Evidence map›Paper›PMID 29333247›Full record

ArticleF1000Research

Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR.

Yunshun Chen, Bhupinder Pal, Jane E Visvader, Gordon K Smyth

Open access · goldAbstract read
In one paragraph

Article in F1000Research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed
4.1field-weighted citation impact, top 5% 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

68 citing papers in PubMed, 89 citations in OpenAlex.

  1. Article
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  7. BenchmarkingInternational journal of molecular sciences · 2026
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  13. Complementary genetic and epigenetic changes facilitate rapid adaptation to multiple global change stressors.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  17. Insights intoBiomolecules · 2025
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  18. Article
  19. Genetic variation drives cancer cell adaptation to ECM stiffness.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  20. Article

8 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

4 authors at 1 institution in 1 country.

Yunshun ChenThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Bhupinder PalThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Jane E VisvaderThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Gordon K SmythThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.ORCID https://orcid.org/0000-0001-9221-2892
The University of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cytosine methylation is an important DNA epigenetic modification. In vertebrates, methylation occurs at CpG sites, which are dinucleotides where a cytosine is immediately followed by a guanine in the DNA sequence from 5' to 3'. When located in the promoter region of a gene, DNA methylation is often associated with transcriptional silencing of the gene. Aberrant DNA methylation is associated with the development of various diseases such as cancer. Bisulfite sequencing (BS-seq) is the current "gold-standard" technology for high-resolution profiling of DNA methylation. Reduced representation bisulfite sequencing (RRBS) is an efficient form of BS-seq that targets CpG-rich DNA regions in order to save sequencing costs. A typical bioinformatics aim is to identify CpGs that are differentially methylated (DM) between experimental conditions. This workflow demonstrates that differential methylation analysis of RRBS data can be conducted using software and methodology originally developed for RNA-seq data. The RNA-seq pipeline is adapted to methylation by adding extra columns to the design matrix to account for read coverage at each CpG, after which the RRBS and RNA-seq pipelines are almost identical. This approach is statistically natural and gives analysts access to a rich collection of analysis tools including generalized linear models, gene set testing and pathway analysis. The article presents a complete start to finish case study analysis of RRBS profiles of different cell populations from the mouse mammary gland using the Bioconductor package edgeR. We show that lineage-committed cells are typically hyper-methylated compared to progenitor cells and this is true on all the autosomes but not the sex chromosomes. We demonstrate a strong negative correlation between methylation of promoter regions and gene expression as measured by RNA-seq for the same cell types, showing that methylation is a regulatory mechanism involved in epithelial linear commitment.

Indexed as

BioconductorBS-seqdifferential methylation analysisMethylation

Identifiers

PMID29333247
PMCPMC5747346
OpenAlexW2770138124

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.