Evidence map›Paper›PMID 42409588›Full record

ArticleLife science alliance2026

DiffMethylTools: a toolbox for the detection, annotation, and visualization of differential methylation.

Houssemeddine Derbel, Evan Kinnear, Justin J-L Wong, Qian Liu

Abstract read
In one paragraph

Article in Life science alliance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Houssemeddine DerbelNevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV, USA.
Evan KinnearNevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV, USA.
Justin J-L WongEpigenetics and RNA Biology Laboratory, School of Medical Sciences, The University of Sydney, Camperdown, Australia.ORCID https://orcid.org/0000-0002-7038-5079
Qian LiuNevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV, USA qian.liu@unlv.edu.ORCID https://orcid.org/0000-0002-6737-4969

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA methylation is a fundamental epigenetic mechanism, and its significant changes (i.e., differential methylation) regulate gene expression, cell-type specification, and disease progression without altering the underlying DNA sequence. Differential methylation has traditionally been detected by statistical comparison of two groups using existing tools and has supported a wide range of downstream analyses in human disease studies. However, few toolboxes efficiently integrate robust detection, annotation, and visualization of differential methylation, and the reproducibility of detected signals across tools remains limited. Moreover, existing methods have rarely been evaluated on long-read methylomes, despite their increasing availability. Here, we present DiffMethylTools, an end-to-end framework designed to address analytical and computational challenges in differential methylation studies. DiffMethylTools generates robust differential methylation detection with comprehensive annotation and visualization modules to streamline analysis workflows. Benchmarking across six datasets, including three long-read methylomes, shows that DiffMethylTools outperforms four widely used tools in detecting differential methylation, with improved overall performance and reproducibility. In addition, DiffMethylTools offers compatibility with upstream methylation-calling pipelines, and facilitates downstream biological interpretation through flexible annotation and visualization capabilities.

Indexed as

Computational BiologyDNA MethylationEpigenomicsSoftwareEpigenesis, GeneticHumansMolecular Sequence AnnotationReproducibility of ResultsSequence Analysis, DNA

Identifiers

PMID42409588
PMCPMC13338798

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LicenceCC BY
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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.