The Statistics and Bioinformatics Core (SBC) of the Michigan Regional Comprehensive Metabolomics Resource Core (MRC2) will assist in the design of optimal metabolomic experiments and transform measurements produced by the Analytical and Data and Information Technology Cores into information and knowledge that will enhance the research of the Core user. To accomplish these goals, the SBC will benefit from its close association with the Department of Computational Medicine and Bioinformatics and its National Center for Integrative Biomedical Informatics (NCIBI). Specifically, the SBC will assist MRC2 users in the design of metabolomics studies in cells, tissues and biofluids. Following analysis, the Core will provide the Core user with data sets from directed metabolomics measures that have been evaluated for quality and with information of the estimated errors for the measurement of the metabolites. The Core will develop and apply statistical methods and tools to accurately define levels of metabolites and compare treatment effects in untargeted metabolomic analysis and provide individualized statistical tools for analysis of metabolomics data for Core users. To better obtain information about individual metabolites of interest the SBC will provide bioinformatics tools, such as Metab2Mesh and Metscape to provide insights into the metabolites and to visualize individual and groups of metabolites In metabolic pathways. Using these, and other tools, SBC personnel will work with investigators to develop appropriate procedures for multi-scale integration of phenotypic and metabolomic data. Working with the Promotion and Outreach Core and the NCIBI, investigators will be trained in the methodologies related to Laboratory Information Management System (LIMS) utilization, spectral interpretation, data management, and data conversions. To increase the pool of metabolomics investigators, the Core will train graduate students in the technologies essential for metabolomics biostatistical and bioinformatic data analysis. Finally, the SBC will provide new tools and techniques to other Regional Comprehensive Metabolomics Resource Cores (RCMRCs) to enhance their ability to provide services to Core customers for metabolomic analysis.

Public Health Relevance

Data generated from metabolomics analysis need to be carefully evaluated to obtain the inherent information in the data sets. Statistical methodology can be utilized to improve the recognition of changes in metabolites in disease states and bioinformatics technologies can be used to better visualize the pathways containing metabolites of interest and to integrate them with other information to understand the disease pathogenesis and responses to treatment.

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University of Michigan Ann Arbor
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