Untargeted Metabolomics Pipeline Development for Natural Products

天然产物的非靶向代谢组学管道开发

• 批准号: 543591-2019
• 负责人: Linington, Roger
• 金额: $ 7.29万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=692830
• 关键词: Untargeted; Metabolomics; Pipeline; Development; Natural

Small molecule chemistry governs many of the interactions in the natural world, from interspecies communication in chemical ecology, to the propagation of disease in human health. However, there remains no facile mechanism to describe either the chemical constitution, or the identities of molecules from complex unknown samples. In contrast to targeted metabolomics, where excellent methods exist to quantify the concentrations of hundreds of target analytes in a single experiment, data analysis tools in untargeted metabolomics are rudimentary at best. Current tools suffer from very high false discovery rates (sometimes over 90%) and poor accuracy for metabolite identification. We will use a combination of developments in mass spectrometry methods for natural products, custom software code, novel data analysis algorithms. Big Data processing methods, and a newly created natural products structure database to develop a unique, accurate and flexible platform for untargeted metabolomics for natural products.

小分子化学控制着自然界中的许多相互作用，从化学生态学中的物种间交流到人类健康中的疾病传播。然而，仍然没有简单的机制来描述复杂未知样品的化学组成或分子的身份。与靶向代谢组学相比，存在在单个实验中量化数百种目标分析物浓度的优秀方法，非靶向代谢组学中的数据分析工具充其量是初步的。目前的工具具有非常高的错误发现率（有时超过90%）和代谢物鉴定的准确性差。我们将结合天然产物质谱分析方法的发展，定制软件代码，新颖的数据分析算法。大数据处理方法和新创建的天然产物结构数据库，为天然产物的非靶向代谢组学开发独特，准确和灵活的平台。