ArticleScientific reports2018
A neural network based model effectively predicts enhancers from clinical ATAC-seq samples.
Article in Scientific reports, 2018. 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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Who cites it
17 citing papers in PubMed, 38 citations in OpenAlex.
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- Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas.bioRxiv : the preprint server for biology · 2025Article
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- Epigenomic landscape of the human dorsal root ganglion: sex differences and transcriptional regulation of nociceptive genes.bioRxiv : the preprint server for biology · 2024Article
- Predmoter-cross-species prediction of plant promoter and enhancer regions.Bioinformatics advances · 2024Article
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
- Cis-regulatory atlas of primary human CD4+ T cells.BMC genomics · 2023Article
- Enhancer-silencer transitions in the human genome.Genome research · 2022Article
- CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data.PLoS computational biology · 2021Article
- Employing core regulatory circuits to define cell identity.The EMBO journal · 2021Review
- Epigenomic landscape of human colorectal cancer unveils an aberrant core of pan-cancer enhancers orchestrated by YAP/TAZ.Nature communications · 2021Article
- Fish-Ing for Enhancers in the Heart.International journal of molecular sciences · 2021Review
- Self-organizing maps with variable neighborhoods facilitate learning of chromatin accessibility signal shapes associated with regulatory elements.BMC bioinformatics · 2021Article
- A pitfall for machine learning methods aiming to predict across cell types.Genome biology · 2020Article
- Analyzing a putative enhancer of optic disc morphology.BMC genetics · 2020Article
- Article
- Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies.BioData mining · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Enhancers are cis-acting sequences that regulate transcription rates of their target genes in a cell-specific manner and harbor disease-associated sequence variants in cognate cell types. Many complex diseases are associated with enhancer malfunction, necessitating the discovery and study of enhancers from clinical samples. Assay for Transposase Accessible Chromatin (ATAC-seq) technology can interrogate chromatin accessibility from small cell numbers and facilitate studying enhancers in pathologies. However, on average, ~35% of open chromatin regions (OCRs) from ATAC-seq samples map to enhancers. We developed a neural network-based model, Predicting Enhancers from ATAC-Seq data (PEAS), to effectively infer enhancers from clinical ATAC-seq samples by extracting ATAC-seq data features and integrating these with sequence-related features (e.g., GC ratio). PEAS recapitulated ChromHMM-defined enhancers in CD14+ monocytes, CD4+ T cells, GM12878, peripheral blood mononuclear cells, and pancreatic islets. PEAS models trained on these 5 cell types effectively predicted enhancers in four cell types that are not used in model training (EndoC-βH1, naïve CD8+ T, MCF7, and K562 cells). Finally, PEAS inferred individual-specific enhancers from 19 islet ATAC-seq samples and revealed variability in enhancer activity across individuals, including those driven by genetic differences. PEAS is an easy-to-use tool developed to study enhancers in pathologies by taking advantage of the increasing number of clinical epigenomes.
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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 Socratic graph.