumptions about the distribution of extreme value statistics in local alignment.
  • This work offers a valuable tool for bioinformatics research and applications.