Center for comprehensive proteogenomic data analysis

项目摘要 蛋白质组学涉及基因组、转录组、蛋白质组和后基因组的综合多组学分析。 基于下一代测序和质谱学的翻译修饰数据 蛋白质组学。临床蛋白质组肿瘤分析联盟(CPTAC)和其他机构发表的几篇论文 强调了蛋白质基因组学在更深入地了解癌症生物学和 识别潜在的毒品目标。多组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 生物信息学和计算生物学背景。 我们提议的全面蛋白质基因组数据分析中心将囊括一个全面的 平台中的一套分析方法，将(I)简单易用(Ii)灵活(Iii)自动化，促进 常规应用于所有CPTAC蛋白质基因组数据集，当它们可用时，以及(Iv)能够 以最小的努力融入新的方法。我们将利用Panoply--一个基于云的平台 自动化和可重复的蛋白质基因组数据分析--作为基础，具体地，仔细地选择 旨在添加的算法可提供一组扩展的分析功能， 为蛋白质基因组研究提供快速和广泛的基线分析，导致许多 可以使用额外的计算和湿实验室进一步探索的疾病特定假设 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 蛋白质组、翻译后修饰和代谢组学数据组合的系统生物学水平分析 利用个体疾病队列的基因组数据和跨队列的泛癌症分析，导致更深层次的 了解癌症生物学，并能够确定治疗目标和疾病/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.