Evidence map›Paper›PMID 34356635›Full record

ReviewBiomolecules2021

Introduction to Single-Cell DNA Methylation Profiling Methods.

Jongseong Ahn, Sunghoon Heo, Jihyun Lee, Duhee Bang

Abstract readReview
In one paragraph

Review in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024
    Review
  13. Review
  14. Article
  15. Applications of single-cell multi-omics in liver cancer.JHEP reports : innovation in hepatology · 2024
    Review
  16. Article
  17. Article
  18. Article
  19. Functional genomics in Spiralia.Briefings in functional genomics · 2023
    Article
  20. 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

4 authors.

Jongseong AhnDepartment of Chemistry, Yonsei University, Seoul 03722, Korea.
Sunghoon HeoDepartment of Chemistry, Yonsei University, Seoul 03722, Korea.ORCID 0000-0003-2878-6108
Jihyun LeeDepartment of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, Seoul 02447, Korea.
Duhee BangDepartment of Chemistry, Yonsei University, Seoul 03722, Korea.

Funding

Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government MSIT; 2016M3A9B6948494Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea HI18C2282, HI14C1277Mid-career Researcher Program of the National Research Foundation (NRF) funded by the Korean government MSIT; 2018R1A2A1A05079172National Research Foundation (NRF) funded by the Korean government MSIT; 2021R1A2C2008490
6 · The paper itself

Abstract

DNA methylation is an epigenetic mechanism that is related to mammalian cellular differentiation, gene expression regulation, and disease. In several studies, DNA methylation has been identified as an effective marker to identify differences between cells. In this review, we introduce single-cell DNA-methylation profiling methods, including experimental strategies and approaches to computational data analysis. Furthermore, the blind spots of the basic analysis and recent alternatives are briefly described. In addition, we introduce well-known applications and discuss future development.

Indexed as

DNA MethylationGene Expression ProfilingSingle-Cell AnalysisAnimalsHumansbioinformaticsDNA methylationsingle cell

Identifiers

PMID34356635
PMCPMC8301785

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Registered trials

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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.