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

VOMBAT: prediction of transcription factor binding sites using variable order Bayesian trees.

Jan Grau1, Irad Ben-Gal, Stefan Posch

  • 1Institute of Computer Science, University Halle, 06099 Halle, Saale, Germany.

Nucleic Acids Research
|July 18, 2006
PubMed
Summary

Variable order models outperform traditional methods for identifying transcription factor binding sites. A new web server, VOMBAT, facilitates training, prediction, and cross-validation of these advanced models for DNA binding site recognition.

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

TALEs, TALENs, and TALE Base Editors: From Plant Pathology to Biotechnology.

Annual review of phytopathology·2026
Same author

Analyzing the digital customer journey: a novel framework for sequential behavior modeling.

Scientific reports·2026
Same author

Reanimating the past: From historical collections of the placenta and uterus to modern imaging, machine learning, and multiscale modeling.

Placenta·2026
Same author

CERTOMICS: trusted single-cell multiomics pipeline for high-resolution profiling of adoptive cellular immunotherapies.

Bioinformatics (Oxford, England)·2026
Same author

Improved reconstruction of transcripts and coding sequences from RNA-seq data.

Nucleic acids research·2026
Same author

Fast barcode calling based on <i>k</i>-mer distances.

PNAS nexus·2026

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional models like position weight matrices struggle with complex DNA binding site recognition.
  • Variable order Markov models and variable order Bayesian trees offer improved accuracy.
  • A need exists for accessible tools to utilize these advanced models.

Purpose of the Study:

  • To develop a web server (VOMBAT) for DNA binding site recognition.
  • To implement variable order Markov models and variable order Bayesian trees.
  • To provide functionalities for model training, prediction, and cross-validation.

Main Methods:

  • Development of a web server utilizing variable order Markov models and variable order Bayesian trees.
  • Implementation of training algorithms for annotated binding sites and genomic background sequences.

Related Experiment Videos

  • Integration of prediction and cross-validation functionalities with adjustable parameters.

    • Main Results:

    • The VOMBAT web server enables training of variable order models.
    • Putative DNA binding sites can be predicted using trained models.
    • Cross-validation experiments can be performed for model evaluation.

    Conclusions:

    • Variable order models demonstrate superior performance in DNA binding site recognition compared to traditional methods.
    • The VOMBAT web server provides a powerful and accessible platform for utilizing these advanced models.
    • The server's computational infrastructure supports demanding tasks like genome-wide predictions.