Untargeted Metabolomics Pipeline Development for Natural Products

小分子化学控制着自然界的许多相互作用，从化学生态学中的物种间交流，到人类健康疾病的传播。然而，仍然没有简单的机制来描述化学结构，或者来自复杂未知样品的分子的身份。与靶向代谢组学相比，在单个实验中存在优秀的方法来量化数百种目标分析物的浓度，而非靶向代谢组学的数据分析工具充其量是初级的。目前的工具存在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。我们将结合天然产品的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，以及新创建的天然产物结构数据库，为天然产物非靶向代谢组学开发独特、准确、灵活的平台。

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.