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

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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.