Evidence map›Paper›PMID 40041113›Full record

ArticleBioinformatics advances2025

easyEWAS: a flexible and user-friendly R package for epigenome-wide association study.

Yuting Wang, Meijie Jiang, Siyuan Niu, Xu Gao

Abstract read
In one paragraph

Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

4 authors.

Yuting WangDepartment of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China.
Meijie JiangDepartment of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China.
Siyuan NiuDepartment of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China.
Xu GaoDepartment of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China.ORCID https://orcid.org/0000-0001-6506-6084

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Rapid advancements in high-throughput sequencing technologies especially the Illumina DNA methylation Beadchip greatly fuelled the surge in epigenome-wide association study (EWAS), providing crucial insights into intrinsic DNA methylation modifications associated with environmental exposure, diseases, and health traits. However, current tools are complex and less user-friendly to accommodate appropriate EWAS designs and make downstream analyses and result interpretations complicated, especially for clinicians and public health professionals with limited bioinformatic skills. Results: We integrated the current state-of-the-art EWAS analysis methods and tools to develop a flexible and user-friendly R package easyEWAS for conducting DNA methylation-based research using Illumina DNA methylation Beadchips. With easyEWAS, we provide a battery of statistical methods to support differential methylation position analysis across various scenarios, as well as differential methylation region analysis based on the DMRcate method. To facilitate result interpretation, we provide comprehensive functional annotation and result visualization functionalities. Additionally, a bootstrap-based internal validation was incorporated into easyEWAS to ensure the robustness of EWAS results. Evaluation in asthma patients as the example demonstrated that easyEWAS could simplify and streamline the conduction of EWAS and corresponding downstream analyses, thus effectively advancing DNA methylation research in public health and clinical settings. Availability and implementation: easyEWAS is implemented as an R package and is available at https://github.com/ytwangZero/easyEWAS.

Identifiers

PMID40041113
PMCPMC11878637

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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.