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Updated: Jun 6, 2025
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Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines
Published on: June 2, 2018
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Mapping enhancer-gene regulatory interactions from single-cell data
Biorxiv : the Preprint Server for Biology
|November 28, 2024
Summary
We developed scE2G, a novel machine learning model to predict enhancer-gene interactions using single-cell data. This tool accurately maps gene regulation and aids in understanding complex traits and diseases.
Area of Science:
- Genomics
- Computational Biology
- Epigenetics
Background:
- Mapping enhancer-gene interactions is vital for understanding gene regulation and disease genetics.
- Accurate prediction of these interactions from single-cell data remains a significant challenge.
Purpose of the Study:
- To introduce scE2G, a new family of classification models for predicting enhancer-gene regulatory interactions.
- To leverage single-cell ATAC-seq and multiomic data for enhanced prediction accuracy.
Main Methods:
- Developed scE2G, a classification model trained on a large CRISPR perturbation dataset (>10,000 element-gene pairs).
- Utilized features from single-cell ATAC-seq and multiomic RNA and ATAC-seq data.
- Benchmarked scE2G against CRISPR perturbations, fine-mapped eQTLs, and GWAS variant-gene associations.
Main Results:
- scE2G demonstrated state-of-the-art performance in predicting enhancer-gene interactions across diverse cell types and perturbation categories.
- Applied scE2G to map regulatory interactions in heterogeneous tissues.
- Identified potential regulatory links between genes like INPP4B and IL15 and lymphocyte counts.
Conclusions:
- scE2G models provide a powerful tool for accurate enhancer-gene interaction mapping.
- This approach facilitates the interpretation of noncoding variants associated with complex human traits.
- The models enable the construction of regulatory maps across thousands of human cell types.
