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 graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites.

Brian T Naughton1, Eugene Fratkin, Serafim Batzoglou

  • 1Department of Biochemistry, Stanford University, CA 94305, USA. briannau@stanford.edu

Nucleic Acids Research
|October 17, 2006
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

A genome-wide association study of mass spectrometry proteomics using a nanoparticle enrichment platform.

Nature genetics·2025
Same author

Multi-omics analysis of a pig-to-human decedent kidney xenotransplant.

Nature·2025
Same author

Multiplexed Nanoparticle Protein Corona Enables Accurate and Precise Deep Plasma Proteomics.

Journal of proteome research·2025
Same author

Cloud-Enabled Scalable Analysis of Large Proteomics Cohorts.

Journal of proteome research·2025
Same author

Deep, Unbiased, and Quantitative Mass Spectrometry-Based Plasma Proteome Analysis of Individual Responses to mRNA COVID-19 Vaccine.

Journal of proteome research·2025
Same author

A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using Proteograph<sup>TM</sup> and Trapped Ion Mobility Mass Spectrometry.

International journal of molecular sciences·2024
Same journal

Correction to 'scSuperAnnotator: A platform for benchmarking comparison and visualizing automated cellular annotation methods for scRNA-seq data'.

Nucleic acids research·2026
Same journal

Correction to 'Differentiable partition function calculation for RNA'.

Nucleic acids research·2026
Same journal

Deployment of non-canonical splicing in tunicate genomes is mediated by divergent U2AF function and changing m6A modification in U1 and U6 snRNA.

Nucleic acids research·2026
Same journal

Bacillus subtilis DnaB forms multiple protein-protein interactions essential for DNA replication initiation.

Nucleic acids research·2026
Same journal

Multiple forms of protein-protein and DNA binding are exhibited by BrxC from the BREX phage restriction system.

Nucleic acids research·2026
Same journal

Biosynthesis of glycosylated 5-hydroxycytosine in the DNA of diverse viruses.

Nucleic acids research·2026
See all related articles

This study introduces a graph-based motif model for identifying transcription factor binding sites. This new approach, MotifScan, outperforms traditional position-specific scoring matrices in detecting eukaryotic motifs.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcription factor binding sites are crucial for gene regulation.
  • Position-specific scoring matrices (PSSMs) are commonly used for motif discovery.
  • Existing models may struggle with complex nucleotide dependencies in motifs.

Purpose of the Study:

  • To develop a novel motif representation that addresses limitations of probabilistic models.
  • To introduce and evaluate the MotifScan algorithm for enhanced motif detection.
  • To analyze clustering patterns in eukaryotic transcription factor binding sites.

Main Methods:

  • Analysis of eukaryotic transcription factor binding sites to identify k-mer clustering.
  • Development of a graph-based motif representation instead of PSSMs.

Related Experiment Videos

  • Implementation and testing of the MotifScan algorithm.

    • Main Results:

    • Eukaryotic motifs exhibit significant clustering of similar k-mers due to functional and evolutionary constraints.
    • The graph-based approach effectively captures motif complexity.
    • MotifScan demonstrated superior performance compared to PSSM-based methods for eukaryotic motif detection.

    Conclusions:

    • Graph-based methods offer advantages for representing complex motif data.
    • MotifScan provides a more accurate and robust tool for identifying transcription factor binding sites.
    • Understanding k-mer clustering is key to improving motif discovery algorithms.