Metabolite identification is a labor intensive task, which can be facilitated through the use of software tools to process complex data files generated from high resolution accurate mass (HRAM) MS instruments coupled to HPLC inlets. For agrochemical research, data analysis challenges include complex sample matrix interferences, identifying not only the predicted but also the unexpected metabolites, trace level concentrations, and structure elucidation such as pinpointing the site of biotransformation. Here we present Compound Discoverer software which provides powerful data processing tools and flexible workflows which facilitate identification and structure elucidation of both expected and unexpected metabolites. We will discuss the expected workflow including dealkylation and fragment ion matching to elucidate the site of biotransformation, unexpected workflow including component detection with adduct ion grouping, Pattern Scoring and Compound Class Scoring for identification of unexpected metabolites, mass defect filtering for removing of matrix interferences, mass list search and database searches to quickly identify proposed metabolites from other prediction software. In addition, we will discuss statistical tools and pathway mapping in Compound Discoverer for metabolomics analysis of agro chemical samples. The workflows and data processing results from Compound Discoverer will be validated using agro chemical samples. We will also discuss data review of the expected and unexpected tables and how the expected and unexpected data can be linked within one table.

Advanced software tools for metabolite identification and metabolomics analysis in agro chemical research

2019 Fall National Meeting

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