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.

项目摘要/摘要 公开的实验方法和数据缺乏统一性是对 研究社区以比较，证实和建立生物医学发现。博览会 数据原则指出，研究数据应“可访问，可访问，可互操作，并且 可重复使用的。”公共代谢组学数据存储库和NIH支持 普通基金，包括代谢组学工作台和综合人类微生物组 项目（IHMP）和其他公共质谱数据存储库，例如全球自然 产品社会分子网络（GNP）和代谢已在最近取得了进展 多年通过使代谢组学数据容易找到前两个公平原则，并且 可访问。不幸的是，最后两个公平原则，该原则表明数据应该是 可互操作和可重复使用，尚未通过代谢组学充分解决 社区。这样可以比较多个相关研究的代谢组学数据，并 共同分析。该建议旨在通过协调来弥合这种互操作性和可重复性差距 社区标准和创建参与的计算工具进行数据重新分析。 具体而言，该建议将1。标准化和转换质谱数据格式（AIM 1），2。与常见的受控词汇协调实验元数据和分析结果 在所有实验中（AIM 1）中具有一致的语义，3。开发网络基础架构以查找 并通过元数据（AIM 1）和4。开发启用云的便携式，可重复使用和 可扩展的共分析生物信息学管道（AIM 2）。这些目标的成功完成将 使整个代谢组学社区的能力巩固已发表的发现的能力， 发现仅在共同分析数据集时突出显示的新代谢产物，然后测试 跨不同模型生物的翻译假设。