Evidence map›Paper›PMID 42251418›Full record

ReviewJournal of translational medicine2026

Long-read sequencing technologies and bioinformatics: a new perspective for decoding DNA methylation modifications.

Hanjing Hou, Yuanfeng Zhang, Yu Ma, Yanxi Han, Jinming Li, Rui Zhang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Hanjing Hou *National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, P. R. China.
Yuanfeng Zhang *National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, P. R. China.
Yu MaNational Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, P. R. China.
Yanxi HanNational Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, P. R. China.
Jinming LiNational Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, P. R. China. jmli@nccl.org.cn.
Rui ZhangNational Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, P. R. China. ruizhang@nccl.org.cn.ORCID 0000-0003-4660-2042

Funding

Beijing Natural Science Foundation L254025the National Key R&D Program of China 2023YFC3402503the Noncommunicable Chronic Diseases-National Science and Technology Major Project 2024ZD0533100/2024ZD0533102
6 · The paper itself

Abstract

backgroundDNA methylation is a crucial epigenetic regulatory mechanism in eukaryotes, and its dysregulation is closely associated with numerous diseases. Recent advances in long-read sequencing (LRS) have transformed the ability to comprehensively characterize the methylation modifications in DNA, including the technically difficult genomic regions. However, the translation of this technological potential into reliable biological insights relies heavily on accurate computational analysis of raw signal data. Currently, a growing number of bioinformatic tools have emerged, demonstrating superior performance in LRS-based methylation detection. MAIN BODY: Our review first briefly describes the detection principles and technological improvement of LRS, and then provides a comprehensive overview of the existing LRS-based methylation detection tools and related benchmarking studies. We further explore the applications in biomedical research, the current challenges, and the future perspective of LRS-based methylation detection.

conclusionsBy highlighting the research progress and key issues in the field, this review aims to provide researchers with an essential framework to advance the further development and application of LRS-based methylation detection.

Indexed as

Computational BiologyDNA MethylationSequence Analysis, DNAAnimalsEpigenesis, GeneticHumansDNA methylationEpigeneticsLong-read sequencing

Identifiers

PMID42251418
PMCPMC13471473

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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.