University of Michigan Proteogenomics Data Analysis Center

摘要 这是建立密歇根大学蛋白基因组数据分析中心（UM- PGDAC）。美国国家癌症研究所（NCI）在新技术平台上进行了大量投资 通过临床蛋白质组肿瘤分析联盟（CPTAC）的倡议， 蛋白质组学提供了从基因组分析中不明显的补充信息， 转录组数据。首先，关键是要确定成千上万的小说或以前很差的 使用基因组学和转录组学方法发现的表征的转录物或序列变体 在蛋白质水平表达，优先考虑这些变体用于随后的验证研究。第二、 跨多种数据类型的定量信息集成已经成为一种强大的策略， 重建癌症中的靶向途径和提名潜在的药物靶点。与此同时， 对基因组和蛋白质组数据进行复杂的综合分析需要先进的生物信息学 严格的质量控制措施。UM-PGDAC具有独特的定位，可实现先进的 生物信息学基础设施来应对这些挑战，并将其应用于CPTAC数据。它汇集了 一个多学科的科学家团队，他们是计算蛋白质组学领域的领先专家， 转录组学、基因组学、癌症系统生物学和精确肿瘤学。该小组是锚定在 密歇根转化病理学中心（MCTP），具有悠久的成功合作历史 调查员之间的关系UM-PGDAC建立在十多年高度相关的工作基础上 这导致了蛋白质基因组学和多基因组学所需的全面基础设施的发展， 组学数据整合研究。UM-PGDAC研究人员将努力进一步提高速度， 蛋白质组学分析的准确性。使用的整合基因组/转录组/蛋白质组管道 UM-PDGAC将通过自动数据可视化能力和报告生成工具得到增强 以透明和易于解释的方式向癌症生物学家展示这些发现。UM-PGDAC 将与CPTAC的其他成员合作，以确保最小的重复工作，高效 通过使用共同文件交换数据和生物信息学方法和工具以及互操作性 格式和数据标准。凭借其在生物标志物发现领域的丰富经验， 精确肿瘤学，通过UM-PGDAC研究者参与EDRN进一步加强， SPORE和其他NIH资助的计划，UM-PGDAC将参与第二轮优先级工作 选择候选的癌症特异性蛋白和肽用于随后的靶向验证， 蛋白质组学测定。最后，UM-PGDAC将利用一个独特的机会-以NCI的形式 资助T32蛋白质组信息学培训计划在密歇根大学-创造一个独特的 为培养精通蛋白质组学技术的新一代癌症研究人员创造了良好的环境。

ABSTRACT This is an application to establish the University of Michigan Proteogenomic Data Analysis Center (UM- PGDAC). The National Cancer Institute (NCI) has made significant investments in new technology platforms for cancer proteomics through the Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative. Proteomics provides complementary information not apparent from the analysis of genomic and transcriptomic data alone. First, it is critical to identify which of the thousands of novel or previously poorly characterized transcripts or sequence variants discovered using genomic and transcriptomic approaches are expressed at the protein level, prioritizing such variants for subsequent validation studies. Second, integration of quantitative information across multiple data types has emerged as a powerful strategy for reconstructing targetable pathways in cancer and for nomination of potential drug targets. At the same time, sophisticated, integrative analyses across genome and proteome data require advanced bioinformatics tools and stringent quality control measures. UM-PGDAC is uniquely positioned to implement advanced bioinformatics infrastructure to address these challenges and apply it across CPTAC data. It brings together a multi-disciplinary team of scientists who are leading experts in the areas of computational proteomics, transcriptomics, genomics, cancer systems biology and precision oncology. The team is anchored at the Michigan Center for Translational Pathology (MCTP), which has a long history of successful collaborations between the individual investigators. UM-PGDAC builds upon more than a decade of highly relevant work that resulted in the development of a comprehensive infrastructure required for proteogenomics and multi- omics data integration research. UM-PGDAC investigators will work to further improve the speed and accuracy of proteogenomics analyses. The integrated genome/transcriptome/proteome pipelines used by the UM-PDGAC will be enhanced with automated data visualization capabilities and report generation tools for presenting the findings to cancer biologists in a transparent and easy to interpret manner. UM-PGDAC will work collaboratively with other members of the CPTAC to ensure minimal duplication of efforts, efficient exchange of data and bioinformatics methods and tools and interoperability via the use of common file formats and data standards. Building upon its extensive experience in the area of biomarker discovery and precision oncology, further enhanced through participation of UM-PGDAC investigators in the EDRN, SPORE, and other NIH funded initiatives, UM-PGDAC will engage in a second round of prioritization work to select candidate cancer-specific proteins and peptides for subsequent targeted validation using multiplex proteomic assays. Finally, UM-PGDAC will take advantage of a unique opportunity – in the form of the NCI funded T32 Proteome Informatics Training Program at the University of Michigan – to create a unique environment for training the new generation of cancer researchers versed in proteomics technology.