Cross Repository Metabolomics Data and Workflow Integration

跨存储库代谢组学数据和工作流程集成

• 批准号: 10576731
• 负责人: PIETER C DORRESTEIN
• 金额: $ 30.28万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10576731
• 关键词: Address; Animal Model; Biological; Communities; Computing Methodologies; Controlled Vocabulary; Data; Data Analyses; Data Set; Ecosystem; Environment; FAIR principles; Funding; Human Microbiome; Infrastructure; Internet; Joints; Mass Spectrum Analysis; Meta-Analysis; Metadata; Methods; Molecular; Natural Products; Physical activity; Publishing; Reproducibility; Research; Resources; Semantics; Software Tools; Source; Standardization; Testing; Transducers; United States National Institutes of Health; Validation; Visualization; analysis pipeline; base; bioinformatics pipeline; computer infrastructure; computerized tools; dashboard; data analysis pipeline; data exchange; data exploration; data format; data harmonization; data repository; data resource; data tools; experimental study; file format; heterogenous data; insight; interoperability; metabolomics; metabolomics resource; metadata standards; portability; prevent; repository; secondary analysis; social

Project Summary/Abstract The lack of uniformity in published experimental methods and data is a major impediment for the research community to compare, corroborate, and build upon biomedical discoveries. The FAIR data principles state that research data should be “findable, accessible, interoperable, and reusable.” Public metabolomics data repositories and large-scale studies supported by the NIH Common Fund, including Metabolomics Workbench and the Integrated Human Microbiome Project (iHMP), and other public mass spectrometry data repositories, such as the Global Natural Products Social Molecular Networking (GNPS) and MetaboLights, have made progress in recent years to address the first two FAIR principles by making metabolomics data easily findable and accessible. Unfortunately, the final two FAIR principles, which state that data should be interoperable and reusable, have not been adequately addressed yet by the metabolomics community. This prevents metabolomics data from multiple relevant studies to be compared and co-analyzed. This proposal aims to bridge this interoperability and reusability gap by harmonizing community standards and creating accompanying computational tools for data re-analysis. Specifically, this proposal will 1. Standardize and convert mass spectrometry data formats (Aim 1), 2. Harmonize experimental metadata and analysis results with common controlled vocabulary with consistent semantics across all experiments (Aim 1), 3. Develop web infrastructure to find and explore datasets by metadata (Aim 1), and 4. Develop cloud-enabled portable, reusable, and scalable co-analysis bioinformatics pipelines (Aim 2). Successful completion of these aims will democratize the ability for the entire metabolomics community to corroborate published findings, discover new metabolites that are highlighted only when co-analyzing datasets, and test translational hypotheses across different model organisms.

项目总结/摘要 发表的实验方法和数据缺乏统一性是研究的主要障碍。 研究社区比较，证实，并建立在生物医学发现。公平 数据原则指出，研究数据应该是“可找到的，可访问的，可互操作的， 可重复使用”公共代谢组学数据库和NIH支持的大规模研究 共同基金，包括代谢组学和综合人类微生物组 项目（iHMP）和其他公共质谱数据库，如全球自然 产品社交分子网络（GNPS）和MetaboLights，最近取得了进展， 多年来，通过使代谢组学数据易于查找， 容易接近不幸的是，最后两个公平原则，其中规定，数据应该是 可互操作和可重复使用，尚未得到代谢组学的充分解决 社区这阻止了来自多个相关研究的代谢组学数据进行比较， 共同分析。该提案旨在通过协调 社区标准，并为数据重新分析创建相应的计算工具。 具体而言，该提案将1。标准化和转换质谱数据格式（Aim 1）、2.将实验元数据和分析结果与通用受控词汇表相协调 在所有实验中具有一致的语义（目标1），3.开发Web基础架构以查找 并通过元数据探索数据集（目标1），以及4.开发支持云计算的便携式、可重用和 可扩展的共同分析生物信息学管道（Aim 2）。成功实现这些目标将 民主化整个代谢组学社区的能力，以证实已发表的发现， 发现仅在共同分析数据集时突出显示的新代谢物，并测试 不同模式生物之间的翻译假说。