sportsXbiodata
sportsXbiodata is a comprehensive, manually curated multi-omics database dedicated to exercise biology. It contains 2,180 sequencing datasets and 3,600 publications covering exercise, metabolism, nutrition, and disease, with data from sources including NCBI, MoTrPAC, GEO, PubMed, and GeneCard. The database integrates multiple omics layers—RNA-seq, proteomics, phosphoproteomics—and offers information on gene expression, differential expression and annotation, integrated gene-protein profiles, phenotype-gene correlations, immune associations, and knowledge graphs. It features visualization options ready for publication, supports user-uploaded data for exercise-related gene enrichment analysis, and includes an exercise-drug homology module to identify key targets linking exercise and drug interventions. sportsXbiodata provides a powerful resource for advancing research on the molecular mechanisms of exercise and exercise-related diseases.
Description of the sample
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Main functions
Data processing
Website Structure
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Citation
Please cite:
Li Z, Xu Z, Li X. SportsXbiodata: A web platform for integrative analysis of exercise-induced gene and protein expression. iScience. 2025 Dec 11;29(1):114403. doi: 10.1016/j.isci.2025.114403. PMID: 41550722; PMCID: PMC12803929.
Human Gene Search
Conveniently querying gene expression differences across various exercise-related tissues and experimental conditions.
Gene Input
GEO Result
GEO Data Table
Mouse Gene Search
Conveniently querying gene expression differences across various exercise-related tissues and experimental conditions.
Gene Input
MoTrPAC Result
MoTrPAC Data Table
GEO Result
GEO Data Table
Knowledge graph
By compiling a comprehensive collection of exercise-related literature and entering specific keywords, an exercise-gene knowledge graph can be automatically generated.
Enter keywords
Knowledge graph Result
Knowledge graph Table
Paper-GENE
Paper-GENE is a literature query module within the database, encompassing 3,600 publications and enabling rapid association of genes with research topics.
Results Table
Gene Changes at Different Duration
Investigate the dynamic changes of specific genes across various tissues at different exercise time points.e
GeneNAME input
Result
Phenotype and Gene
Evaluate the correlation between the expression of target genes across different organs and phenotypic data, which may indicate potential associations between gene expression and final phenotypic traits such as body weight.
GeneNAME input
Result
Data Table
immune cell Gene Related
Assess the correlation between immune cells and the input genes, with example data available for download.
Upload Data File GENE names
Result
Data Table
proteomics
To examine the expression of specific proteins in various organs pre- and post-exercise.
protein input
Result
Table result
phosphoproteomic
Analyze the changes in phosphorylation sites before and after exercise.
ProteinNAME input
Result
Data Table
Multi-omics
This module visualizes multi-omics data from an eight-week exercise intervention in mice, comparing mRNA and protein expression changes across 12 organs with customizable experimental conditions.
SportEnrich-GSEA
This module performs GSEA using 120 exercise-related gene sets to reveal how different exercise types (e.g., aerobic or resistance training) influence molecular changes, automatically generating enrichment plots from uploaded sequencing data.
Exercise-Drug Convergence
This module integrates exercise and drug intervention multi-omics data to identify shared molecular pathways and potential exercise-drug interaction networks.
Difference analysis of datasets
Result
Data Table
PPA–PA Integration
This module is designed to perform visualization and integrative analysis of proteomics and phosphoproteomics data generated from an eight-week exercise intervention experiment in mice, thereby facilitating an in-depth exploration of their biological significance
Difference analysis of datasets
Result
PPA–PA Integration
Phosphorylation site changes
Metabolomics
This module visualizes the metabolomics data obtained from the eight-week exercise intervention experiment on mice for display, and also supports the customization of experimental conditions.
About Me
Zheng Li
I am a university lecturer specializing in rehabilitation medicine and bioinformatics. My research interests include exercise interventions, multi-omics data analysis, and the development of bioinformatics tools for sports science.
Contact Information
- Email: lizhengsix@proton.me
- Institution: School of Sports Science and Technology, Guangzhou College of Applied Science and Technology
User Support
If you encounter any errors, or aspects of this tool that you find confusing, please feel free to contact me via email. Your feedback is valuable and will help improve this platform.
Update Log
2025-11-13: Subcutaneous fat sequencing data GSE303983 after acute aerobic exercise was added
2025-10-14: debug to Multi-omics
2025-10-12: Integrate metabolomics, proteomics - phosphoproteomics data analysis results and improve the analysis of gender differences