III: Small: RUI: Investigating Fragmentation Rules and Improving Metabolite Identification Using Graph Grammar and Statistical Methods

拟议的工作解决了代谢物识别的长期挑战：缺乏对来自各种分析方法的大量数据的碎片化规则。该项目不仅可以促进代谢组学的发展，而且可以促进相关领域的发展。串联质谱仪(MS/MS)是一种常用的方法，因此一系列领域可以从改进的识别性能中受益，并可以应用于其他系统。此外，成功地将图文法应用于解决代谢组学中的图论问题，也将为其他科学家利用图文法来解决他们的研究问题提供支持。这个项目的一个明确的重点是让本科生参与计算研究，整个研究项目的各个方面都是在适当的水平上设计的。本项目的具体研究目标是(1)将MS/MS数据的分析问题转化为图论问题，(2)使用图文法和统计方法来研究碎片规则，(3)将这些规则集成到识别工具MIDAS-G中。图文法可以灵活地表示和处理相邻结构。通过利用其高水平的解释，图文法可以与统计方法一起工作，从带注释的MS/MS数据中显式地研究碎片规则。这些学习到的规则将被整合到识别工具中，算法将被设计成使该工具能够高效地识别代谢物。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The proposed work addresses the long-standing challenge of metabolite identification: the lack of fragmentation rules for the large amounts of data from various approaches to analysis. This project can not only advance metabolomics, but also enhance related fields. Tandem mass spectrometry, MS/MS, is a generally used approach, so a range of fields can benefit from improved identification performance, and may be applied to other systems. Moreover, successfully applying graph grammar to solve the graph theory problem in metabolomics will provide support to other scientists who also use graphs to address their research problems by adopting graph grammar methods. A clear focus of this project is to involve undergraduate students in computational research, with the aspects of the overall research project designed at an appropriate level.The specific research objectives of this project is to (1) convert the problem of analyzing MS/MS data into a graph theory problem, (2) use graph grammar and statistical methods to investigate fragmentation rules, and (3) integrate these rules into an identification tool, MIDAS-G. Graph grammar can flexibly represent and process adjacent structures. By taking advantage of its high level for interpretation, graph grammar can work with statistical methods for explicitly investigating fragmentation rules from annotated MS/MS data. These learned rules will in turn be integrated into the identification tool and algorithms will be designed to enable the tool to identify metabolites efficiently.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.