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

连接质谱计算生态系统以增强对公开数据的生物学见解

• 批准号: 2152526
• 负责人: Pieter Dorrestein
• 金额: $ 89.4万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152526&HistoricalAwards=false
• 关键词: Linking; Mass; Spectrometry; Computational; Ecosystems

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.

代谢物注释是从代谢/代谢组学数据集中获取生物知识的关键过程。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，以及其数据存储在代谢组学数据库中的数量，包括英国的代谢光(https://www.ebi.ac.uk/metabolights/)和美国的全球天然产品社会分子网络(GNPS：https://gnps.ucsd.edu/))，正在呈指数级增长(数据量每两年增加300%)。目前代谢组学数据库中的数据量可以被描述为大数据，并为重复使用、重新分析和荟萃分析研究提供了重要机会，以调查代谢物在包括微生物、植物、哺乳动物和其他无脊椎动物在内的生物系统中的作用。存放在代谢组学数据库中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，就无法实现生物数据的集成或解释，这些信息被定义为代谢物注释。可能有成百上千的代谢物没有在每项研究中指定的化学结构。目前的大部分数据来自液-质联用化学分析，其中应用色谱保留时间、完整代谢物的质量和气相碰撞诱导(MS/MS)碎片离子的质量来得出化学结构。全球导航卫星系统和新陈代谢系统提供的数据和工具有很大差异，这限制了它们对所有用户的充分惠益。在这个项目中，加州大学圣地亚哥分校、宾厄姆顿大学和欧洲生物信息学研究所将合作扩大和加强计算工作流程，以进一步增加被注释的代谢物的数量并增加这些注释的可信度，然后将随后将额外的工具应用于MetabLights中提供的所有数据集以及所有未来提交的数据集。这一国际努力旨在开发一种适用于代谢组学数据库中所有存储数据的工作流程，并将来自两个资源的数据和专业知识结合在一起，这将极大地造福于全球组学社区。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils

• DOI: 10.1021/acs.jproteome.2c00632
• 发表时间: 2023-01-23
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Bittremieux, Wout;Levitsky, Lev;Dorrestein, Pieter C.
• 通讯作者: Dorrestein, Pieter C.