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Related Experiment Video

Updated: Aug 9, 2025

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

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

Published on: June 2, 2018

Delta.EPI: a probabilistic voting-based enhancer-promoter interaction prediction platform.

Yuyang Zhang¹, Haoyu Wang¹, Jing Liu²

¹CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.

Journal of Genetics and Genomics = Yi Chuan Xue Bao

|February 23, 2023

Summary

This summary is machine-generated.

Delta.EPI is a new platform that assesses enhancer-promoter interactions (EPIs) using a statistical model. It helps molecular biologists make reliable EPI predictions for gene regulation studies.

Keywords:

Benchmark Enhancer–promoter interaction Hi-C Prediction Web server

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

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

Published on: March 31, 2022

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Enhancer-promoter interactions (EPIs) are crucial for gene transcriptional regulation in eukaryotes.
Predicting EPIs from genomic data is challenging due to empirical benchmarking and lack of quantitative comparisons.
Molecular biologists require reliable EPI predictions for gene regulation and 3D genome studies.

Purpose of the Study:

To introduce Delta.EPI, a novel platform for predicting enhancer-promoter interactions.
To provide a quantitative, model-based assessment of state-of-the-art EPI predictors.
To offer a user-friendly tool for reliable EPI predictions to guide biological research.

Main Methods:

Development of Delta.EPI based on a statistical data integration model.
Comprehensive assessment of four existing EPI prediction tools.
Implementation of a user-friendly interface with visualization capabilities.

Main Results:

Delta.EPI provides a quantitative comparison of EPI prediction algorithms.
The platform presents sorted EPI predictions with confidence scores.
A case study demonstrates the utility of Delta.EPI in biological research.

Conclusions:

Delta.EPI offers a powerful, accessible tool for enhancer-promoter interaction prediction.
The platform aids molecular biologists in gene regulation and 3D genome studies.
Delta.EPI facilitates ease-to-access EPI predictions, advancing genomic research.