| EAP: A versatile cloud-based platform for efficient quantitative analysis of large-scale ChIP/ATAC-seq datasets |
| 论文作者 |
Zheng, GY; Chen, HJ; Guo, ZJ; Ma, LX; Zheng, AQ; Huang, T; Chen, WR; Tu, SQ; Li, YX; Shao, Z |
| 期刊/会议名称 |
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL |
| 论文年度 |
2025 |
| 论文类别 |
|
| 摘要 |
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. |
| 卷 |
27 |
| 影响因子 |
4.1 |
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