Evidence map›Paper›PMID 41340888›Full record

ArticleComputational and structural biotechnology journal2025

EAP: A versatile cloud-based platform for efficient quantitative analysis of large-scale ChIP/ATAC-seq datasets.

Guangyong Zheng, Haojie Chen, Zhijie Guo, Liangxiao Ma, Anqin Zheng, Tao Huang, Weiran Chen, Shiqi Tu, Yixue Li, Zhen Shao

Erratum issuedAbstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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

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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

5 · Who and what money

Authors and funding

10 authors.

Guangyong ZhengCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Haojie ChenCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Zhijie GuoCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Liangxiao MaCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Anqin ZhengCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Tao HuangCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Weiran ChenCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Shiqi TuCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Yixue LiCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Zhen ShaoCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epigenome profiling techniques such as ChIP-seq and ATAC-seq have revolutionized our understanding of gene expression regulation in many biological processes. The rapidly increasing volume of these data necessitates the development of an integrated platform equipped with powerful computational resources and a versatile suite of analytical tools to facilitate in-depth epigenomic analysis. However, most existing cloud-based analysis platforms still require environment configuration, workflow optimization, or even cloud infrastructure management, posing significant barriers for researchers, particularly experimental biologists. To address this demand, we have developed EAP (Epigenomic Analysis Platform; https://www.biosino.org/epigenetics), a scalable web platform based on cloud technology for efficient and reproducible analysis of large-scale ChIP/ATAC-seq datasets. EAP provides a configuration-free environment and extensive downstream functional analysis capabilities, distinguishing it from existing cloud-based ChIP/ATAC-seq analysis tools. Moreover, EAP integrates a curated collection of computational tools, many of which were recently developed by us, supporting both supervised and unsupervised analyses on heterogeneous datasets. This design enables researchers from diverse backgrounds to perform analyses ranging from data preprocessing to tumor subtyping, therapeutic target discovery, and gaining biological insights into epigenomic dynamics during tissue development and disease progression.

Indexed as

Cancer epigenomic subtypingCloud-based platformInferred TF motif associated chromatin occupancy/accessibilityLarge-scale ATAC/ChIP-seq dataQuantitative analysis

Identifiers

PMID41340888
PMCPMC12670574

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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.