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Updated: Jun 11, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
Published on: July 29, 2022
GEPREP: A comprehensive data atlas of RNA-seq-based gene expression profiles of exercise responses
Lei Sun1, Jinwen Luan2, Jinbiao Wang3
1School of Information Engineering, Yangzhou University, Yangzhou 225127, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China.
Background:
Physical activity can regulate and affect gene expression in multiple tissues and cells. Recently, with the development of next-generation sequencing, a large number of RNA-sequencing (RNA-seq)-based gene expression profiles about physical activity have been shared in public resources; however, they are poorly curated and underutilized. To tackle this problem, we developed a data atlas of such data through comprehensive data collection, curation, and organization.
Methods:
The data atlas, termed gene expression profiles of RNA-seq-based exercise responses (GEPREP), was built on a comprehensive collection of high-quality RNA-seq data on exercise responses. The metadata of each sample were manually curated. Data were uniformly processed and batch effects corrected. All the information was well organized in an easy-to-use website for free search, visualization, and download.
Results:
GEPREP now includes 69 RNA-seq datasets of pre- and post-exercise, comprising 26 human datasets (1120 samples) and 43 mouse datasets (1006 samples). Specifically, there were 977 (87.2 %) human samples of skeletal muscle and 143 (12.8 %) human samples of blood. There were also samples across 9 mice tissues with skeletal muscle (359, 35.7 %) and brain (280, 27.8 %) accounting for the main fractions. Metadata-including subject, exercise interventions, sampling sites, and post-processing methods-are also included. The metadata and gene expression profiles are freely accessible at http://www.geprep.org.cn/.
Conclusion:
GEPREP is a comprehensive data atlas of RNA-seq-based gene expression profiles responding to exercise. With its reliable annotations and user-friendly interfaces, it has the potential to deepen our understanding of exercise physiology.
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