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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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Pairwise statistical significance of local sequence alignment using sequence-specific and position-specific

Ankit Agrawal1, Xiaoqiu Huang

  • 1Department of Computer Science, Iowa State University, 226 Atanasoff Hall, Ames, IA 50011-1041, USA. ankitag@iastate.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|November 13, 2010
PubMed
Summary

This study enhances pairwise sequence alignment by using sequence-specific and position-specific matrices for statistical significance estimation. This approach offers improved accuracy over standard methods and database significance for bioinformatics applications.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Pairwise sequence alignment is fundamental in bioinformatics, forming the basis for numerous applications.
  • Relatedness of sequences is typically assessed by statistical significance rather than raw alignment scores.
  • Recent work suggests pairwise statistical significance offers a more sequence-specific and database-independent alternative for estimating alignment score significance.

Purpose of the Study:

  • To improve pairwise statistical significance estimation by incorporating more sequence-specific information.
  • To evaluate the performance of sequence-specific and position-specific substitution matrices in estimating pairwise statistical significance.

Main Methods:

  • Utilized sequence-specific and position-specific substitution matrices to derive pairwise statistical significance estimates.
  • Conducted experiments on a benchmark database comparing different levels of sequence-specific contribution.
  • Compared results against standard matrices (e.g., BLOSUM62) and popular database search programs (BLAST, PSI-BLAST, SSEARCH).

Main Results:

  • Sequence-specific substitution matrices significantly improved pairwise statistical significance estimation compared to standard matrices and database significance methods (BLAST, SSEARCH).
  • PSI-BLAST with pretrained Position-Specific Scoring Matrices (PSSMs) outperformed the proposed methods when PSSMs were available.
  • Position-specific substitution matrices yielded significantly better results than even PSI-BLAST with pretrained PSSMs.

Conclusions:

  • Sequence-specific and position-specific matrices offer a more accurate approach to estimating pairwise statistical significance in sequence alignment.
  • These methods provide a valuable alternative for enhancing bioinformatics analyses, particularly when pretrained PSSMs are not available or applicable.