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Query-seeded iterative sequence similarity searching improves selectivity 5-20-fold.

William R Pearson1, Weizhong Li2, Rodrigo Lopez2

  • 1Dept. of Biochemistry and Molecular Genetics, University of Virginia, School of Medicine, Charlottesville, VA 22908, USA.

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|December 8, 2016
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Summary
This summary is machine-generated.

A new method significantly reduces errors in protein family searches by anchoring position-specific scoring models (PSSMs) to the original query sequence, minimizing contamination and improving accuracy.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Iterative similarity search programs (e.g., PSIBLAST, HMMER) use position-specific scoring models (PSSMs or HMMs) for sensitive protein family identification.
  • These models are susceptible to contamination from unrelated sequences, leading to inaccurate results and model divergence.

Purpose of the Study:

  • To develop a strategy for reducing PSSM contamination in iterative similarity searches.
  • To improve the accuracy and reliability of protein family identification methods.

Main Methods:

  • Examined alignment errors in PSIBLAST PSSM contamination.
  • Implemented a strategy of anchoring the PSSM to the original query sequence by inserting query residues into gapped positions of aligned subject sequences.

Main Results:

  • The anchoring strategy significantly reduced PSSM contamination, leading to a >20-fold decrease in false-positive alignments compared to standard PSIBLAST and HMMER.
  • This method maintained high search sensitivity with minimal loss.
  • The modified PSSMs showed increased target percent identity.

Conclusions:

  • A simple modification to PSSM construction can dramatically reduce contamination and improve the specificity of iterative similarity searches.
  • This approach enhances the reliability of identifying protein families and their homologs.