Evidence map›Paper›PMID 34761274›Full record

ArticleNucleic acids research2022

scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species.

Tianshun Gao, Zilong Zheng, Yihang Pan, Chengming Zhu, Fuxin Wei, Jinqiu Yuan, Rui Sun, Shuo Fang, Nan Wang, Yang Zhou and 1 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. scBlood: A comprehensive single-cell accessible chromatin database of blood cells.Computational and structural biotechnology journal · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Molecular therapy. Nucleic acids · 2023
    Article
  12. Review
  13. Article
  14. Article
  15. Article
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

11 authors.

Tianshun GaoBig Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.ORCID 0000-0002-0466-9081
Zilong ZhengBig Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.
Yihang PanBig Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.
Chengming ZhuScientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.
Fuxin WeiDepartment of Orthopaedics, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.
Jinqiu YuanBig Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.
Rui SunBig Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.
Shuo FangBig Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.
Nan WangScientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.
Yang ZhouBig Data Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China.
Jiang QianThe Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD 21231, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous studies on enhancers and their target genes were largely based on bulk samples that represent 'average' regulatory activities from a large population of millions of cells, masking the heterogeneity and important effects from the sub-populations. In recent years, single-cell sequencing technology has enabled the profiling of open chromatin accessibility at the single-cell level (scATAC-seq), which can be used to annotate the enhancers and promoters in specific cell types. A comprehensive resource is highly desirable for exploring how the enhancers regulate the target genes at the single-cell level. Hence, we designed a single-cell database scEnhancer (http://enhanceratlas.net/scenhancer/), covering 14 527 776 enhancers and 63 658 600 enhancer-gene interactions from 1 196 906 single cells across 775 tissue/cell types in three species. An unsupervised learning method was employed to sort and combine tens or hundreds of single cells in each tissue/cell type to obtain the consensus enhancers. In addition, we utilized a cis-regulatory network algorithm to identify the enhancer-gene connections. Finally, we provided a user-friendly platform with seven useful modules to search, visualize, and browse the enhancers/genes. This database will facilitate the research community towards a functional analysis of enhancers at the single-cell level.

Indexed as

Databases, GeneticEnhancer Elements, GeneticSoftwareUnsupervised Machine LearningAnimalsCell LineageChromatinConsensus SequenceDrosophila melanogasterEukaryotic CellsGene Expression RegulationGene Regulatory NetworksGenetic HeterogeneityHumansInternetMiceChromatin

Identifiers

PMID34761274
PMCPMC8728125

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.