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ENCODE-rE2G

Summary

ENCODE-rE2G is a supervised predictive model designed to identify enhancer-gene (E-G) regulatory interactions in the human genome. Developed by the Engreitz Lab, it integrates cell-type-specific chromatin activity and physical contact features to predict which distal genetic elements regulate specific target genes. The model achieves state-of-the-art performance across CRISPR perturbation, eQTL, and GWAS benchmarking datasets, providing a comprehensive resource of over 92 million regulatory links across 1,458 human biosamples (as of 2026-07-15).

Overview

Identifying the target genes of transcriptional enhancers is a major challenge in human genetics, particularly for interpreting the functional impact of noncoding disease-associated variants. ENCODE-rE2G addresses this by using supervised machine learning trained directly on gold-standard CRISPR perturbation datasets (such as CRISPRi-FlowFISH and Perturb-seq) rather than relying solely on physical contact or correlation proxies.

The framework consists of two main classifier variants: 1. ENCODE-rE2G: A general model that relies on features computable from cell-type-specific DNase-seq data combined with a tissue-averaged Hi-C contact map, making it widely applicable across hundreds of biosamples. 2. ENCODE-rE2GExtended: An expanded model utilizing 45 features derived from additional assays (such as H3K27ac ChIP-seq, cell-type-specific Hi-C, and ChIA-PET) available in Tier 1 cell types.

Model Features

The standard ENCODE-rE2G model uses a logistic regression classifier with eight selected features: - Chromatin State: Quantitative DNase-seq signals at both the enhancer element and promoter. - 3D Contact: Contact frequency derived from a reference Hi-C Megamap (averaged across 65 ENCODE datasets). - Activity-by-Contact: The Activity-by-Contact (ABC) Model score, which multiplies enhancer activity and 3D contact frequency. - Genomic Position: Distance from the element to the promoter and number of intervening genes/promoters. - Promoter Class: Whether the gene is expressed uniformly (housekeeping) or cell-type-specifically. - Nearby Enhancer Activity: The cumulative activity of other elements within 5 kb of the candidate element, capturing synergistic enhancer-enhancer interactions.

Benchmarking and Performance

ENCODE-rE2G was systematically evaluated against a gold-standard dataset of 10,356 element-gene pairs tested with CRISPR in K562 cells: - CRISPR Validation: Achieved an Area Under the Precision-Recall Curve (AUPRC) of 0.66, outperforming the standard ABC model (AUPRC = 0.56). - eQTL Linking: Demonstrated 25-fold enrichment of fine-mapped GTEx eQTL variants in predicted enhancers. - GWAS Target Gene Identification: Achieved 68% precision in linking lead variants of 197 blood-trait credible sets to causal target genes.

Code and Data Availability

The ENCODE-rE2G model, Snakemake workflow, and predictions are available at the following repositories: - Model Code: GitHub - ENCODE_rE2G - Predictions: Available via the ENCODE Portal (as of 2026-07-15).

See Also

Citations