ArticleGenome biology2020
A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 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
70 citing papers in PubMed, 123 citations in OpenAlex.
- Hi-Cformer enables multiscale chromatin contact map modeling for single-cell Hi-C data analysis.Science advances · 2026Article
- Predicting enhancer-gene links from single-cell multi-omics data by integrating prior Hi-C information.Nucleic acids research · 2026Article
- Profiling the transcriptional regulatory network reveals putative shared regulatory elements within homoeologs in polyploid Brassica napus.Genome biology · 2026Article
- cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions.Nature methods · 2026Article
- Prediction of target genes and functional types of cis-regulatory modules in the human genome reveals their distinct properties.BMC biology · 2025Article
- Class-balanced negative training sets for improving classifier model predictions of enhancer-promoter interactions.BMC bioinformatics · 2025Article
- SEgene identifies links between super enhancers and gene expression across cell types.NPJ systems biology and applications · 2025Article
- Centromere-size reduction and chromatin state dynamics following intergenomic hybridization in cotton.PLoS genetics · 2025Article
- KansformerEPI: a deep learning framework integrating KAN and transformer for predicting enhancer-promoter interactions.Briefings in bioinformatics · 2025Article
- Multiomic single-cell profiling identifies critical regulators of postnatal brain.Nature genetics · 2025Article
- GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.Genes · 2024Article
- GEEES: inferring cell-specific gene-enhancer interactions from multi-modal single-cell data.Bioinformatics (Oxford, England) · 2024Article
- A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding.iScience · 2024Article
- Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation.BMC biology · 2024Article
- JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.Nucleic acids research · 2024Article
- Dynamic IL-6R/STAT3 signaling leads to heterogeneity of metabolic phenotype in pancreatic ductal adenocarcinoma cells.Cell reports · 2024Article
- TF-EPI: an interpretable enhancer-promoter interaction detection method based on Transformer.Frontiers in genetics · 2024Article
- Homeodomain-only protein suppresses proliferation and contributes to differentiation- and age-related reduced CD8Frontiers in immunology · 2024Article
- Editorial: Artificial intelligence and bioinformatics applications for omics and multi-omics studies.Frontiers in genetics · 2024Article
- CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms.Genome biology · 2023Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMany genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes.
resultsTo facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. We use BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the TargetFinder and PEP supervised learning methods. We find that while TargetFinder is the best-performing method, it is only modestly better than a baseline distance method for most benchmark datasets when trained and tested with the same cell type and that TargetFinder often does not outperform the distance method when applied across cell types.
conclusionsOur results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.
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.