Global Infrastructure for Collaborative High-throughput Cancer Genomics Analysis
Global Infrastructure for Collaborative High-throughput Cancer Genomics Analysis
批准号:
9211085
负责人:
GAD A GETZ
金额:
$96.27万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-20 至 2021-08-31
关键词:
AddressAlgorithmsAttentionBioinformaticsBiologicalBiomedical ResearchCategoriesClassificationClinicClinicalClinical DataClinical TrialsCodeCollaborationsCommunitiesComplexConsensusCorrelative StudyCustomDataData AggregationData AnalysesData SetDatabasesDepositionDevelopmentDiagnosticDocumentationEnsureEvolutionFreezingFutureGeneral PopulationGenome Data Analysis CenterGenome Data Analysis NetworkGenomic Data CommonsGoalsInstitutesKnowledgeLearningLeftLinkMalignant NeoplasmsManuscriptsMissionModelingMolecularMorphologic artifactsNational Cancer InstituteOncogenesPaperPathway AnalysisPatient-Focused OutcomesPatientsPhaseProcessProductionPublicationsPublishingReportingReproducibilityResearch InfrastructureResearch PersonnelRunningSamplingScienceScientistServicesStructureSuggestionSummary ReportsSystemThe Cancer Genome AtlasTimeTimeLineTumor SubtypeUpdateVertebral columnWorkWritingabstractingbasecancer genomecancer genomicsclinically relevantcostdisorder subtypeexperiencegenome analysishigh standardimprovedinnovationinsightmembermolecular subtypesoperationpreventrepositoryresearch studytooltreatment responseuser-friendlywhole genomeworking group
中文摘要
摘要
癌症基因组图谱(TCGA)为大规模癌症基因组制定了标准
世界各地的项目。在下一阶段,国家癌症研究所及其研究中心
癌症基因组学正在计划与临床问题密切相关的大型项目和
审判。为了对这些数据进行分析,NCI正在创建基因组数据
不同类型的基因组数据分析中心的分析网络(GDAN)。
这个网络的核心是一个单一的处理GDAC,它将承担所有协调的
数据,存储在NCI的基因组数据共享中,并执行更高级别的集成
对这些数据进行分析,以支持以下两个分析工作组
网络(将为每个项目组成,以执行数据的特殊分析和
撰写手稿)以及整个生物医学研究社区。
在此,我们建议在我们的FireCloud之上构建集中处理GDAC
平台,一个在云上运行大规模计算的基础设施,完全严格
和可复制的时尚。FireCloud开发基于我们在
FireHose,标准TCGA数据和分析的广阔内部平台
目前正在运行。我们建议创建和运行GDAN标准工作流程,
纳入在GDAN内部和整个
领域,特别强调临床工具。此工作流将作为开始
为特设工作组设定最高的透明度、再现性和严格性标准
癌症基因组分析。标准工作流的结果将存储在公共
数据库,并可通过标准API访问,并与连续
更新先验知识数据库,以创建将提供的科学报告
以出版前的方式向社区发布。最后,一项重大创新是AWG
成员将能够登录到FireCloud并重新运行整个或部分工作流,
使用它们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)为协作Extreme创建全球基础设施-
规模癌症分析;(2)在规模上操作标准工作流程;(3)快速和
不断发展标准工作流;以及(4)创建了改进的功能
报告、探索结果、临床诊断和重复性。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
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批准号:10440579
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资助金额:$79.11万
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依托单位:
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批准号:10491092
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财政年份:--
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Bioinformatic and Biostatistics Core
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财政年份:--
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依托单位:
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项目类别:
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财政年份:--
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负责人:GAD A GETZ
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依托单位:
海外基金