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Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients
Gliomas are the most common and malignant intracranial tumors in adults. Recent studies have revealed the significance of functional genomics for glioma pathophysiological studies and treatments. However, access to comprehensive genomic data and analytical platforms is often limited. Here, we develo...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8498921/ https://www.ncbi.nlm.nih.gov/pubmed/33662628 http://dx.doi.org/10.1016/j.gpb.2020.10.005 |
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author | Zhao, Zheng Zhang, Ke-Nan Wang, Qiangwei Li, Guanzhang Zeng, Fan Zhang, Ying Wu, Fan Chai, Ruichao Wang, Zheng Zhang, Chuanbao Zhang, Wei Bao, Zhaoshi Jiang, Tao |
author_facet | Zhao, Zheng Zhang, Ke-Nan Wang, Qiangwei Li, Guanzhang Zeng, Fan Zhang, Ying Wu, Fan Chai, Ruichao Wang, Zheng Zhang, Chuanbao Zhang, Wei Bao, Zhaoshi Jiang, Tao |
author_sort | Zhao, Zheng |
collection | PubMed |
description | Gliomas are the most common and malignant intracranial tumors in adults. Recent studies have revealed the significance of functional genomics for glioma pathophysiological studies and treatments. However, access to comprehensive genomic data and analytical platforms is often limited. Here, we developed the Chinese Glioma Genome Atlas (CGGA), a user-friendly data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples from Chinese cohort. Currently, open access is provided to whole-exome sequencing data (286 samples), mRNA sequencing (1018 samples) and microarray data (301 samples), DNA methylation microarray data (159 samples), and microRNA microarray data (198 samples), and to detailed clinical information (age, gender, chemoradiotherapy status, WHO grade, histological type, critical molecular pathological information, and survival data). In addition, we have developed several tools for users to analyze the mutation profiles, mRNA/microRNA expression, and DNA methylation profiles, and to perform survival and gene correlation analyses of specific glioma subtypes. This database removes the barriers for researchers, providing rapid and convenient access to high‐quality functional genomic data resources for biological studies and clinical applications. CGGA is available at http://www.cgga.org.cn. |
format | Online Article Text |
id | pubmed-8498921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-84989212021-10-12 Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients Zhao, Zheng Zhang, Ke-Nan Wang, Qiangwei Li, Guanzhang Zeng, Fan Zhang, Ying Wu, Fan Chai, Ruichao Wang, Zheng Zhang, Chuanbao Zhang, Wei Bao, Zhaoshi Jiang, Tao Genomics Proteomics Bioinformatics Database Gliomas are the most common and malignant intracranial tumors in adults. Recent studies have revealed the significance of functional genomics for glioma pathophysiological studies and treatments. However, access to comprehensive genomic data and analytical platforms is often limited. Here, we developed the Chinese Glioma Genome Atlas (CGGA), a user-friendly data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples from Chinese cohort. Currently, open access is provided to whole-exome sequencing data (286 samples), mRNA sequencing (1018 samples) and microarray data (301 samples), DNA methylation microarray data (159 samples), and microRNA microarray data (198 samples), and to detailed clinical information (age, gender, chemoradiotherapy status, WHO grade, histological type, critical molecular pathological information, and survival data). In addition, we have developed several tools for users to analyze the mutation profiles, mRNA/microRNA expression, and DNA methylation profiles, and to perform survival and gene correlation analyses of specific glioma subtypes. This database removes the barriers for researchers, providing rapid and convenient access to high‐quality functional genomic data resources for biological studies and clinical applications. CGGA is available at http://www.cgga.org.cn. Elsevier 2021-02 2021-03-02 /pmc/articles/PMC8498921/ /pubmed/33662628 http://dx.doi.org/10.1016/j.gpb.2020.10.005 Text en © 2021 The Authors. Published by Elsevier B.V. and Science Press on behalf of Beijing Institute of Genomics, Chinese Academy of Sciences and Genetics Society of China. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Database Zhao, Zheng Zhang, Ke-Nan Wang, Qiangwei Li, Guanzhang Zeng, Fan Zhang, Ying Wu, Fan Chai, Ruichao Wang, Zheng Zhang, Chuanbao Zhang, Wei Bao, Zhaoshi Jiang, Tao Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients |
title | Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients |
title_full | Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients |
title_fullStr | Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients |
title_full_unstemmed | Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients |
title_short | Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients |
title_sort | chinese glioma genome atlas (cgga): a comprehensive resource with functional genomic data from chinese glioma patients |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8498921/ https://www.ncbi.nlm.nih.gov/pubmed/33662628 http://dx.doi.org/10.1016/j.gpb.2020.10.005 |
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