Novel software tools for improved processing and interpretation of metabolomics data

In a typical metabolomics experiment huge amounts of raw data are generated. This makes the efficient automated data processing a necessity for state of the art metabolomics workflows. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, in which the data contain a large extent of noise and background related signals, data processing and visualisation still consist major bottlenecks. of analytical workflows. Within this project, novel algorithms and software tools for improved evaluation of metabolomics data will be developed: The developed tools will be applied to study cereals such as wheat and maize, filamanteous fungi and their biological interaction. The project has three specific goals: 1) Optimisation and integration of software tools for the processing of stable isotope assisted LC-HRMS based metabolomics into a single software suite 2) Development of generic R script(s) for data analysis, visualisation and statistics. These Scripts shall provide templates for various future metabolomics experiments 3) Application and refinement of the developed stable isotope labelling assisted workflows and software algorithms within a functional genomics study. In collaboration with the group of Gerhard Adam we want to identify and characterise polyketide-type secondary metabolites of F. graminearum putatively involved in Fusarium head blight pathogenesis.