Center for comprehensive proteogenomic data analysis
综合蛋白质组数据分析中心
基本信息
- 批准号:10440579
- 负责人:
- 金额:$ 79.11万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-06-15 至 2027-05-31
- 项目状态:未结题
- 来源:
- 关键词:AffectAffinityAlgorithmic AnalysisAlgorithmsAntineoplastic AgentsBig DataBiochemicalBioinformaticsBiologicalBiological ModelsCancer BiologyClinicalCollaborationsCollectionComplexComputational BiologyCopy Number PolymorphismDataData AnalysesData CommonsData SetDatabasesDecision AidDecision MakingDiseaseDoseDrug TargetingDrug resistanceEncapsulatedFoundationsGenesGeneticGenomeGenomicsHLA AntigensHumanImmuneIndividualLeadershipLigaseLightMalignant NeoplasmsMapsMass Spectrum AnalysisMeasurementMethodologyMethodsModelingMultiomic DataMutateMutationNormal tissue morphologyPathway AnalysisPathway interactionsPeptidesPharmacotherapyPhasePhosphotransferasesPost Translational Modification AnalysisPost-Translational Modification SitePost-Translational Protein ProcessingProcessPrognostic MarkerProteinsProteomeProteomicsPublicationsRNAReportingReproducibilityRetrievalSamplingScientistSignal TransductionSiteSourceSpecific qualifier valueStandardizationSubgroupSystemSystems BiologyTechniquesTherapeuticTherapeutic InterventionTranscriptTranslational ResearchUrsidae FamilyValidationantigen bindingbasecancer biomarkerscancer subtypescancer typecloud basedcohortcomputerized data processingdata repositorydifferential expressiondisease prognosticflexibilitygenomic dataimprovedindividual patientinsightinterestlaboratory experimentmetabolomicsmultiple omicsneoantigensnew therapeutic targetnext generation sequencingnovelpersonalized medicinephenotypic dataproteogenomicsresponsetherapeutic targettooltranscriptomicstranslational pipelinetumor
项目摘要
Project Summary
Proteogenomics involves the integrative multi-omic analysis of genomic, transcriptomic, proteomic and post-
translational modification data produced by next-generation sequencing and mass spectrometry-based
proteomics. Several publications by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and others
have highlighted the impact of proteogenomics in enabling deeper insight into the biology of cancer and
identification of potential drug targets. Integrative analysis of multi-omic data requires the deployment of
complex algorithms and data processing techniques, which are generally inaccessible to scientists without a
background in bioinformatics and computational biology.
Our proposed center for comprehensive proteogenomic data analysis will encapsulate a comprehensive
set of analysis methods in a platform that will be (i) simple to use (ii) flexible (iii) automated, facilitating the
routine application to all CPTAC proteogenomic datasets as they become available, and (iv) able to
incorporate new methods with minimal effort. We will leverage PANOPLY--a cloud-based platform for
automated and reproducible proteogenomic data analysis--as the foundation, with specific, carefully chosen
algorithms targeted for addition to provide an expansive set of analysis capabilities that can be easily
harnessed to provide a rapid and extensive baseline analysis for proteogenomic studies, leading to many
disease specific hypotheses that can be explored further using additional computational and wet-lab
experiments.
Our collection of tools, algorithms and interactive reports will enable unprecedented, automated and integrative
systems-biology level analyses of proteome, post-translational modification and metabolomic data combined
with genomic data for individual disease cohorts and pan-cancer analysis across cohorts, leading to deeper
understanding of cancer biology and enabling identification of therapeutic targets and disease/prognostic
biomarkers.
项目摘要
蛋白质组学是一门涉及基因组学、转录组学、蛋白质组学和后基因组学的多组学综合分析技术。
基于下一代测序和质谱的翻译修饰数据
蛋白质组学临床蛋白质组学肿瘤分析联盟(CPTAC)和其他组织的几篇出版物
强调了蛋白基因组学在更深入地了解癌症生物学方面的影响,
确定潜在的药物靶点。多组学数据的综合分析需要部署
复杂的算法和数据处理技术,科学家如果没有一个
生物信息学和计算生物学背景。
我们建议的综合蛋白基因组数据分析中心将包含一个全面的
在一个平台中使用一套分析方法,该平台将(i)易于使用(ii)灵活(iii)自动化,
常规应用于所有可用的CPTAC蛋白基因组数据集,以及(iv)能够
以最小的努力融入新方法。我们将利用PANOTOWN--一个基于云的平台,
自动化和可重复的蛋白质组学数据分析-作为基础,具有特定的,精心挑选的
算法的目标是增加提供一个扩展的分析能力,可以很容易地
利用它为蛋白质基因组学研究提供快速和广泛的基线分析,
疾病特异性假设,可以进一步探索使用额外的计算和湿实验室
实验
我们收集的工具、算法和交互式报告将实现前所未有的自动化和集成化
结合蛋白质组、翻译后修饰和代谢组学数据的系统生物学水平分析
利用单个疾病队列的基因组数据和跨队列的泛癌症分析,
了解癌症生物学,并能够识别治疗靶点和疾病/预后
生物标志物。
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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{{ truncateString('GAD A GETZ', 18)}}的其他基金
Center for comprehensive proteogenomic data analysis
综合蛋白质组数据分析中心
- 批准号:
10644013 - 财政年份:2022
- 资助金额:
$ 79.11万 - 项目类别:
Comprehensive analysis of point mutations in cancer
癌症点突变综合分析
- 批准号:
10301857 - 财政年份:2021
- 资助金额:
$ 79.11万 - 项目类别:
Comprehensive analysis of point mutations in cancer
癌症点突变综合分析
- 批准号:
10491092 - 财政年份:2021
- 资助金额:
$ 79.11万 - 项目类别:
Comprehensive analysis of point mutations in cancer
癌症点突变综合分析
- 批准号:
10676830 - 财政年份:2021
- 资助金额:
$ 79.11万 - 项目类别:
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:
9571405 - 财政年份:2016
- 资助金额:
$ 79.11万 - 项目类别:
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:
9355157 - 财政年份:2016
- 资助金额:
$ 79.11万 - 项目类别:
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 - 财政年份:2016
- 资助金额:
$ 79.11万 - 项目类别:
Discovery of clinically distinct CLL subgroups by integrative mapping of large-scale CLL genetic, expression and clinical data
通过大规模 CLL 遗传、表达和临床数据的综合绘图发现临床上不同的 CLL 亚组
- 批准号:
10005157 - 财政年份:2016
- 资助金额:
$ 79.11万 - 项目类别:
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:
9211085 - 财政年份:2016
- 资助金额:
$ 79.11万 - 项目类别:
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