Overview
Welcome to the CGGA - Chinese Glioma Genome Atlas! The CGGA database contains clinical and sequencing data of over 2,000 brain tumor samples from Chinese cohorts, and is equiped with a user-friendly web application for data storage and exploration. Currently, the CGGA includes image-genomic data (274), scRNA-seq data (6,148 cells, 73 regions from 14 patients), whole-exome sequencing data (286), mRNA sequencing data (693, batch 2), mRNA sequencing data (325, batch 1), mRNA microarray data (301), DNA methylation data (159), microRNA microarray data (198), mRNA seqencing data (20, non-glioma), and matched clinical data. In addition, we provide some tools to analyse DNA mutation profile, mRNA expression profile, methylation profile, and microRNA expression profile. Some utilities have been developed to allow users to analyze their own data. We hope that the CGGA database will remove barriers for researchers and provide fast and easy access to high quality functional genomic data resources for biological research and clinical applications.
News
- NewOpen access to read counts data for RNA-seq of non-glioma controls (Jan 4, 2023)
- The WES data article is published online in the Scientific Data journal (November 11, 2022)
- The article of a multicenter retrospective study from CGGA is published online in the Cancer Biology & Medicine journal (October 10, 2022)
- Add a tool to query SCI IF (2022) (June 28, 2022)
- 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 | 14 | total 6,148 cells, involving in 73 surgically obtained biopsies from 14 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