Center for comprehensive proteogenomic data analysis

综合蛋白质组数据分析中心

• 批准号: 10644013
• 负责人: GAD A GETZ
• 金额: $ 77.53万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10644013
• 关键词: Affect; Affinity; Algorithmic Analysis; Algorithms; Antigen Targeting; Big Data; Biochemical; Bioinformatics; Biological; Biological Models; Cancer Biology; Clinical; Collaborations; Collection; Complex; Computational Biology; Copy Number Polymorphism; Data; Data Analyses; Data Commons; Data Set; Databases; Decision Aid; Decision Making; Differentiation Antigens; Disease; Dose; Drug Targeting; Drug resistance; Encapsulated; Foundations; Genes; Genetic; Genome; Genomics; HLA Antigens; Human; Immune; Individual; Leadership; Ligase; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Measurement; Methodology; Methods; Modeling; Multiomic Data; Mutate; Mutation; Pathway Analysis; Pathway interactions; Peptides; Pharmaceutical Preparations; Pharmacotherapy; Phase; Phosphotransferases; Post Translational Modification Analysis; Post-Translational Modification Site; Post-Translational Protein Processing; Process; Prognostic Marker; Proteins; Proteome; Proteomics; Publications; RNA; Reporting; Reproducibility; Retrieval; Sampling; Scientist; Signal Transduction; Site; Source; Specific qualifier value; Standardization; Subgroup; System; Systems Biology; Techniques; Therapeutic; Therapeutic Intervention; Tissues; Transcript; Translational Research; Validation; antigen binding; cancer biomarkers; cancer subtypes; cancer type; cloud based; cohort; computerized data processing; data repository; differential expression; flexibility; genomic data; improved; individual patient; insight; interest; laboratory experiment; metabolomics; multiple omics; neoantigens; new therapeutic target; next generation sequencing; novel; personalized medicine; phenotypic data; proteogenomics; response; therapeutic target; tool; transcriptomics; translational pipeline; tumor; ubiquitin isopeptidase

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 蛋白质组学涉及基因组，转录组，蛋白质组学和后的多词综合分析 下一代测序和基于质谱的转化修改数据 蛋白质组学。临床蛋白质组学分析联盟（CPTAC）的几本出版物以及其他出版物 强调了蛋白质组学对癌症生物学和 识别潜在药物靶标。多运动数据的综合分析需要部署 复杂的算法和数据处理技术，科学家通常无法访问这些技术 生物信息学和计算生物学的背景。 我们提出的全面蛋白质组数据分析中心将封装全面 平台中的一组分析方法（i）易于使用（ii）灵活（iii）自动化，促进 将所有CPTAC蛋白质组学数据集的常规应用程序可用，并且（iv）能够 将新方法纳入最小的努力。我们将利用Panoply - 一个基于云的平台 自动化和可重现的蛋白质组数据分析 - 作为基础，特定于精心选择 用于增加的算法，以提供一组宽敞的分析功能，可以轻松 利用为蛋白质组研究提供快速而广泛的基线分析，导致许多 疾病特定的假设，可以使用其他计算和湿LAB进一步探索 实验。 我们收集的工具，算法和交互式报告将使前所未有，自动化和综合性能够 蛋白质组，翻译后修饰和代谢组数据的系统生物学水平分析合并 借助各个人群的基因组数据和泛型群体分析，导致更深层次 了解癌症生物学并鉴定治疗靶标和疾病/预后 生物标志物。