Evidence map›Paper›PMID 34737275›Full record

ArticleNature communications2021

Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen.

Zhijian Li, Christoph Kuppe, Susanne Ziegler, Mingbo Cheng, Nazanin Kabgani, Sylvia Menzel, Martin Zenke, Rafael Kramann, Ivan G Costa

Abstract read
In one paragraph

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 73 papers.

0numbers the graph read from it
0cells of the map it votes in
73citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

73 citing papers in PubMed.

  1. Review
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  14. Nanoparticles in the Treatment of Renal Fibrosis.International journal of nanomedicine · 2026
    Review
  15. Article
  16. Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025
    Review
  17. Article
  18. Article
  19. Article
  20. Article

13 more citing papers are in PubMed but not listed here.

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

9 authors.

Zhijian Li *Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany.ORCID http://orcid.org/0000-0002-1523-1333
Christoph Kuppe *Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany.ORCID http://orcid.org/0000-0003-4597-9833
Susanne ZieglerInstitute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany.
Mingbo ChengInstitute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany.
Nazanin KabganiInstitute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany.
Sylvia MenzelInstitute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany.
Martin ZenkeDepartment of Cell Biology, Institute of Biomedical Engineering, RWTH Aachen University Medical School, 52074, Aachen, Germany.
Rafael KramannInstitute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany. rkramann@gmx.net.ORCID http://orcid.org/0000-0003-4048-6351
Ivan G CostaInstitute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany. ivan.costa@rwth-aachen.de.ORCID http://orcid.org/0000-0003-2890-8697

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A major drawback of single-cell ATAC-seq (scATAC-seq) is its sparsity, i.e., open chromatin regions with no reads due to loss of DNA material during the scATAC-seq protocol. Here, we propose scOpen, a computational method based on regularized non-negative matrix factorization for imputing and quantifying the open chromatin status of regulatory regions from sparse scATAC-seq experiments. We show that scOpen improves crucial downstream analysis steps of scATAC-seq data as clustering, visualization, cis-regulatory DNA interactions, and delineation of regulatory features. We demonstrate the power of scOpen to dissect regulatory changes in the development of fibrosis in the kidney. This identifies a role of Runx1 and target genes by promoting fibroblast to myofibroblast differentiation driving kidney fibrosis.

Indexed as

ChromatinDNASequence Analysis, DNASingle-Cell AnalysisChromatinDNA

Identifiers

PMID34737275
PMCPMC8568974

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.