Linking Mass Spectrometry Computational Ecosystems to Enhance Biological Insights of Publicly-Available Data

代谢物注释是从代谢/代谢组学数据集中获得生物学知识的关键过程。在同行评审的科学期刊上发表的代谢组学研究项目的数量正在呈指数级增长，其数据被存储在代谢组学数据库中，包括英国的MetaboLights （https://www.ebi.ac.uk/metabolights/）和美国的Global Natural Products Social Molecular Networking (GNPS: https://gnps.ucsd.edu/)，数据量每两年增长300%。代谢组学数据库中目前存在的数据量可以被描述为“大数据”，并为重复使用、再分析和荟萃分析研究提供了重要机会，以调查代谢物在生物系统中的作用，包括微生物、植物、哺乳动物和其他无脊椎动物。存入代谢组学数据存储库的大多数数据没有分配给它的化学结构，如果没有这些信息（定义为代谢物注释），就无法实现生物数据整合或解释。在每项研究中，可能有成百上千种代谢物没有指定的化学结构。目前的大部分数据来自液相色谱-质谱化学分析，其中色谱保留时间，完整代谢物的质量和气相碰撞诱导（MS/MS）碎片离子的质量用于推导化学结构。通过GNPS和Metabolights获得的数据和工具存在显著差异，这限制了它们对所有用户的充分受益。在这个项目中，加州大学圣地亚哥分校、宾厄姆顿大学和emlb -欧洲生物信息学研究所将合作扩展和加强计算工作流程，以进一步增加代谢物注释的数量，并增加这些注释的可信度，然后将额外的工具应用于MetaboLights中所有可用的数据集以及所有未来提交的数据集。这项国际努力旨在开发一种工作流程，应用于代谢组学数据存储库中的所有存储数据，并将两种资源的数据和专业知识结合起来，将极大地造福全球组学社区。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。

Metabolite annotation is a key process to derive biological knowledge from metabolic/metabolomic datasets. The number of metabolomics research projects being published in peer reviewed scientific journals and whose data are being deposited in metabolomics data repositories, including MetaboLights (https://www.ebi.ac.uk/metabolights/ ) in the UK and Global Natural Products Social Molecular Networking (GNPS: https://gnps.ucsd.edu/) in the USA, is exponentially increasing (the volume of data increases by 300% every two years). The volume of data now present in metabolomics data repositories can be described as 'big data' and provides significant opportunities for reuse, re-analysis and for meta-analysis studies to investigate the roles of metabolites in biological systems including microbes, plants, mammals and other invertebrates. The majority of data deposited into metabolomics data repositories do not have a chemical structure assigned to it and no biological data integration or interpretation is achievable without this information, defined as the metabolite annotation. There can be hundreds or low thousands of metabolites which do not have a chemical structure assigned in each study. The majority of data present is derived from liquid chromatography-mass spectrometry chemical assays where the chromatographic retention time, mass of the intact metabolite and mass of gas phase collision induced (MS/MS) fragment ions are applied to derive a chemical structure. The data and tools available through GNPS and Metabolights have significant differences and this limits their full benefit to all users. In this project the University of California San Diego, the University of Binghamton, and EMBL-European Bioinformatics Institute will collaborate to expand and strengthen the computational workflow to further increase the number of metabolites annotated and to increase the confidence of these annotations and will then subsequently apply the additional tools to all datasets available in MetaboLights as well as for all future submitted datasets. This international effort to develop a workflow to apply to all deposited data in a metabolomics data repository and combining data and expertise from two resources will greatly benefit the global 'omics community.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.