Integrative Pipeline for Analysis & Translational Application of TCGA Data (GDAC)
综合分析管道
基本信息
- 批准号:8546703
- 负责人:
- 金额:$ 188.01万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-09-29 至 2016-07-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmsArchitectureBioinformaticsBiologicalBiological MarkersBiologyBiometryCancer CenterCancer PatientClinicalClinical ResearchConfidentialityCountryDataData AnalysesDevelopmentEnsureFundingGenomeGoalsHumanImageryInstitutionInstructionLeadMalignant NeoplasmsMolecular ProfilingParticipantPathologyPathway AnalysisResearch PersonnelSoftware EngineeringSourceSpecimenSystemSystems BiologyTechnologyThe Cancer Genome AtlasTimeUniversity of Texas M D Anderson Cancer CenterWorkbasebiological systemsbiosignaturecancer Biomedical Informatics Gridcancer diagnosiscancer therapycancer typecomputer based Semantic Analysisdata integrationdesignfollow-upinnovationmembernovelprogramssoftware developmenttumoruser-friendly
项目摘要
The proposed Genome Data Analysis Center B (GDAC B) will work cooperatively with other GDACs funded
by The Cancer Genome Atlas (TCGA) project to (i) develop an innovative, integrative pipeline for systems-
level analysis of TCGA's molecular profiling data on many different types of human tumors and (ii) apply that
pipeline and its component modules to TCGA data to address important biological and clinical questions. An
overarching goal is to 'personalize' the management of patients' cancers on the basis of new tumor
biomarkers and biosignatures. For the first time, it is easier to generate millions of data points on tumors than
to analyze or interpret those data, hence the bioinformatic challenge is formidable. The pipeline will be
constructed using the Agile software development paradigm and semantic web query architecture. It will be
based on novel algorithms and modules developed by participants in the GDAC. Included will be modules for
data integration, data visualization, pathway analysis, and systems biological interpretation, all designed to
be user-friendly for the bench researcher and clinician. Those modules will be interfaced with additional ones
developed by other GDACs, All development will adhere to standards of TCGA and the Cancer Biomedical
Informatics Grid (caBIG) and will provide controlled access to ensure confidentiality of personally identifiable
data. The proposed GDAC team brings to this project expertise in bioinformatics, biostatistics, software
engineering, high-throughput molecular profiling technologies, systems-oriented biology, biomarker studies,
pathology, and clinical research. The three co-PIs (for bioinformatics, systems biology, and clinical research)
have each participated actively in TCGA since its inception, as have other members of the team, including
the lead software engineer. A major strength is the University of Texas M. D. Anderson Cancer Center
(MDACC) as an institution. MDACC has been, and presumably will continue to be, the largest source of
tumor specimens for TCGA. As one of the country's foremost cancer centers, with by far the largest cancer
clinical research program, MDACC has unparalleled expertise for follow up on medically important leads that
result from the development and application of the pipeline to TCGA data.
拟议的基因组数据分析中心B(GDAC B)将与其他GDAC合作,
通过癌症基因组图谱(TCGA)项目,(i)为系统开发创新的综合管道-
水平分析TCGA对许多不同类型的人类肿瘤的分子谱数据,以及(ii)应用
管道及其组件模块到TCGA数据,以解决重要的生物学和临床问题。一个
总体目标是根据新的肿瘤,对患者的癌症进行“个性化”管理。
生物标记和生物特征。这是第一次,生成数百万个肿瘤数据点比生成数百万个肿瘤数据点更容易
分析或解释这些数据,因此生物信息学的挑战是艰巨的。该管道将
使用敏捷软件开发范式和语义Web查询架构构建。将
基于GDAC参与者开发的新算法和模块。将包括以下模块:
数据集成、数据可视化、途径分析和系统生物学解释,所有这些都旨在
对实验室研究人员和临床医生来说是用户友好的。这些模块将与其他模块连接
由其他GDAC开发,所有开发将遵循TCGA和癌症生物医学标准
信息网格(caBIG),并将提供受控访问,以确保个人身份信息的保密性。
数据拟议的GDAC团队为该项目带来了生物信息学、生物统计学、软件方面的专业知识
工程,高通量分子分析技术,系统导向生物学,生物标志物研究,
病理学和临床研究。三个共同PI(生物信息学、系统生物学和临床研究)
自TCGA成立以来,我和团队的其他成员都积极参与了TCGA,包括
首席软件工程师一个主要的优势是得克萨斯大学M。D.安德森癌症中心
(MDACC)作为一个机构。MDACC一直是,并可能继续是,最大的来源,
用于TCGA的肿瘤标本。作为全国最重要的癌症中心之一,
临床研究计划,MDACC具有无与伦比的专业知识,跟进医学上重要的线索,
这是TCGA数据流水线开发和应用的结果。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Rehan Akbani其他文献
Rehan Akbani的其他文献
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{{ truncateString('Rehan Akbani', 18)}}的其他基金
The Cancer Proteome Atlas: an Integrated Bioinformatics Resource for Functional Cancer Proteomic Data
癌症蛋白质组图谱:功能性癌症蛋白质组数据的综合生物信息学资源
- 批准号:
10653202 - 财政年份:2022
- 资助金额:
$ 188.01万 - 项目类别:
A Genome Data Analysis Center Focused on Batch Effect Analysis and Data Integration
专注于批量效应分析和数据集成的基因组数据分析中心
- 批准号:
10300778 - 财政年份:2021
- 资助金额:
$ 188.01万 - 项目类别:
A Genome Data Analysis Center Focused on Batch Effect Analysis and Data Integration
专注于批量效应分析和数据整合的基因组数据分析中心
- 批准号:
10689115 - 财政年份:2021
- 资助金额:
$ 188.01万 - 项目类别:
Computational Tools for Analysis and Visualization of Quality Control Issues in Metabolomic Data
用于代谢组数据质量控制问题分析和可视化的计算工具
- 批准号:
9615762 - 财政年份:2018
- 资助金额:
$ 188.01万 - 项目类别:
Computational Tools for Analysis and Visualization of Quality Control Issues in Metabolomic Data
用于代谢组数据质量控制问题分析和可视化的计算工具
- 批准号:
10251093 - 财政年份:2018
- 资助金额:
$ 188.01万 - 项目类别:
Computational Tools for Analysis and Visualization of Quality Control Issues in Metabolomic Data
用于代谢组数据质量控制问题分析和可视化的计算工具
- 批准号:
10005202 - 财政年份:2018
- 资助金额:
$ 188.01万 - 项目类别:
Batch effects in molecular profiling data on cancers: detection, quantitation, interpretation, and correction
癌症分子分析数据的批次效应:检测、定量、解释和校正
- 批准号:
9352299 - 财政年份:2016
- 资助金额:
$ 188.01万 - 项目类别:
Integrated analysis of protein expression data from the Reverse Phase Protein Array (RPPA) platform
对反相蛋白阵列 (RPPA) 平台的蛋白表达数据进行集成分析
- 批准号:
10005168 - 财政年份:2016
- 资助金额:
$ 188.01万 - 项目类别:
Batch effects in molecular profiling data on cancers: detection, quantitation, interpretation, and correction
癌症分子分析数据的批次效应:检测、定量、解释和校正
- 批准号:
9789027 - 财政年份:2016
- 资助金额:
$ 188.01万 - 项目类别:
Integrated analysis of protein expression data from the Reverse Phase Protein Array (RPPA) platform
对反相蛋白阵列 (RPPA) 平台的蛋白表达数据进行集成分析
- 批准号:
9789028 - 财政年份:2016
- 资助金额:
$ 188.01万 - 项目类别:
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