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Rapid significance estimation in local sequence alignment with gaps.

Ralf Bundschuh1

  • 1Department of Physics, Ohio State University, Columbus, OH 43210, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 23, 2002
PubMed
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Estimating the distribution of random sequence alignment scores is vital for assessing significance. This study introduces a faster algorithm for Gumbel parameter estimation, making significance testing interactive.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Analysis

Background:

  • Assessing the statistical significance of sequence alignments requires understanding the distribution of alignment scores for random sequences.
  • The distribution of scores for gapped local alignments follows a Gumbel distribution.
  • Determining the parameters of this Gumbel distribution is computationally intensive using traditional methods.

Purpose of the Study:

  • To develop a more efficient algorithmic approach for estimating Gumbel distribution parameters in sequence alignment.
  • To accelerate the process of significance estimation for sequence alignments.

Main Methods:

  • A novel algorithmic approach for estimating key Gumbel parameters was developed.
  • The new algorithm's performance was compared against traditional methods.

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Main Results:

  • The new algorithm estimates important Gumbel parameters at least five times faster than traditional methods.
  • Runtime analysis showed the algorithm completes estimations in under a minute on a workstation.
  • This speed improvement facilitates interactive applications for significance estimation.

Conclusions:

  • The developed algorithm significantly enhances the speed of Gumbel parameter estimation for sequence alignment significance testing.
  • This computational efficiency makes interactive significance estimation feasible.
  • The findings contribute to faster and more accessible bioinformatics analyses.