Center for comprehensive proteogenomic data analysis

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 强调了蛋白质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CPTAC蛋白基因组数据集，因为它们变得可用，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。

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.