Capacity: BBSRC-NSF/BIO: Globally harmonized re-analysis of Data Independent Acquisition (DIA) proteomics datasets enables the creation of new resources (DIA-eXchange)

研究界已经生成了数百万个用于回答感兴趣的科学问题的数据集，并将这些数据集存储到公共数据存储库中，作为全球资助机构强制数据共享政策的一部分。特别是对于为测量生物样品的蛋白质含量而生成的数据集，在重新分析过程中，可以使用更新的技术、更新版本的软件和更新的参考信息，从这些公共数据集中提取大量额外的信息。该项目将开发一个软件基础设施，用于全球统一重新分析一种新兴类型的蛋白质组学数据集，以从旧数据中提取新信息。该基础设施将提高我们将蛋白质组学数据转化为有关全球相对蛋白质丰度的知识以及从蛋白质丰度如何相关推断的蛋白质之间相互作用的能力。该项目还将为学生提供学习科学数据分析技能的机会。该项目是EMBL-EBI （Hinxton， UK）蛋白质组学团队和利物浦大学（UoL； UK）系统、分子和整合生物学研究所（ISMIB）的国际合作项目。该项目将使数据独立获取（DIA）质谱蛋白质组学方法生成的数据集更易于查找、访问、互操作和重用（FAIR）。这将通过开发用于分析DIA实验数据的质谱库的索引系统来完成。该项目将进一步制定基准和指导方针，以推进该领域的发展，然后开发数据分析管道，以大规模处理数千个公共实验，并将结果提供给研究界。在数千个实验中得到的蛋白质丰度图将通过从这些数据集中提取的蛋白质共表达模式的推断来开发蛋白质相互作用图，这只能通过数千个数据集的一致分析来完成。学生将有机会参与这项工作，以建立他们的技能，所有软件和数据产品将公开，以进一步推进该领域。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。

The research community has generated millions of datasets that were used to answer scientific questions of interest, and deposited those datasets into public data repositories as part of the mandated data sharing policies of funding agencies worldwide. Especially for datasets generated to measure the protein content of biological samples, substantial additional information can be extracted from these public datasets using newer techniques, newer versions of software, and newer reference information during re-analysis. This project will develop a software infrastructure for globally harmonized re-analysis of an emerging type of proteomics dataset to extract new information from older data. The infrastructure will improve our ability to turn proteomics data into knowledge about global relative protein abundances and about interactions between proteins inferred from how protein abundances are correlated. The project will also provide opportunities for students to learn skills in scientific data analysis. This project is an international collaboration with the Proteomics Team at EMBL-EBI (Hinxton, UK) and the Institute of Systems, Molecular and Integrative Biology (ISMIB) at the University of Liverpool (UoL; UK).This project will make the datasets generated by the method known as data-independent acquisition (DIA) mass spectrometry proteomics more findable, accessible, interoperable, and reusable (FAIR). This will be accomplished by developing an indexing system for the libraries of mass spectra that are used to analyze the data from DIA experiments. The project will further develop benchmarks and guidelines to advance the field, and then develop data analysis pipelines to process thousands of public experiments at scale and make the results available to the research community. The resulting protein abundance maps across thousands of experiments will enable the development of protein interaction maps via inference from protein co-expression patterns extracted from these datasets, something that can only be accomplished by thousands of datasets analyzed in unison. Students will be given opportunities to participate in the work in order to build their skills, and all software and data products will be made publicly available to further advance the field.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.