Abstract

The goal of untargeted metabolomics is to profile metabolism by measuring as many metabolites as possible. A major advantage of the untargeted approach is the detection of unexpected or unknown metabolites. These metabolites have chemical structures, metabolic pathways, or cellular functions that have not been previously described. Hence, they represent exciting opportunities to advance our understanding of biology. This beneficial approach, however, also adds considerable complexity to the analysis of metabolomics data - an individual signal cannot be readily identified as a unique metabolite. As such, a major challenge faced by the untargeted metabolomic workflow is extracting the analyte content from a dataset. Successful applications of metabolomics bypass this limitation by throwing away the 99% of the dataset that is not statistically altered between sample groups.1 This widely accepted approach to untargeted metabolomics is functional for a very narrow set of applications, but critically, it fails to provide a comprehensive view of metabolism.

Committee Chair

Gary J. Patti

Committee Members

Jacob Schaefer, Michael L. Gross, Steven L. Johnson, Tim Schedl,

Comments

Permanent URL: https://doi.org/10.7936/K7K072QD

Degree

Doctor of Philosophy (PhD)

Author's Department

Chemistry

Author's School

Graduate School of Arts and Sciences

Document Type

Dissertation

Date of Award

Spring 5-15-2017

Language

English (en)

Author's ORCID

https://orcid.org/0000-0002-0469-9934

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Chemistry Commons

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