ABI Innovation: A Metabolomics Toolkit for NMR and Mass Spectrometry

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With support from the Advances in Biological Informatics in the Division of Biological Infrastructure, Professor Robert Powers and his research group at the University of Nebraska - Lincoln will further develop their MVAPACK software toolkit that analyzes data generated in metabolomics research. Metabolomics is the study of the chemical signatures of the small molecules, like sugars, that are the products of biological processes. Metabolite fingerprints reveal a great deal about the recent activities occurring in biological samples. Knowing how organisms respond to their surroundings gives important leads to understanding events in fields like plant biology and agriculture, environmental and water quality studies, and human health research into nutrition or diseases. The chemical fingerprints are generated using two different technologies, called nuclear magnetic resonance and mass spectrometry. The results can be very complex and difficult to merge. This means the challenges for data analysis are also high, and include requirements to develop new approaches, like multivariate statistical methods. This proposal aims to produce software tools that will extract the most possible information from the data, including data format conversion, preprocessing, validation, significance testing, visualization, and metabolite identification at defined levels of confidence. The MVAPACK toolkit will enable important, currently unavailable data processing functions in the form of freely available and open source software modules, with an impact on data quality, sharing, information content resulting in deeper biological insights from studies that include metabolite profiling. During the award period, this research project will further enhance the MVAPACK metabolomics toolkit by including additional and urgently needed capabilities. Such improvements will include adding features to load and process liquid chromatography (LC)-MS, gas chromatography(GC)-MS and multidimensional datasets; to expand capabilities to integrate multiple datasets for chemometrics; and to automate metabolite identification, which is one of the more challenging and time-consuming aspects of metabolomics. This work will employ, among other approaches, graph theory, covariance NMR, back-scaled pseudospectral loadings, and a combined NMR and MS approach to facilitate the accurate and rapid assignment of metabolites from complex biological samples. Overall, these efforts will lead to an improved MVAPACK software package (http://bionmr.unl.edu/mvapack.php) for the metabolomics scientific community that will enhance the information content, utility, and ease of processing and combining various types of NMR and MS datasets.