TY - JOUR T1 - Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data for Chinese Glioma Patients JF - bioRxiv DO - 10.1101/2020.01.20.911982 SP - 2020.01.20.911982 AU - Zhao, Zheng AU - Zhang, Ke’nan AU - Wang, Qiangwei AU - Li, Guanzhang AU - Zeng, Fan AU - Zhang, Ying AU - Wu, Fan AU - Chai, Ruichao AU - Wang, Zheng AU - Zhang, Chuanbao AU - Zhang, Wei AU - Bao, Zhaoshi AU - Jiang, Tao Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/01/21/2020.01.20.911982.abstract N2 - Gliomas are the most common and malignant intracranial tumours in adults. Recent studies have shown that functional genomics greatly aids in the understanding of the pathophysiology and therapy of glioma. However, comprehensive genomic data and analysis platforms are relatively limited. In this study, we developed the Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn), a user-friendly data portal for storage and interactive exploration of multi-dimensional functional genomic data that includes nearly 2,000 primary and recurrent glioma samples from Chinese cohorts. CGGA currently provides access to whole-exome sequencing (286 samples), messenger RNA sequencing (1,018 samples) and microarray (301 samples), DNA methylation microarray (159 samples), and microRNA microarray (198 samples) data, as well as detailed clinical data (e.g., WHO grade, histological type, critical molecular genetic information, age, sex, chemoradiotherapy status and survival data). In addition, we developed an analysis tool to allow users to browse mutational, mRNA/microRNA expression, and DNA methylation profiles and perform survival and correlation analyses of specific glioma subtypes. CGGA greatly reduces the barriers between complex functional genomic data and glioma researchers who seek rapid, intuitive, and high-quality access to data resources and enables researchers to use these immeasurable data sources for biological research and clinical application. Importantly, the free provision of data will allow researchers to quickly generate and provide data to the research community. ER -