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A probabilistic model of local sequence alignment that simplifies statistical significance estimation.

Sean R Eddy1

  • 1Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, United States of America. eddys@janelia.hhmi.org

Plos Computational Biology
|June 3, 2008
PubMed
Summary

Accurate statistical significance estimation in sequence alignment is crucial. This study shows optimal (Viterbi) and probabilistic (Forward) alignment scores have predictable distributions, simplifying statistical evaluations.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Modeling

Background:

  • Accurate statistical significance estimation is vital for sequence database searches.
  • Optimal local sequence alignment scores follow Gumbel distributions, but parameter estimation (lambda) is computationally intensive.
  • Probabilistic scores (Forward scores) offer greater power than optimal alignment scores, but their score distributions are unknown.

Purpose of the Study:

  • To conjecture and validate simple, predictable score distributions for probabilistic local sequence alignment.
  • To enable efficient and accurate determination of expectation values (E-values) for both Viterbi and Forward scores.

Main Methods:

  • Utilized full probabilistic modeling methods for local sequence alignment.
  • Conducted extensive simulation studies using 9,318 profile-hidden Markov models from the Pfam database.
  • Compared Viterbi and Forward alignment scores against established distributions.

Main Results:

  • Conjectured and demonstrated that optimal alignment (Viterbi) scores are Gumbel-distributed with a constant lambda = log 2.
  • Showed that the high-scoring tail of Forward scores follows an exponential distribution with the same constant lambda.
  • Validated these findings across a wide range of profile/sequence comparisons.

Conclusions:

  • Established predictable statistical distributions for both Viterbi and Forward scores in probabilistic local alignments.
  • The findings simplify and improve the accuracy of statistical significance estimation (E-values) in sequence analysis.
  • Enables more efficient and reliable interpretation of sequence alignment results in large databases.