Zheng Xinchang, Zong Wenting, Li Zhaohua, Ma Yingke, Sun Yanling, Xiong Zhuang, Wu Song, Yang Fei, Zhao Wei, Bu Congfan, Du Zhenglin, Xiao Jingfa, Bao Yiming
National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, China.
CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, China.
Front Genet. 2022 Aug 11;13:956781. doi: 10.3389/fgene.2022.956781. eCollection 2022.
Due to the explosion of cancer genome data and the urgent needs for cancer treatment, it is becoming increasingly important and necessary to easily and timely analyze and annotate cancer genomes. However, tumor heterogeneity is recognized as a serious barrier to annotate cancer genomes at the individual patient level. In addition, the interpretation and analysis of cancer multi-omics data rely heavily on existing database resources that are often located in different data centers or research institutions, which poses a huge challenge for data parsing. Here we present CCAS (Cancer genome Consensus Annotation System, https://ngdc.cncb.ac.cn/ccas/#/home), a one-stop and comprehensive annotation system for the individual patient at multi-omics level. CCAS integrates 20 widely recognized resources in the field to support data annotation of 10 categories of cancers covering 395 subtypes. Data from each resource are manually curated and standardized by using ontology frameworks. CCAS accepts data on single nucleotide variant/insertion or deletion, expression, copy number variation, and methylation level as input files to build a consensus annotation. Outputs are arranged in the forms of tables or figures and can be searched, sorted, and downloaded. Expanded panels with additional information are used for conciseness, and most figures are interactive to show additional information. Moreover, CCAS offers multidimensional annotation information, including mutation signature pattern, gene set enrichment analysis, pathways and clinical trial related information. These are helpful for intuitively understanding the molecular mechanisms of tumors and discovering key functional genes.
由于癌症基因组数据的爆炸式增长以及癌症治疗的迫切需求,轻松、及时地分析和注释癌症基因组变得越来越重要且必要。然而,肿瘤异质性被认为是在个体患者层面注释癌症基因组的严重障碍。此外,癌症多组学数据的解读和分析严重依赖于通常位于不同数据中心或研究机构的现有数据库资源,这给数据解析带来了巨大挑战。在此,我们展示了CCAS(癌症基因组共识注释系统,https://ngdc.cncb.ac.cn/ccas/#/home),这是一个针对个体患者的多组学水平一站式综合注释系统。CCAS整合了该领域20种广泛认可的资源,以支持涵盖395个亚型的10类癌症的数据注释。来自每种资源的数据通过使用本体框架进行人工整理和标准化。CCAS接受单核苷酸变异/插入或缺失、表达、拷贝数变异和甲基化水平的数据作为输入文件,以构建共识注释。输出以表格或图形的形式呈现,可以进行搜索、排序和下载。带有附加信息的扩展面板用于简洁展示,大多数图形具有交互性以显示更多信息。此外,CCAS提供多维注释信息,包括突变特征模式、基因集富集分析、通路和临床试验相关信息。这些有助于直观理解肿瘤的分子机制并发现关键功能基因。