Computational methods for identification and characterization of metabolites
Abstract/Contents
- Abstract
- Metabolites are the small organic molecules that serve as the precursors to, actors in, and products of cellular metabolism. They create a unique dynamic fingerprint of a living system's chemical state, yet the metabolite composition of almost any biological sample cannot be completely determined using current methods. Liquid chromatography - mass spectrometers are workhorse instruments for analyzing metabolites and can detect analytes at ultra low concentrations. As such, untargeted metabolomics studies in which the global metabolite fingerprint of a system is measured generate incredibly rich datasets with signals coming from potentially thousands of metabolites. However, only a small fraction of the metabolite signals in an untargeted metabolomics dataset can be identified and mapped to a chemical structure. The work here describes the development of computational analysis tools for metabolite identification and characterization in untargeted metabolomics data to increase the amount of biochemical insight generated from a given study. First, a pipeline for mapping raw data to chemical features of interest is created, and this pipeline is used in the creation of a map of the human colon metabolome. Next, a supervised topic modeling approach is explored for the structural characterization of individual unknown molecules measured via tandem mass spectrometry. Finally, an approach for integrating mass spectrometry metabolite data from heterogeneous experimental sources and protocols is investigated. Together, these approaches represent advances towards more comprehensive and insightful analysis of metabolomics data using computational methods.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Reder, Gabriel |
|---|---|
| Degree supervisor | Fischbach, Michael |
| Thesis advisor | Fischbach, Michael |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Holmes, Susan |
| Degree committee member | Altman, Russ |
| Degree committee member | Holmes, Susan |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Gabriel Reder. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/qg350hg3257 |
Access conditions
- Copyright
- © 2021 by Gabriel Reder
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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