The Cancer Genome Atlas Data Analysis Center

癌症基因组图谱数据分析中心

• 批准号: 7788542
• 负责人: LYNDA CHIN
• 金额: $ 266.49万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-29 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7788542
• 关键词: Architecture; Atlases; Bioinformatics; Cancer Biology; Clinical Investigator; Commit; Complex; Data; Data Analyses; Databases; Discipline; Disease; Environment; Genomics; Goals; Human; Institutes; Knowledge; Leadership; Malignant Neoplasms; Medicine; Output; Patients; Pilot Projects; Production; Publications; Quality Control; Reporting; Research Infrastructure; Resources; Running; Scientist; Secure; Standardization; Structure; Testing; Text; analytical tool; base; caGrid; cancer Biomedical Informatics Grid; cancer genome; cancer genomics; data format; data structure; design; experience; flexibility; high throughput analysis; improved; next generation

DESCRIPTION (provided by applicant): The overarching goal of TCGA is to change the practice of cancer medicine 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.

描述（由申请人提供）：TCGA的总体目标是改变癌症医学的实践，并通过癌症基因组学改善患者的生存。一个关键的可交付方式是启用和使用复杂的多维基因组数据进行下游研究。我们建议通过开发分析管道所需的领导，专业知识和基础设施来运营GDAC-A中心，该管道将生成预定义的综合分析和解释，这些分析和解释是根据基本，翻译和临床研究人员量身定制的，用于为假设进行了量身定制。我们的团队由癌症生物学，基因组学和生物信息学专家组成，并具有TCGA领导能力的记录。分析工具和管道结构基于我们广泛的TCGA体验，旨在最佳实现其目标。该管道将​​使用Genepattern生物信息学工作流程环境建造 - 一种灵活的模块化体系结构，符合Cabig和Cagrid，并保存在广阔研究所的建立良好，坚固且安全的IT基础设施中，并且可以用作24/7的生产管道。 利用这个良好的资源，我们将追求以下特定目标。 AIM 1。我们将为所有输入和输出文件定义符合Cabig的数据格式。为了进一步增强标准化，我们建议对试点项目定义的标准数据结构（级别1-4）提出两种添加。级别0将定义分析中使用的所有参考数据库的特定版本，第5级将捕获结合先验知识的疾病级别的发现。 AIM 2。我们将设计分析模块，以合并来自TCGA的所有组件的数据并执行综合分析。结果将在符合Cabig的输出文件中提交DCC，并附有包含文本摘要，表和数字的人类可读报告，其格式可以理解为不同学科的科学家，与出版物的结果部分相似。此外，我们致力于对管道的持续技术和分析改进，特别是在支持向下一代测序平台的过渡时。 AIM 3。我们将在工业级别的生产模式下实施这种高通量分析管道，并具有严格的质量控制，利用Broad的基础设施支持以及在运行和维持高通量计算管道方面的丰富经验。