ArticleNature communications2021
Deep learning-based enhancement of epigenomics data with AtacWorks.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks.Nature communications · 2026Article
- EpiExpr: Predicting gene expression using epigenetic data and chromatin interactions.bioRxiv : the preprint server for biology · 2026Article
- R-loops orchestrate RNAPII transcriptional reprogramming for the maternal-to-zygotic transition.Cell research · 2026Article
- Chrom-Sig: de-noising 1D genomic profiles by signal processing methods.Bioinformatics (Oxford, England) · 2025Article
- DeepWheat: predicting the effects of genomic variants on gene expression and regulatory activities across tissues and varieties in wheat using deep learning.Genome biology · 2025Article
- EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment.bioRxiv : the preprint server for biology · 2025Article
- Chromatin accessibility provides a window into the genetic etiology of human brain disease.Trends in genetics : TIG · 2025Review
- The AaFoxA factor regulates female reproduction through chromatin remodeling in the mosquito vector Aedes aegypti.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Combining single-cell ATAC and RNA sequencing for supervised cell annotation.BMC bioinformatics · 2025Article
- Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases.Briefings in functional genomics · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Unlocking hematopoietic stem cell potential: integrative computational approaches for genomic and transcriptomic analysis.Frontiers in cell and developmental biology · 2025Review
- Glioma Image-Level and Slide-Level Gene Predictor (GLISP) for Molecular Diagnosis and Predicting Genetic Events of Adult Diffuse Glioma.Bioengineering (Basel, Switzerland) · 2024Article
- Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation.bioRxiv : the preprint server for biology · 2024Article
- ChromaFold predicts the 3D contact map from single-cell chromatin accessibility.Nature communications · 2024Article
- dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility.Briefings in bioinformatics · 2024Article
- MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts.Nature communications · 2024Article
- Interface-guided phenotyping of coding variants in the transcription factor RUNX1.Cell reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks, a deep learning toolkit to denoise sequencing coverage and identify regulatory peaks at base-pair resolution from low cell count, low-coverage, or low-quality ATAC-seq data. Models trained by AtacWorks can detect peaks from cell types not seen in the training data, and are generalizable across diverse sample preparations and experimental platforms. We demonstrate that AtacWorks enhances the sensitivity of single-cell experiments by producing results on par with those of conventional methods using ~10 times as many cells, and further show that this framework can be adapted to enable cross-modality inference of protein-DNA interactions. Finally, we establish that AtacWorks can enable new biological discoveries by identifying active regulatory regions associated with lineage priming in rare subpopulations of hematopoietic stem cells.
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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 OpenQuestion graph.