ArticlePLoS computational biology2019
EAGLE: An algorithm that utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 26 citations in OpenAlex.
- CRM-TI: an enhanced pipeline for computationally assigning the target genes of cis-regulatory modules by considering comprehensive long-range regulation mechanisms.BioData mining · 2026Article
- In vivo transition in chromatin accessibility during differentiation of deep-layer excitatory neurons in the neocortex.Development (Cambridge, England) · 2025Article
- SEgene identifies links between super enhancers and gene expression across cell types.NPJ systems biology and applications · 2025Article
- Prediction of Enhancer-Gene Interactions Using Chromatin-Conformation Capture and Epigenome Data Using STARE.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Integrative approaches based on genomic techniques in the functional studies on enhancers.Briefings in bioinformatics · 2023Review
- From Genotype to Phenotype: How Enhancers Control Gene Expression and Cell Identity in Hematopoiesis.HemaSphere · 2023Review
- Learning gene networks under SNP perturbation using SNP and allele-specific expression data.bioRxiv : the preprint server for biology · 2023Article
- The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data.Bioinformatics (Oxford, England) · 2023Article
- Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques.BMC bioinformatics · 2022Article
- Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants.Bioinformatics (Oxford, England) · 2021Article
- Predict long-range enhancer regulation based on protein-protein interactions between transcription factors.Nucleic acids research · 2021Article
- Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.Briefings in bioinformatics · 2021Article
- Head to head evaluation of second generation ALK inhibitors brigatinib and alectinib as first-line treatment for ALK+ NSCLC using anOncotarget · 2021Article
- EpiRegio: analysis and retrieval of regulatory elements linked to genes.Nucleic acids research · 2020Article
- Integrative computational epigenomics to build data-driven gene regulation hypotheses.GigaScience · 2020Article
- EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species.Nucleic acids research · 2020Article
- Genome-wide profiles of UV lesion susceptibility, repair, and mutagenic potential in melanoma.Mutation researchArticle
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2 authors at 1 institution in 1 country.
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Abstract
Long-range regulation by distal enhancers is crucial for many biological processes. The existing methods for enhancer-target gene prediction often require many genomic features. This makes them difficult to be applied to many cell types, in which the relevant datasets are not always available. Here, we design a tool EAGLE, an enhancer and gene learning ensemble method for identification of Enhancer-Gene (EG) interactions. Unlike existing tools, EAGLE used only six features derived from the genomic features of enhancers and gene expression datasets. Cross-validation revealed that EAGLE outperformed other existing methods. Enrichment analyses on special transcriptional factors, epigenetic modifications, and eQTLs demonstrated that EAGLE could distinguish the interacting pairs from non- interacting ones. Finally, EAGLE was applied to mouse and human genomes and identified 7,680,203 and 7,437,255 EG interactions involving 31,375 and 43,724 genes, 138,547 and 177,062 enhancers across 89 and 110 tissue/cell types in mouse and human, respectively. The obtained interactions are accessible through an interactive database enhanceratlas.org. The EAGLE method is available at https://github.com/EvansGao/EAGLE and the predicted datasets are available in http://www.enhanceratlas.org/.
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