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

DWE: discriminating word enumerator.

Pavel Sumazin1, Gengxin Chen, Naoya Hata

  • 1Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA. ps@cs.pdx.edu

Bioinformatics (Oxford, England)
|August 31, 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

DNA methylation reprogramming in marsupial embryos is restricted to the extraembryonic lineage.

Nature communications·2026
Same author

Catalytic Enantioselective Dearomatizing [2,3]-Wittig Rearrangements Allow Divergent [2,3]-, [1,2]-, and Sommelet-Hauser-Type Products.

Journal of the American Chemical Society·2026
Same author

The acylative kinetic resolution of 1,2-azaborine naphthols.

Chemical science·2026
Same author

The catalytic enantioselective [1,2]-Wittig rearrangement cascade of allylic ethers.

Nature chemistry·2026
Same author

Complete mitochondrial genome of the litchi pathogen <i>Phytophthora litchii</i>.

Microbiology resource announcements·2025
Same author

Multi-channel and free-spectral-range-free tunable add-drop filters on thin-film lithium niobate.

Nanophotonics (Berlin, Germany)·2025
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

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

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

We developed a new tool to discover tissue-specific transcription factor binding sites using gene expression and sequence data. This method identifies key liver-specific factors like HNF-4 and C/EBPbeta, revealing their synergistic interactions.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Tissue-specific transcription factor binding sites are crucial for understanding cell-specific gene regulation.
  • Identifying these sites aids in deciphering complex gene expression patterns.

Purpose of the Study:

  • To develop a novel computational tool for discovering de novo tissue-specific transcription factor binding sites.
  • To leverage both sequence and gene expression data for enhanced binding site identification.
  • To investigate synergistic interactions among transcription factors in specific tissues.

Main Methods:

  • A word-counting approach was employed for de novo binding site discovery.
  • Tissue-specific gene expression data was incorporated via gene classification (positive/repressed expression).

Related Experiment Videos

  • A statistical method was used to identify overrepresented binding sites in foreground versus background promoter sequences.
  • The approach was extended to detect synergistic transcription factor binding site combinations.

    • Main Results:

    • The tool successfully identified overrepresented transcription factor binding sites in liver-specific genes.
    • Hepatocyte nuclear factors (HNF-1, HNF-4) and CCAAT/enhancer-binding protein (C/EBPbeta) binding sites were found to be significantly enriched.
    • Strong synergistic relationships were observed between HNF-4 and other factors including HNF-1, HNF-3beta, and C/EBPbeta.

    Conclusions:

    • The developed tool effectively identifies tissue-specific transcription factor binding sites and their regulatory roles.
    • Key transcription factors and their synergistic interactions in liver-specific gene regulation were elucidated.
    • This approach provides a valuable resource for studying tissue-specific gene regulation and transcription factor networks.