Evidence map›Paper›PMID 27895806›Full record

ReviewClinical epigenetics2016

Genome-wide epigenomic profiling for biomarker discovery.

René A M Dirks, Hendrik G Stunnenberg, Hendrik Marks

Open access · goldAbstract readReview
In one paragraph

Review in Clinical epigenetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
5.6field-weighted citation impact, top 4% of its field
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

37 citing papers in PubMed, 98 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. The Evolution of Next-Generation Sequencing Technologies.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Epigenetic modification in diabetic kidney disease.Frontiers in endocrinology · 2023
    Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. 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

3 authors at 1 institution in 1 country.

René A M DirksDepartment of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6500HB Nijmegen, The Netherlands.
Hendrik G StunnenbergDepartment of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6500HB Nijmegen, The Netherlands.
Hendrik MarksDepartment of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6500HB Nijmegen, The Netherlands.ORCID 0000-0002-4198-3731
Radboud University Nijmegen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A myriad of diseases is caused or characterized by alteration of epigenetic patterns, including changes in DNA methylation, post-translational histone modifications, or chromatin structure. These changes of the epigenome represent a highly interesting layer of information for disease stratification and for personalized medicine. Traditionally, epigenomic profiling required large amounts of cells, which are rarely available with clinical samples. Also, the cellular heterogeneity complicates analysis when profiling clinical samples for unbiased genome-wide biomarker discovery. Recent years saw great progress in miniaturization of genome-wide epigenomic profiling, enabling large-scale epigenetic biomarker screens for disease diagnosis, prognosis, and stratification on patient-derived samples. All main genome-wide profiling technologies have now been scaled down and/or are compatible with single-cell readout, including: (i) Bisulfite sequencing to determine DNA methylation at base-pair resolution, (ii) ChIP-Seq to identify protein binding sites on the genome, (iii) DNaseI-Seq/ATAC-Seq to profile open chromatin, and (iv) 4C-Seq and HiC-Seq to determine the spatial organization of chromosomes. In this review we provide an overview of current genome-wide epigenomic profiling technologies and main technological advances that allowed miniaturization of these assays down to single-cell level. For each of these technologies we evaluate their application for future biomarker discovery. We will focus on (i) compatibility of these technologies with methods used for clinical sample preservation, including methods used by biobanks that store large numbers of patient samples, and (ii) automation of these technologies for robust sample preparation and increased throughput.

Indexed as

DNA MethylationBinding SitesBiological Specimen BanksChromatinChromosomes, HumanDNAEpigenomicsGenetic MarkersHumansPrecision MedicineSequence Analysis, DNASingle-Cell AnalysisSpecimen HandlingChromatinDNAGenetic MarkersATAC-SeqAutomationBiomarker discoveryDNA methylationGenome-wide epigenetic profilingMiniaturizationPrecision medicineSingle cellStratificationWGBS

Identifiers

PMID27895806
PMCPMC5117701
OpenAlexW2551651776

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.