DEVELOPING THE UNTARGETED METABOLOMIC WORKFLOW FOR HIGH-THROUGHPUT ANALYSES

开发用于高通量分析的非目标代谢组工作流程

• 批准号: 8687653
• 负责人: Gary J Patti
• 金额: $ 36.86万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8687653
• 关键词: Address; Biological; Classification; Communities; Computer software; Core Facility; Data; Databases; Development; Diagnostic; Genomics; Link; Maps; Meta-Analysis; Metabolic; Metabolic Pathway; Methods; Molecular; Pathway interactions; Process; Publications; Recording of previous events; Relative (related person); Research; Research Personnel; Sampling; Specialist; Specificity; System; Time; Work; base; cohort; insight; liquid chromatography mass spectrometry; metabolomics; platform-independent; prevent; programs; screening; software development; tool

DESCRIPTION (provided by applicant): By using liquid chromatography/mass spectrometry (LC/MS), thousands of peaks can be detected in a metabolite extract from a typical biological sample. The unbiased and comprehensive profiling of these peaks is known as untargeted metabolomics. In contrast to targeted approaches which focus on only a subset of these molecules, untargeted metabolomics is global in scope and presents an unprecedented opportunity to interrogate previously unexplored metabolic pathways at the systems level. Despite the global scope of the untargeted approach, the overwhelming majority of metabolomic publications to date have exclusively applied targeted methods. A critical barrier that has prevented the widespread and large-scale application of untargeted metabolomics is the time and expertise required for data interpretation, specifically to establish metabolite identification. To directly address this barrier, the proposed work will develop a new untargeted metabolomic workflow in which the metabolite identification process is automated. The automated platform will accelerate the identification of large numbers of metabolites by requiring significantly less time and expertise. To support the automated platform, this proposal will develop new software which will link what is currently the most widely used metabolomic software (XCMS) with the largest metabolite database (METLIN). Importantly, XCMS and METLIN have a longstanding history of being freely available and the proposed software will therefore be highly adoptable by the general scientific community. The developed software will automatically perform two major functions: (i) relative quantitation and (ii) database searching for identification on the basis ofthe accurate mass of the intact compound as well as its tandem MS spectra. Other functionalities that will guide the non-specialist in identifying unknown compounds will also be incorporated, such as molecular classification and pathway mapping. Additionally, the software will provide a tool to perform meta-analysis across independent studies. In the latter context, the proposed work will enable the ultimate large-scale analysis by facilitating the comparison of untargeted metabolomic data from multiple labs.

描述(申请人提供)：通过使用液-质联用(LC/MS)，可以在一个典型生物样品的代谢物提取物中检测到数千个峰。这些峰的无偏和全面的图谱被称为非靶向代谢组学。与只关注这些分子的子集的靶向方法不同，非靶向代谢组学是全球性的，并提供了在系统水平上询问以前未探索的代谢途径的前所未有的机会。尽管非靶向方法的全球范围，到目前为止，绝大多数代谢组学出版物都只应用了靶向方法。阻碍非靶向代谢组学广泛和大规模应用的一个关键障碍是数据解释所需的时间和专业知识，特别是建立代谢物鉴定所需的时间和专业知识。为了直接解决这一障碍，拟议的工作将开发一种新的无靶向代谢物工作流程，其中代谢物鉴定过程是自动化的。该自动化平台将大大减少所需的时间和专业知识，从而加快识别大量代谢物的速度。为了支持自动化平台，该提案将开发新的软件，将目前使用最广泛的代谢物组学软件(XCMS)与最大的代谢物数据库(Metlin)连接起来。重要的是，XCMS和Metlin具有免费提供的悠久历史，因此拟议的软件将被普通科学界高度采用。开发的软件将自动执行两个主要功能：(I)相对定量和(Ii)数据库搜索，以根据完整化合物的准确质量及其串联MS谱进行鉴定。将指导非专家识别未知化合物的其他功能也将被纳入，例如分子分类和路径图。此外，该软件将提供一个工具，以执行跨独立研究的荟萃分析。在后一种情况下，拟议的工作将通过促进来自多个实验室的非靶向代谢组数据的比较来实现最终的大规模分析。