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

BioOptimizer: a Bayesian scoring function approach to motif discovery.

Shane T Jensen1, Jun S Liu

  • 1Department of Statistics, Harvard University, Cambridge, MA 02138-2901, USA. jensen@stat.harvard.edu

Bioinformatics (Oxford, England)
|February 14, 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

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

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

BioOptimizer enhances transcription factor (TF) binding site prediction by optimizing motif signals. This Bayesian approach improves accuracy over existing methods, aiding biological relevance assessments.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences.
  • Experimental identification of TF binding sites is costly and time-intensive.
  • Existing motif-finding algorithms have limitations in accuracy and biological relevance assessment.

Purpose of the Study:

  • To develop a comprehensive scoring function for optimizing TF binding site motif discovery.
  • To introduce an algorithm, BioOptimizer, to reduce noise and improve motif signal detection.
  • To enable objective comparison and selection of the most biologically relevant predicted motifs.

Main Methods:

  • Derivation of a scoring function using a full Bayesian model.
  • Incorporation of handling for unknown site abundance, motif width, and two-block motifs with variable gaps.

Related Experiment Videos

  • Development of the BioOptimizer algorithm to optimize the scoring function.

    • Main Results:

    • BioOptimizer demonstrates superior accuracy compared to individual motif-finding programs in simulations and bacterial gene sets.
    • The scoring function effectively reduces noise in motif signals.
    • Objective comparison and selection of optimal motifs are enabled, combining strengths of existing tools.

    Conclusions:

    • BioOptimizer offers a robust method for enhancing TF binding site prediction accuracy.
    • The approach improves the biological relevance assessment of predicted motifs.
    • BioOptimizer can be integrated with existing motif-finding programs for improved performance.