Evidence map›Paper›PMID 27358654›Full record

ReviewEpigenetics & chromatin2016

Profiling genome-wide DNA methylation.

Wai-Shin Yong, Fei-Man Hsu, Pao-Yang Chen

Abstract readReview
In one paragraph

Review in Epigenetics & chromatin, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
161citing papers in PubMed, 2 pooled it
–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

161 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Review
  6. Review
  7. Article
  8. Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Estimating population structure using epigenome-wide methylation data.medRxiv : the preprint server for health sciences · 2025
    Article
  15. Article
  16. A comprehensive guide to genomeZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025
    Review
  17. Article
  18. Article
  19. Article
  20. Review

101 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

3 authors.

Wai-Shin Yong *Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 11529 Taiwan, ROC.
Fei-Man Hsu *Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8561 Japan.
Pao-Yang ChenInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, 11529 Taiwan, ROC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA methylation is an epigenetic modification that plays an important role in regulating gene expression and therefore a broad range of biological processes and diseases. DNA methylation is tissue-specific, dynamic, sequence-context-dependent and trans-generationally heritable, and these complex patterns of methylation highlight the significance of profiling DNA methylation to answer biological questions. In this review, we surveyed major methylation assays, along with comparisons and biological examples, to provide an overview of DNA methylation profiling techniques. The advances in microarray and sequencing technologies make genome-wide profiling possible at a single-nucleotide or even a single-cell resolution. These profiling approaches vary in many aspects, such as DNA input, resolution, genomic region coverage, and bioinformatics analysis, and selecting a feasible method requires knowledge of these methods. We first introduce the biological background of DNA methylation and its pattern in plants, animals and fungi. We present an overview of major experimental approaches to profiling genome-wide DNA methylation and hydroxymethylation and then extend to the single-cell methylome. To evaluate these methods, we outline their strengths and weaknesses and perform comparisons across the different platforms. Due to the increasing need to compute high-throughput epigenomic data, we interrogate the computational pipeline for bisulfite sequencing data and also discuss the concept of identifying differentially methylated regions (DMRs). This review summarizes the experimental and computational concepts for profiling genome-wide DNA methylation, followed by biological examples. Overall, this review provides researchers useful guidance for the selection of a profiling method suited to specific research questions.

Indexed as

Bisulfite sequencingDNA methylationHydroxymethylationMethylomeRRBSSingle-cellWGBS

Identifiers

PMID27358654
PMCPMC4926291

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.