ArticleNature genetics2026
Mapping enhancer-gene regulatory interactions from single-cell data.
Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Comparing bulk and single-cell methodologies and models to profile gene expression, chromatin accessibility and regulatory links in endothelial cells treated with TNFα.Biochemistry and biophysics reports · 2026Article
- Article
- Dynamic neuro-immune regulation of psychiatric risk loci in human neurons.Nature communications · 2026Article
- Inherited Susceptibility to Urinary Tract Infections from Kidney Papilla to Bladder.medRxiv : the preprint server for health sciences · 2026Article
- Comparing bulk and single-cell methodologies and models to profile gene expression, chromatin accessibility and regulatory links in endothelial cells treated with TNFα.bioRxiv : the preprint server for biology · 2026Article
- Multimodal atlas of human atherosclerosis links granular vascular cell states to coronary artery disease risk.medRxiv : the preprint server for health sciences · 2026Article
- PerturbPlan: An analytical framework for designing Perturb-seq experiments.bioRxiv : the preprint server for biology · 2026Article
- Design and interpretation of eQTL-GWAS colocalisation studies: Lessons from a large-scale evaluation.PLoS genetics · 2026Article
- An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease.Genome biology · 2026Article
- CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data.Bioinformatics (Oxford, England) · 2026Article
- Distinguishing causal from tagging enhancers using single-cell multiome data.medRxiv : the preprint server for health sciences · 2026Article
- Decoding Vascular Cell Diversity: Single-Cell Approaches to Mechanisms of Vascular Disease.Circulation research · 2026Review
- Deep learning the dynamic regulatory sequence code of cardiac organoid differentiation.bioRxiv : the preprint server for biology · 2025Article
- Sex differences in the developing human cortex intersect with genetic risk of neurodevelopmental disorders.bioRxiv : the preprint server for biology · 2025Article
- Genome-wide rules of transcription factor cooperativity revealed throughbioRxiv : the preprint server for biology · 2025Article
- Variant-specific priors clarify colocalisation analysis.PLoS genetics · 2025Article
- Atlas of nascent RNA transcripts reveals tissue-specific enhancer to gene linkages.BMC genomics · 2025Article
- Molecular convergence of risk variants for congenital heart defects leveraging a regulatory map of the human fetal heart.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
- Update of
Authors and funding
23 authors.
Funding
Abstract
Mapping enhancers and their target genes in specific cell types is crucial for understanding gene regulation and human disease genetics. However, accurately predicting enhancer-gene regulatory interactions from single-cell datasets has been challenging. Here we introduce a family of classification models, scE2G, to predict enhancer-gene regulation. These models use features from single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) or multiomic RNA and ATAC-seq data, and are trained on a CRISPR perturbation dataset including >10,000 evaluated element-gene pairs. We benchmark scE2G models against CRISPR perturbations, fine-mapped expression quantitative trait loci and genome-wide association study variant-gene associations and demonstrate state-of-the-art performance at prediction tasks across several cell types and categories of perturbations. We apply scE2G to build maps of enhancer-gene regulatory interactions in heterogeneous tissues and interpret noncoding variants associated with complex traits, nominating regulatory interactions linking INPP4B and IL15 to lymphocyte count. The scE2G models will enable accurate mapping of enhancer-gene regulatory interactions across thousands of human cell types.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.