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 Concept Videos

Reporter Genes02:11

Reporter Genes

11.3K
Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
11.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Further improvement in London's air quality demands more than the Ultra Low Emission Zone policy.

NPJ clean air·2025
Same author

Preoperative prognostic assessment using intratumoral and peritumoral adipose tissue radiomics derived from contrast-enhanced CT in cT3-4 gastric cancer.

Frontiers in oncology·2025
Same author

TDP-43: unveiling the hidden key to cellular fate decisions.

Cell communication and signaling : CCS·2025
Same author

Structurally Confined Ni with Oxygen-Deficient CeO<sub>2</sub> for Efficient Self-Transfer Hydrogenolysis of Lignin into Jet Fuel Precursors.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

A Nanolasing-Based Sensor for Ultra-Sensitive Detection of Trace HSA in Artificial Urine.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Impact of neoadjuvant and adjuvant chemotherapy on breast cancer prognosis in a propensity score matched population.

Scientific reports·2025

Related Experiment Video

Updated: Jun 15, 2025

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

8.2K

TF-EPI: an interpretable enhancer-promoter interaction detection method based on Transformer.

Bowen Liu1, Weihang Zhang1, Xin Zeng1

  • 1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan.

Frontiers in Genetics
|August 26, 2024
PubMed
Summary
This summary is machine-generated.

We developed TF-EPI, a Transformer-based deep learning model for detecting enhancer-promoter interactions (EPIs) from DNA sequences. TF-EPI outperforms existing methods and identifies cell-specific regulatory elements, advancing gene regulation studies.

Keywords:
Transformerattention mechanismenhancer-promoter interactionsmotif discoverytransfer learning

More Related Videos

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines
10:46

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines

Published on: June 2, 2018

9.3K
AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain
09:59

AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain

Published on: March 31, 2022

2.6K

Related Experiment Videos

Last Updated: Jun 15, 2025

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

8.2K
Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines
10:46

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines

Published on: June 2, 2018

9.3K
AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain
09:59

AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain

Published on: March 31, 2022

2.6K

Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Enhancer-promoter interactions (EPIs) are critical for gene expression regulation and understanding disease mechanisms.
  • Accurate detection of EPIs is essential for deciphering complex regulatory networks.
  • Current methods for EPI detection face challenges in scalability and accuracy, particularly across different cell types.

Purpose of the Study:

  • To develop a novel deep learning model, TF-EPI, for detecting enhancer-promoter interactions (EPIs) solely from DNA sequences.
  • To leverage the Transformer architecture's attention mechanism for identifying key sequence motifs and transcription factor binding sites involved in EPIs.
  • To enhance the accuracy and interpretability of EPI detection and explore its application in understanding cell type-specific gene regulation.

Main Methods:

  • Developed TF-EPI, a deep learning model utilizing the Transformer architecture for sequence-based EPI detection.
  • Employed the attention mechanism within the Transformer to identify significant sequence motifs and features in enhancers and promoters.
  • Validated identified motifs and sequences against established databases (JASPAR, UniBind) and analyzed associated transcription factors (TFs).
  • Incorporated transfer learning to improve cross-cell line EPI detection accuracy.

Main Results:

  • TF-EPI demonstrated superior performance compared to state-of-the-art methods on multiple benchmark datasets for EPI detection.
  • The attention mechanism successfully identified distinct, cell type-specific sequence motifs in enhancers and promoters, validated against JASPAR and UniBind.
  • Analysis of TF motifs revealed conserved and heterogeneous gene regulatory mechanisms across cell types and identified cell line-specific TFs.
  • Transfer learning significantly improved the accuracy of EPI detection across different cell lines.

Conclusions:

  • TF-EPI provides a powerful, sequence-based approach for accurate enhancer-promoter interaction detection.
  • The Transformer's attention mechanism offers valuable insights into the sequence determinants and TF binding preferences underlying EPIs.
  • This method advances the understanding of cis-regulatory grammar and cell type-specific gene regulation, offering a milestone in the field.