Evidence map›Paper›PMID 39881144›Full record

ArticleScientific reports2025

scATAC-seq generates more accurate and complete regulatory maps than bulk ATAC-seq.

E Ravza Gur, Jim R Hughes

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. DecipheringBioinformatics advances · 2026
    Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

E Ravza GurMRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK.
Jim R HughesMRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK. jim.hughes@imm.ox.ac.uk.

Funding

Medical Research Council MC_UU_00029/3Milli Eğitim Bakanliği YLSYWellcome Trust 225220Wellcome Trust 225220/Z/22/Z
6 · The paper itself

Abstract

Bulk ATAC-seq assays have been used to map and profile the chromatin accessibility of regulatory elements such as enhancers, promoters, and insulators. This has provided great insight into the regulation of gene expression in many cell types in a variety of organisms. To date, ATAC-seq has most often been used to provide an average evaluation of chromatin accessibility in populations of cells. The development of a single cell approach (scATAC-seq) assay enables researchers to evaluate chromatin accessibility in individual cells and identify sub-groups in mixed populations of cells. To investigate the full potential of single-cell epigenomic data, we have comprehensively compared the information derived from bulk ATAC-seq and scATAC-seq in populations of cells. We found that the chromatin architecture signal is the same using bulk ATAC-seq and scATAC-seq to analyse aliquots of the same cell population. However, scATAC-seq provides substantially higher data quality compared to bulk ATAC-seq improving the sensitivity to detect relatively weak, but functionally important ATAC-seq signals. Furthermore, we found that scATAC-seq identified differences in what was previously assumed to be a homogenous population of cells. Finally, we determined the number of cells required to generate aggregated open chromatin profiles from single cells and to identify biologically meaningful clusters after pseudo-bulking of data. This study illustrates the added value of using scATAC-seq rather than bulk ATAC-seq in evaluating both homogeneous and heterogeneous populations of cells. This paper provides a comprehensive guide on the benefits of using scATAC-seq data to study gene regulation.

Indexed as

ChromatinChromatin Immunoprecipitation SequencingRegulatory Sequences, Nucleic AcidSingle-Cell AnalysisHumansChromatinBulk ATAC-seqChromatin accessibilityClusteringGene regulationPseudo-bulkingRegulatory elementsscATAC-seqSingle-cell epigenomics

Identifiers

PMID39881144
PMCPMC11779887

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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.