Description: The Computational Metabolomics for Clinical Research course will cover innovative software, online tools and knowledge bases to analyze metabolomics datasets. Metabolomics enables discoveries of metabolic mechanisms and predictive biomarkers that can be translated into new prevention and treatment strategies for human diseases. Metabolomics data for human specimens (blood, tissue, urine, saliva, etc) are mainly collected using a liquid or gas chromatography connected to a high-resolution mass spectrometry (HRMS) instrument. Signals for a few thousand chemical compounds can be observed in such data collected for specimens from human studies. Sample sizes in these studies are often >200.  Consequently, these datasets are large and complex and need to be processed using specialized computational approaches to extract, annotate, analyze and interpret them in the context of human metabolic systems. These approaches include signal processing, chemoinformatics, biostatistics, bioinformatics, data visualization and knowledge integration. This one-week long course will cover critical discussions, mentored-practices and homework exercises on how to prepare, analyze and report metabolomics datasets.

Targeted trainees: Researchers utilizing the metabolomics data matrices (w/ compound annotations) and the raw GC/LC HRMS data files for human specimens (blood, saliva, tissue, urine, feces) and large sample sizes (n > 200), particularly generated by the West Coast Metabolomics Center (UC Davis), Broad Institute Metabolomics Platform, UNC Metabolomics Platform and Metabolon Inc.

Course Fee: $2,000

Programming language requirement: Basic R

Computer requirements: Two monitors (one for R-scripting and another for attending the Zoom session). Each participant will receive access to an online server for two months to practice the computational workflows. 

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