Development of Trans Proteomic Pipeline, an Analysis Suite for Mass Spectrometry

项目摘要 基于质谱的蛋白质组学是鉴定、定量和分析蛋白质组学的关键技术。 蛋白质及其翻译后修饰在生物学各个方面的比较。MS数据集 随着仪器的进步，实验室的档案也越来越大， 可供重新分析和比较的数据。为了满足蛋白质组学社区的需求， 为了应对大数据，我们一直在开发端到端的数据处理和分析工具套件， 跨蛋白质组学管道（TPP）该项目将推动广泛使用的TPP软件套件， 对用户社区更加有用，使他们能够更快地执行分析， 人工努力，以及增加目前不可能或仅处于测试阶段的功能。我们将添加 对数据独立采集（DIA）工作流程（如SWATH-MS）提供全面的端到端TPP支持，以及 蛋白质组学工作流程，如RNA-seq辅助蛋白质组学。TPP已经部分支持 这些工作流，但需要额外的整理，强化和扩展到高容量云计算 让我们的平台对所有用户都真正有用。随着蛋白质丰度定量变得更加 为了进行更多的实验，我们还将加强现有的同位素和同量异位素标记数据的工具 作为无标签数据，并建立一个新的分析工作台，使我们的用户能够访问高级统计 分析和比较例程已经存在，但许多用户难以处理。除了 捆绑这个统计软件，我们将建立一个框架，允许用户采取他们的定量结果， 从任何传统的工作流程或新的工作流程，将它们转换为统计 软件包需要，然后可视化和交互式地探索统计分析的输出，因此趋势可以 在原始数据中发现异常值并验证异常值。 将对TPP套件进行大量较小的增强，以使工具更智能， 通过各种工具减轻了用户设置参数和牧羊数据的负担。我们将 为现有工具开发新的操作模式，以应对用户提出的挑战 根据我们从他们那里得到的反馈。我们将继续开展多项外联工作，其中包括 每年多次教授软件课程，在科学会议上举办研讨会和展位， 与我们的用户会面并获得反馈，并开发更多公开可用的教程和食谱 用于在各种情况下使用工具和应用程序。我们当然会继续宣传 通过文献中的文章和在科学会议上的演讲，介绍TPP的进展。在 总而言之，这项拟议中的计划将继续推动TPP成为卓越的自由和开源项目。 用于蛋白质组学中常规和大数据应用的端到端软件分析工具套件。

Project Summary Mass spectrometry (MS) based proteomics is a key technology for the identification, quantification and comparison of proteins and their post-translational modifications across all aspects of biology. MS datasets have been growing ever larger with the advancement of instrumentation, as has the archive of experimental data available for re-analysis and comparison. In order to meet the needs of the proteomics community for coping with big data, we have been developing our end-to-end suite of data processing and analysis tools, called the Trans-Proteomic Pipeline (TPP). This project will advance the widely used TPP software suite to become even more useful to its user community, enabling them to perform their analyses even faster with less human effort, and adding capabilities that are currently not possible or are only in testing stages. We will add full end-to-end TPP support for the data independent acquisition (DIA) workflows, such as SWATH-MS, and proteogenomics workflows, such as RNA-seq assisted proteomics. The TPP already has partial support for these workflows, but needs additional finishing, hardening, and extension to high capacity cloud computing platforms to become truly useful to all our users. As protein abundance quantification becomes even more essential to more experiments, we will enhance our existing tools for isotopic and isobaric labeled data as well as label-free data, and build a new analysis workbench that will give our users access to advanced statistical analysis and comparison routines that already exist but are difficult for many users to handle. In addition to bundling this statistical software, we will build a framework that allows users to take their quantitative results from any of the traditional workflows or new workflows, transform them into the formats that the statistical packages require, and then visualize and interactively explore the outputs of statistical analysis, so trends can be uncovered and outliers verified in the original data. A substantial number of smaller enhancements to the TPP suite will be made to make the tools smarter so that users are relieved of the burden setting parameters and shepherding data through various tools. We will develop new modes of operation for existing tools to be able to handle challenges presented by our users based on the feedback we receive from them. We will continue our many outreach efforts, which include teaching software courses several times per year, hosting workshops and booths at scientific conferences to meet with and gain feedback from our users, and develop many more publicly available tutorials and recipes for using the tools and applications to various circumstances. We will of course continue to disseminate the advancements of the TPP with articles in the literature and with presentations at scientific conferences. In summary, this proposed program will continue to advance the TPP as the preeminent free and open-source end-to-end software analysis tool suite for routine and big data applications in proteomics.