Global Infrastructure for Collaborative High-throughput Cancer Genomics Analysis

协作高通量癌症基因组分析的全球基础设施

• 批准号: 10011769
• 负责人: GAD A GETZ
• 金额: $ 94.19万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-20 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10011769
• 关键词: Address; Algorithms; Attention; Bioinformatics; Biological; Biomedical Research; Cancer Center; Cancer Gene Mutation; Categories; Classification; Clinic; Clinical; Clinical Data; Clinical Trials; Code; Collaborations; Communities; Complex; Consensus; Correlative Study; Credentialing; Custom; Data; Data Aggregation; Data Analyses; Data Analytics; Data Set; Databases; Deposition; Development; Documentation; Ensure; Evolution; Freezing; Future; General Population; Genome Data Analysis Center; Genome Data Analysis Network; Genomic Data Commons; Goals; Infrastructure; Institutes; Knowledge; Link; Malignant Neoplasms; Manuscripts; Mission; Modeling; Molecular; Morphologic artifacts; National Cancer Institute; Paper; Pathway Analysis; Patient-Focused Outcomes; Patients; Phase; Process; Production; Publications; Publishing; Reporting; Reproducibility; Research Personnel; Running; Sampling; Science; Scientist; Service delivery model; Structure; Suggestion; Summary Reports; System; The Cancer Genome Atlas; Time; TimeLine; Tumor Subtype; Update; Vertebral column; Work; Writing; base; cancer genome; cancer genomics; clinical diagnostics; clinically relevant; cost; data harmonization; disorder subtype; experience; experimental study; genome analysis; high standard; improved; innovation; insight; member; molecular subtypes; operation; prevent; repository; tool; treatment response; user-friendly; whole genome; working group

Abstract The Cancer Genome Atlas (TCGA) set the standards for large-scale cancer genome projects worldwide. In the next phase, the National Cancer Institute and its Center for Cancer Genomics are planning large-scale projects closely tied to clinical questions and trials. In order to perform the analysis of these data, the NCI is creating a Genome Data Analysis Network (GDAN) of different types of Genome Data Analysis Centers (GDACs). Central to this Network is a single Processing GDAC, which will take all the harmonized data, as stored in the NCI's Genomics Data Commons, and perform higher level integrated analyses on these data to support both the Analysis Working Groups (AWGs) within the Network (which will be formed for each project to perform special analyses of the data and write manuscripts) as well as the entire biomedical research community. Herein we propose to build the centralized Processing GDAC on top of our FireCloud platform, an infrastructure to run large scale computation on the cloud in a fully rigorous and reproducible fashion. FireCloud development was based on our experience with Firehose, the Broad internal platform on which the standard TCGA data and analyses currently run. We propose to create and operate the GDAN Standard Workflow, incorporating tools actively developed and used within the GDAN and across the entire field, with particular emphasis on clinical tools. This Workflow will serve as the starting point for AWGs and set the highest standards of transparency, reproducibility and rigor for cancer genome analysis. The results of the Standard Workflow will be stored in a public database, and accessible via standard APIs, and used together with a continuously updated database of prior knowledge to create scientific reports that will be made available to the community, in a pre-publication manner. Finally, a major innovation is that AWG members will be able to login into FireCloud and rerun the entire workflow, or parts of it, with their own parameters and subsets of the data – thus making the entire GDAN analysis fully reproducible and scalable. Our goals are therefore: (1) To create a global infrastructure for collaborative extreme- scale cancer analysis; (2) Operate the Standard Workflows at scale; (3) Rapidly and continuously evolve the Standard Workflows; and (4) created improved capabilities for reporting, exploring the results, clinical diagnostics and reproducibility.

摘要 癌症基因组图谱（TCGA）为大规模癌症基因组制定了标准 全球的项目。在下一阶段，美国国家癌症研究所及其癌症研究中心 癌症基因组学正在计划与临床问题密切相关的大规模项目， 审判为了对这些数据进行分析，NCI正在创建一个基因组数据库， 不同类型的基因组数据分析中心（GDAC）的分析网络（GDAN）。 这个网络的中心是一个单一的处理GDAC，它将把所有的协调 数据，存储在NCI的基因组学数据共享区，并执行更高级别的集成 分析这些数据，以支持分析工作组（特设工作组）和 网络（将为每个项目建立网络，对数据进行专门分析， 撰写手稿）以及整个生物医学研究界。 在此，我们建议在FireCloud之上构建集中式处理GDAC 平台，一个在云上以完全严格的方式运行大规模计算的基础设施。 和可复制的时尚。FireCloud的开发是基于我们的经验， Firehose，Broad内部平台，标准TCGA数据和分析 目前运行。我们建议创建和操作GDAN标准工作流程， 整合GDAN内和整个GDAN内积极开发和使用的工具 特别强调临床工具。此工作流程将作为 为特设工作组设立了透明度、再现性和严谨性的最高标准， 癌症基因组分析标准工作流的结果将存储在公共 数据库，可通过标准API访问，并与连续 更新现有知识数据库，以创建将提供的科学报告 以出版前的方式向社会公布。最后，一个主要的创新是AWG 成员将能够登录到FireCloud并浏览整个工作流程，或其中的一部分， 使用自己的参数和数据子集-从而使整个GDAN分析 完全可复制和可扩展。 因此，我们的目标是：（1）创建一个全球基础设施，以实现协作的极端- 大规模癌症分析;（2）大规模操作标准工作流程;（3）快速， 不断发展标准工作流程;（4）创建改进的能力， 报告、探索结果、临床诊断和再现性。

项目成果

Diverse mutational landscapes in human lymphocytes.

• DOI: 10.1038/s41586-022-05072-7
• 发表时间: 2022-08
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Machado, Heather E.;Mitchell, Emily;Obro, Nina F.;Kubler, Kirsten;Davies, Megan;Leongamornlert, Daniel;Cull, Alyssa;Maura, Francesco;Sanders, Mathijs A.;Cagan, Alex T. J.;McDonald, Craig;Belmonte, Miriam;Shepherd, Mairi S.;Braga, Felipe A. Vieira;Osborne, Robert J.;Mahbubani, Krishnaa;Martincorena, Inigo;Laurenti, Elisa;Green, Anthony R.;Getz, Gad;Polak, Paz;Saeb-Parsy, Kourosh;Hodson, Daniel J.;Kent, David G.;Campbell, Peter J.
• 通讯作者: Campbell, Peter J.

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• DOI: 10.1126/science.aas9824
• 发表时间: 2018
• 期刊: Science (New York, N.Y.)
• 作者: Stewart,Chip;Leshchiner,Ignaty;Hess,Julian;Getz,Gad
• 通讯作者: Getz,Gad