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Statistical significance of normalized global alignment.

Guillermo Peris1, Andrés Marzal

  • 1Department de Llenguatges i Sistemes Informátics, Universitat Jaume I , Castelló, Spain .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 10, 2014
PubMed
Summary

Normalized global alignment offers a computationally efficient method for protein sequence comparison. Its scores follow a log-normal distribution, enabling accurate statistical significance assessment for evolutionary and functional analyses.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Comparing homologous proteins across species aids in function assignment and evolutionary reconstruction.
  • Global alignment is crucial for multiple sequence alignments and phylogenetic tree construction, but its statistical significance is often unclear or computationally intensive.
  • Existing methods like Z-score for assessing global alignment significance are computationally expensive.

Purpose of the Study:

  • To analyze the statistical significance of a novel normalized global alignment method.
  • To establish a theoretical basis for normalized global alignment's effectiveness in protein sequence comparison.
  • To provide a computationally efficient and statistically sound approach for protein alignment.

Main Methods:

  • Introduced a normalized global alignment definition balancing alignment cost and length.
  • Evaluated normalized global alignment performance against the Z-score method using SCOP ASTRAL database.
  • Analyzed the distribution of normalized global alignment scores for unrelated proteins.

Main Results:

  • Normalized global alignment achieved better classification results than Z-score at a significantly lower computational cost.
  • Normalized global alignment scores for unrelated proteins were found to fit a log-normal distribution.
  • This log-normal distribution allows for the derivation of statistical significance for normalized global alignments.

Conclusions:

  • Normalized global alignment is a statistically sound and computationally efficient algorithm for protein sequence alignment.
  • The log-normal distribution of scores provides a robust method for determining statistical significance.
  • This approach enhances the utility of global alignment in functional and evolutionary studies of proteins.