Overview
Welcome to CGGA - the Chinese Glioma Genome Atlas! The CGGA database is a user-friendly web application for data storage and analysis to explore brain tumors datasets over 2,000 samples from Chinese cohorts. This database includes image-genomic data (274), single-cell sequencing data (73), whole-exome sequencing data (286), DNA methylation data (159), mRNA sequencing data (1,018), mRNA microarray data (301) and microRNA microarray data (198) and matched clinical data. The analyse tool allows users to browse DNA mutation profile, mRNA/microRNA expression profile and methylation profile, and to do correlation and survival analysis in specific glioma subtype.
Citation: Zhao Z, Zhang KN, Wang Q, et al. Chinese Glioma
Genome Atlas (CGGA): A Comprehensive Resource with Functional
Genomic Data from Chinese Glioma Patients. Genomics, Proteomics &
Bioinformatics. 2021 Feb;19(1):1-12. doi:10.1016/j.gpb.2020.10.005
News
- New Add a tool to query SCI IF (2022) (June 28, 2022)
- New Open access to read counts data for mRNAseq_693 and mRNAseq_325 datasets (June 20, 2022)
- CGGA has been recognized as one of 2021 China’s top 10 research advances in bioinformatics (February 18, 2022)
- The radiomics article is published online in the Brain journal (February 6, 2022)
- Open access to single cell sequencing data from the CGGA network (6,148 cells from 73 regions of 13 glioma patients) (January 4, 2022)
- Release the glioma image-genomic data from CGGA Network (274 glioma patients) (April 15, 2021).
- Release the single-cell sequencing data from CGGA Network (6,148 cells from 73 regions of 13 glioma patients) (April 13, 2021).
- This article is published online in the Genomics, Proteomics and Bioinformatics journal (March 2, 2021)
- This article is accepted by the Genomics, Proteomics and Bioinformatics journal (December 26, 2020)
- Expression profiles from TCGA and Rembrandt were added (June 14, 2020)
- Expression profiles of 20 non-glioma patients were added (June 14, 2020)
- Interactive function to show the sample details was developed (June 14, 2020)
- Update clinical information (May 6, 2020)
- Submit the database article to bioRxiv (January 21, 2020)
- Update clinical information (November 28, 2019)
- Add download statistics tool in download page (October 21, 2019)
- Raw sequencing data were submitted to BIGD database (October 1, 2019)
- Update to provide data presentation and R code in analysis results (September 27, 2019)
- Update clinical information (September 9, 2019)
- Website started to build (June 7, 2019)
Statistics
| Dataset | Data Type | Sample Number | Clinical Data | Analyse | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #total | #pLGG | #rLGG | #pGBM | #rGBM | #sGBM | histology | grade | age | gender | therapy | survival | ||||
| NewscRNA-seq | STRT-seq | 73 | total 6,148 cells, involving in 73 surgically obtained biopsies from 13 patients. | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | under construction | |||||
| Image-genomic data | MRI | 274 | 274 glioma patients were collected retrospectively. | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | under construction | |||||
| WEseq_286 | WESeq | 286 | 126 | 58 | 54 | 48 | 0 | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| mRNAseq_693 (batch 1) |
RNA-seq | 693 | 282 | 161 | 140 | 109 | 0 | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| mRNAseq_325 (batch 2) |
RNA-seq | 325 | 144 | 38 | 85 | 24 | 30 | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| mRNA-array_301 | Microarray | 301 | 156 | 18 | 108 | 5 | 11 | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| methyl_159 | Microarray | 159 | 100 | 8 | 33 | 4 | 6 | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| microRNA-array_198 | Microarray | 198 | 99 | 8 | 81 | 4 | 6 | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
Features
Analyse the WEseq data
Analyse the mRNA data
- Browse expression pattern of interest of gene in a specific dataset
- Browse co-expression pattern of interest of gene pair in a specific dataset
- To do survival analysis
Analyse the methylation data
- Browse DNA methylation pattern of interest of gene in a specific dataset
- Browse co-expression pattern of interest of gene pair in a specific dataset
- To do survival analysis
Analyse the microRNA data
- Browse expression pattern of interest of microRNA in a specific dataset
- Browse co-expression pattern of interest of microRNA pair in a specific dataset
- To do survival analysis
Download CGGA data
Questions or comments
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