The Cancer Genome Atlas Data Analysis Center

TCGA的首要目标是改变癌症医学的实践，改善患者的生活质量。 通过癌症基因组学生存。一个关键的交付成果是能够访问和使用复杂的多 三维基因组数据用于下游研究。我们建议在GDAC-A中心的 开发分析管道所需的领导力、专业知识和基础设施， 定义的综合分析和解释，是专门设计的假设测试的基本， 翻译和临床研究人员。我们的团队由癌症生物学、基因组学和 生物信息学与TCGA的领导记录。分析工具和管道结构是 基于我们丰富的TCGA经验，旨在最佳地实现其目标。这条管线将 使用GenePattern生物信息学工作流环境构建-一种灵活的模块化架构 符合caBIG和caGRID标准，在完善、可靠和安全的IT基础设施中维护 在布罗德研究所，可以作为生产管道24/7运行。 我们会善用这方面的资源，达致以下的具体目标。 目标1.我们将为所有输入和输出文件定义符合caBIG的数据格式。进一步增强 为了实现标准化，我们建议对试点项目（级别）中定义的标准数据结构增加两项内容 第1-4段）。0级将定义分析中使用的所有参考数据库的特定版本，5级将 捕获包含先验知识的疾病水平发现。 目标2.我们将设计分析模块来整合TCGA所有组件的数据， 进行综合分析。结果将在符合caBIG的输出文件中提交给DCC 附有人类可读的报告，其中包含文本摘要、表格和图表， 不同学科的科学家可以理解，类似于出版物的结果部分。在 此外，我们致力于不断改进管道的技术和分析，特别是 支持向下一代测序平台的过渡。 目标3.我们将在工业级生产中实现这种高通量分析管道 严格的质量控制模式，利用远大的基础设施支持和丰富的经验 运行和维护高吞吐量计算管道。

The overarching goal of TCGA is to change the practice of cancer nnedicine and improve patient survival through cancer genomics. A key deliverable is to enable access and use of complex multi- dimensional genomic data for downstream studies. We propose to operate a GDAC-A center with the leadership, expertise and infrastructure required to develop an analysis pipeline that will generate pre- defined integrative analyses and interpretations that are tailor-designed for hypothesis-testing by basic, translational and clinical investigators. Our team consists of experts in cancer biology, genomics and bioinformatics with a track record of leadership in TCGA. The analytical tools and pipeline structure are based on our extensive TCGA experiences and designed to optimally achieve its goals. This pipeline will be built using the GenePattern bioinformatic workflow environment - a flexible and modular architecture that is caBIG and caGRID compliant, maintained in the well-established, robust and secure IT infrastructure at the Broad Institute and can be operated 24/7 as a Production Pipeline. Leveraging this well-established resource, we will pursue the following specific aims. Aim 1. We will define caBIG compliant data format for all input and output files. To further enhance standardization, we propose two additions to the standard data structure defined in the Pilot Project (Levels 1-4). Level 0 will define specific versions of all reference databases used in the analyses and Level 5 will capture disease-level findings that incorporate prior knowledge. Aim 2. We will design analysis modules to consolidate data from all components of TCGA and to perform integrative analyses. Results will be submitted to DCC in caBIG compliant output files accompanied by human-readable reports containing text summaries, tables and figures in a format understandable to scientists of diverse disciplines, similar to the Results Section of a publication. In addition, we are committed to continuous technical and analytical improvement of the pipeline, particularly in supporting the transition to next-generation sequencing platforms. Aim 3. We will implement this high-throughput analysis pipeline in an industrial-level production mode with rigorous quality control, leveraging the Broad's infrastructural support and extensive experiences in running and maintaining high-throughput computational pipelines.