TRTech-PGR: A PeptideAtlas for Arabidopsis thaliana and other plant species; harnessing world-wide proteomics data and mining for biological features

植物研究的一个主要限制是组织和生物体中蛋白质的准确鉴定和定量。质谱分析是一种技术，可以让科学家从组织或生物体中识别数千种蛋白质。所有蛋白质的集合统称为“蛋白质组”。该项目将利用来自世界各地实验室的公开蛋白质质谱研究，生成一个模式植物物种拟南芥（Arabidopsis thaliana）（通常被称为“thale cress”）中已鉴定蛋白质的图谱。这样的图谱将使研究人员能够解决有关这种植物蛋白质组的许多重要生物学问题。一旦为拟南芥生成了第一个蛋白质图谱，研究人员还将生成两种作物的图谱，如玉米、水稻或大豆。该项目还将教育和培训年轻科学家处理和管理质谱数据的技术，以了解植物的蛋白质组。高质量的基因组注释和大量公开可用的质谱（MS）蛋白质组学数据使拟南芥成为绘制其蛋白质组和改进基因组注释的理想植物。研究小组将把这些信息与其他系统数据相结合，以解决一系列悬而未决的问题和假设。利用人类多肽图谱蛋白质组基础设施，该项目将基于来自世界各地实验室的数百项MS研究，通过ProteomeXchange （http://www.proteomexchange.org/）收集，并通过统一的处理管道重新分析，生成第一个拟南芥多肽图谱。所有匹配的ms衍生肽数据将被投影到与光谱、技术和生物元数据链接的预测初级蛋白序列上。该肽图谱及其元数据将有助于解决几个生物学问题和假设，例如哪些mRNA亚型得到蛋白质组学证据的支持，以及PTMs之间是否存在蛋白质内串扰或竞争的证据。拟南芥肽图谱将链接到TAIR （https://www.arabidopsis.org/）和其他基于网络的资源。完整的数据集将公开供下载，从而进一步促进科学界的数据挖掘，扩大该项目的影响。一旦针对拟南芥的前几个构建发布，并根据社区反馈/互动，PeptideAtlas构建将扩展到其他具有社会和/或经济重要性的植物物种，这些植物物种可以获得广泛和全面的基于ms的蛋白质组学数据（例如玉米，番茄，水稻，大豆）。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A major limitation of plant research is the accurate identification and quantification of proteins from tissues and organisms. Mass spectrometry is a technology that allows scientists to identify thousands of proteins from a tissue or organism. Together, the collective set of all proteins is called the "proteome". This project will generate an atlas of identified proteins in the model plant species Arabidopsis thaliana, commonly named "thale cress", using publicly available protein mass spectrometry studies from laboratories around the world. Such an atlas will then allow the researchers to address a number of important biological questions about this plant proteome. Once the first protein atlas has been generated for Arabidopsis, the researchers will also generate an atlas for two crop species, such as maize, rice, or soybean. This project will also educate and train young scientists in techniques for processing and managing mass spectrometry data for understanding the proteome of plants. The availability of a high quality annotated genome and a wealth of publicly available mass-spectrometry (MS) based proteomics data makes Arabidopsis thaliana an ideal plant for mapping its proteome and improving genome annotation. The research team will integrate this information with other systems data to address a range of outstanding questions and hypotheses. Taking advantage of the Human PeptideAtlas proteome infrastructure, by co-PI Deutsch and colleagues at ISB, this project will generate the first Arabidopsis PeptideAtlas based on hundreds of existing MS studies from laboratories around the world, collected through ProteomeXchange (http://www.proteomexchange.org/), and reanalyzed through a uniform processing pipeline. All matched MS-derived peptide data will be projected onto predicted primary protein sequences with links to spectral, technical and biological metadata. This PeptideAtlas and its metadata will serve to address several biological questions and hypotheses, such as which mRNA isoforms are supported by proteomics evidence and is there evidence for intra-protein cross-talk or competition between PTMs. The Arabidopsis PeptideAtlas will be linked to TAIR (https://www.arabidopsis.org/) and other web-based resources. Complete dataset will be publicly available for download, thus further facilitating data mining by the scientific community and amplifying the impact of this project. Once the first few builds are released for Arabidopsis, and following community feedback/interactions, PeptideAtlas building will be extended to other plant species of societal and/or economic importance for which extensive and comprehensive MS-based proteomics data are available (e.g. maize, tomato, rice, soybean).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.