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

BALSA: Bayesian algorithm for local sequence alignment.

Bobbie-Jo M Webb1, Jun S Liu, Charles E Lawrence

  • 1The Wadsworth Center for Laboratories and Research, New York State Department of Health, Albany, NY 12201, USA.

Nucleic Acids Research
|February 28, 2002
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

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Advances in neural information processing systems·2026
Same author

Phenotypic prediction of missense variants via deep contrastive learning.

Nature biomedical engineering·2026
Same author

DEDUCE: statistical inference on disease-associated genes uncovers tissue-disease associations.

NAR genomics and bioinformatics·2026
Same author

Designing strongly coupled polaritonic structures via statistical machine learning.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

bioRxiv : the preprint server for biology·2025
Same author

Participation bias in the estimation of heritability and genetic correlation.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same journal

Neurochondrin promotes U5 snRNP maturation by regulating AAR2 release from PRPF8.

Nucleic acids research·2026
Same journal

Elongationless start-stop elements are stress-resilient translation gates that are more repressive than uTranslons.

Nucleic acids research·2026
Same journal

Evolution of the ribosomal exit tunnel through the eyes of the nascent chain.

Nucleic acids research·2026
Same journal

Enhancing the performance and interpretability of epigenetic clocks.

Nucleic acids research·2026
Same journal

FABIAN-variant 2026: improved prediction of the effects of DNA variants on transcription factor binding.

Nucleic acids research·2026
Same journal

Structural and biochemical characterization of Grimontia hollisae thermostable direct hemolysin with DNA reveals first Vibrio hemolysin with nuclease activity.

Nucleic acids research·2026
See all related articles

The new Bayesian algorithm for local sequence alignment (BALSA) improves upon traditional methods by accounting for uncertainties in scoring matrices and gap penalties. BALSA offers more robust remote homology detection than existing algorithms.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional Smith-Waterman algorithm provides a single optimal alignment, but is sensitive to scoring matrix and gap penalty choices.
  • Alignment scores are sequence-length dependent, necessitating post-analysis adjustments.
  • Existing methods struggle with detecting remote homologs effectively.

Purpose of the Study:

  • To develop a novel Bayesian algorithm for local sequence alignment (BALSA) that addresses limitations of existing methods.
  • To incorporate uncertainty in scoring matrices and gap penalties into the alignment process.
  • To improve the detection of remote homologs and structural neighbors.

Main Methods:

  • Developed a Bayesian algorithm for local sequence alignment (BALSA).

Related Experiment Videos

  • BALSA incorporates uncertainty by considering multiple scoring matrices, gap parameters, and alignments in its forward sums.
  • Compared BALSA's performance against SSEARCH using SCOP databases (PDB40D-B and PDB90D-B).

    • Main Results:

    • BALSA accounts for uncertainty in scoring matrices and gap penalties, and automatically adjusts for sequence length variations.
    • BALSA detected 19.8% and 41.3% of remote homologs in PDB40D-B and PDB90D-B, respectively, at a 1% error rate.
    • SSEARCH detected 18.4% and 38% of remote homologs in the same databases, respectively.

    Conclusions:

    • BALSA offers a more robust approach to local sequence alignment by managing uncertainty in key parameters.
    • BALSA demonstrates superior performance in detecting remote homologs compared to the established SSEARCH algorithm.
    • The Bayesian framework provides posterior probabilities for gap penalties and scoring matrices, offering deeper insights.