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Finding Protein and Nucleotide Similarities with FASTA.

William R Pearson1

  • 1University of Virginia School of Medicine, Charlottesville, Virginia.

Current Protocols in Bioinformatics
|March 25, 2016
PubMed
Summary
This summary is machine-generated.

The FASTA programs offer versatile tools for rapid and optimal sequence similarity searching in protein and DNA data. These tools improve alignment accuracy and sensitivity, aiding in sequence characterization and analysis.

Keywords:
E()-valuealignment annotationexpectationhomologyscoring matricessimilarity

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence similarity searching is crucial for understanding protein and DNA function.
  • Existing tools like BLAST offer rapid searching, but comprehensive options for optimal and specialized searches are also needed.
  • Accurate statistical significance estimation and flexible output formats are essential for integrating search results into analysis pipelines.

Purpose of the Study:

  • To introduce the FASTA programs as a comprehensive suite of tools for sequence similarity searching.
  • To highlight the advanced features of FASTA, including empirical statistical significance estimation and flexible output options.
  • To demonstrate the utility of FASTA for characterizing protein and DNA sequences through various comparison types.

Main Methods:

  • The FASTA programs encompass rapid similarity search tools (e.g., fasta36) and slower, optimal search programs (e.g., ssearch36).
  • An empirical strategy is employed for estimating statistical significance, accommodating diverse scoring matrices and gap penalties.
  • The software supports various database formats (including SQL) and offers strategies for integrating domain annotations and highlighting critical residues.

Main Results:

  • FASTA provides a range of tools for rapid, optimal, local, and global similarity searches across protein and DNA sequences.
  • The empirical statistical method enhances alignment boundary accuracy and search sensitivity.
  • Output formats are compatible with existing pipelines, and the programs facilitate searching large datasets via representative subsets.

Conclusions:

  • The FASTA programs offer a powerful and flexible alternative for sequence similarity searching and characterization.
  • Their advanced statistical methods and integration capabilities enhance the analysis of biological sequence data.
  • FASTA supports diverse comparison types (protein:protein, protein:DNA, DNA:DNA) and database formats, making it a versatile bioinformatics resource.