ArticleNature communications2021
Comprehensive analysis of single cell ATAC-seq data with SnapATAC.
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 261 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
261 citing papers in PubMed, 1 synthesis or guideline pooled it, 471 citations in OpenAlex.
- Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.Briefings in bioinformatics · 2023Pooled it
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- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Mapping enhancer-gene regulatory interactions from single-cell data.Nature genetics · 2026Article
- Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina.Science advances · 2026Article
- Functional and Transcriptional Downregulation of Glutamate Transporters (EAAT1 and EAAT2) via DNMT3B Overexpression in Glial Cells.Molecular neurobiology · 2026Article
- ATACompass enables cross-species cell identity mapping for scATAC-seq without gene annotations.Cell reports methods · 2026Article
- Ectomesenchyme contributes to epidermal stem cell formation through mesenchymal-to-epithelial transition.Nature communications · 2026Article
- scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.Genomics, proteomics & bioinformatics · 2026Article
- Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks.Nature communications · 2026Article
- Hybrid Encoding and Adaptive Guidance for Enhanced Single-Cell Multi-omics Clustering.Interdisciplinary sciences, computational life sciences · 2026Article
- Optimal transport for label transfer in single-cell multi-omics integration.Briefings in bioinformatics · 2026Article
- Single-cell chromatin accessibility and cis-regulatory element analyses in plants using the scPlantReg platform.Nature plants · 2026Article
- PAIR: Reconstructing Single-Cell Open-Chromatin Landscapes for Transcription Factor Regulome Mapping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Connecting polygenic disease risk to cell states and regulatory programs through single-cell chromatin accessibility.bioRxiv : the preprint server for biology · 2026Article
- Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.Genome biology · 2026Article
- Monocyte Lineage Expansion Drives Transcriptomic Individuality in Genetically Identical Armadillo Quadruplets.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation.PLoS computational biology · 2026Article
201 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
16 authors at 5 institutions in 2 countries.
Funding
Abstract
Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.
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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.