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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：
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
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/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

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.

项目总结