Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A local alignment metric for accelerating biosequence database search.

Peter A Spiro1, Natasa Macura

  • 1Incyte Genomics, Inc., 3160 Porter Drive, Palo Alto, CA 94304, USA. Peter.Spiro@alumni.brown.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 10, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dependence of macrophage phagocytic efficacy on antibody concentration.

Infection and immunityยท2007
See all related articles

We developed a novel metric for sequence alignment searches that accelerates discovery by identifying and skipping redundant database entries. This method enhances search efficiency without compromising accuracy in biological sequence analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Optimal local sequence alignment is computationally intensive.
  • Current methods face challenges in efficiently searching large biological sequence databases.
  • Accelerating alignment searches without compromising sensitivity is a key goal in bioinformatics.

Purpose of the Study:

  • To introduce a new metric for local sequence alignments.
  • To leverage the metric's properties for accelerating optimal alignment searches.
  • To demonstrate the metric's utility in reducing computational load without sensitivity loss.

Main Methods:

  • Introduction of a novel metric based on the triangle inequality property.
  • Mathematical proof of the metric's existence for various scoring systems.

Related Experiment Videos

  • Development of a database clustering and search strategy utilizing the triangle inequality.

    • Main Results:

    • The metric allows identification of redundant database entries, enabling skipped comparisons.
    • Demonstrated establishment of the triangle inequality for nucleotide-to-protein comparisons.
    • Achieved moderate but significant acceleration of searches against the "nr" protein database.

    Conclusions:

    • The proposed metric effectively accelerates optimal alignment searches.
    • The triangle inequality provides a theoretically sound basis for database clustering.
    • This approach offers a standard for evaluating heuristic clustering strategies in bioinformatics.