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：
GAD A GETZ
金额：
$ 79.11万
依托单位：
BROAD INSTITUTE, INC.
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：
https://reporter.nih.gov/project-details/10440579
关键词：
Affect Affinity Algorithmic Analysis Algorithms Antineoplastic Agents 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 Disease Dose Drug Targeting Drug resistance Encapsulated Foundations Genes Genetic Genome Genomics HLA Antigens Human Immune Individual Leadership Ligase Light Malignant Neoplasms Maps Mass Spectrum Analysis Measurement Methodology Methods Modeling Multiomic Data Mutate Mutation Normal tissue morphology Pathway Analysis Pathway interactions Peptides 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 Transcript Translational Research Ursidae Family Validation antigen binding base cancer biomarkers cancer subtypes cancer type cloud based cohort computerized data processing data repository differential expression disease prognostic 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

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.

项目概要蛋白质组学涉及基因组、转录组、蛋白质组和后处理的综合多组学分析。基于下一代测序和质谱法产生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) 等的多篇出版物强调了蛋白质基因组学在更深入地了解癌症生物学方面的影响，识别潜在的药物靶点。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，如果没有专业知识，科学家通常无法获得这些技术生物信息学和计算生物学背景。我们提议的综合蛋白质组数据分析中心将囊括全面的平台中的一套分析方法将 (i) 使用简单 (ii) 灵活 (iii) 自动化，促进对所有可用的 CPTAC 蛋白质组数据集进行常规应用，并且 (iv) 能够以最小的努力融入新方法。我们将利用 PANOPLY——一个基于云的平台自动化且可重复的蛋白质组数据分析——作为基础，以特定的、精心选择的针对加法的算法提供了一组广泛的分析功能，可以轻松地利用它为蛋白质组学研究提供快速而广泛的基线分析，从而导致许多可以使用额外的计算和湿实验室进一步探索疾病特定假设实验。我们的工具、算法和交互式报告集合将实现前所未有的自动化和集成蛋白质组、翻译后修饰和代谢组数据相结合的系统生物学水平分析具有单个疾病队列的基因组数据和跨队列的泛癌分析，从而导致更深入的研究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。