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Flexible sequence similarity searching with the FASTA3 program package.

W R Pearson1

  • 1University of Virginia, Charlottesville, USA.

Methods in Molecular Biology (Clifton, N.J.)
|November 5, 1999
PubMed
Summary
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FASTA and BLAST tools offer accurate statistical estimates for sequence similarity searches. Effective strategies prioritize amino acid comparisons and use sequence statistics over percent identity for reliable homology detection.

Area of Science:

  • Bioinformatics and Computational Biology
  • Genomic and Proteomic Data Analysis

Background:

  • Sequence similarity searches are crucial for identifying homologous sequences in biological databases.
  • Traditional methods often focus on identifying homology, but effective strategies require careful consideration of statistical significance and alignment quality.

Purpose of the Study:

  • To provide guidelines for optimizing sequence similarity searches using tools like FASTA and BLAST.
  • To emphasize the importance of accurate statistical estimates and high-quality alignments in determining sequence homology.

Main Methods:

  • Prioritize amino acid-level comparisons over nucleotide-level comparisons.
  • Utilize sequence statistics (e.g., expectation value E) as the primary criterion for homology, rather than percent identity.

Related Experiment Videos

  • Employ strategies such as searching smaller, relevant databases and validating statistical accuracy with shuffled sequences.

    • Main Results:

    • FASTA and BLAST provide reliable answers to homology questions when expectation values (E) are statistically significant (< 0.001-0.01).
    • Effective search strategies involve comparing protein sequences first, then translated DNA, and lastly DNA sequences.
    • Scoring matrices and gap penalties can be adjusted to improve the detection of relationships, especially for partial or lower-quality sequences.

    Conclusions:

    • Statistically significant sequence similarity reliably infers homology, which implies common three-dimensional structure but not necessarily common function.
    • Orthologous sequences typically share similar functions, while paralogous sequences may diverge in function.
    • Motif databases can support homology findings but motif identity alone is insufficient without overall sequence similarity.