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Thesis_final_submission_EM_ACP.pdf (5.63 MB)

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Integrative and Network-Based Approaches for Functional Interpretation of Metabolomic Data

Patt, Andrew Christopher
http://rave.ohiolink.edu/etdc/view?acc_num=osu16257554115069

Abstract Details

2021, Doctor of Philosophy, Ohio State University, Biomedical Sciences.
Metabolism is a process that touches all aspects of life, from homeostasis to disease, such that the study of metabolites yields valuable insights into the inner workings of biological systems. Translating the findings of metabolomic and lipidomic experiments into biological insight, biomarkers, or actionable targets associated with disease requires functional interpretation of the data, which is challenging. One common strategy for interpreting metabolomic data is pathway enrichment analysis. Pathway analysis is useful because pathway-level perturbation can be more reproducible across samples than individual metabolite shifts, which are hindered by inconsistent experimental coverage of metabolites and functional redundancy of metabolites. However, pathway analysis of metabolites faces many barriers for success. Issues with metabolite pathway analysis include lack of metabolite pathway annotations, highly overlapping pathway definitions, and (again) lack of reproducibility in metabolite detection between experiments. Here, I present two complementary software resources, RaMP and MetaboSPAN, which I helped to develop in order to address these issues. RaMP is a metabolite annotations database that consolidates pathway, reaction, chemical structure, and other information from multiple publicly available data sources. RaMP’s associated R package allows users to query information on metabolites of interest as well as perform pathway enrichment analysis using the Fisher’s exact test. MetaboSPAN is an advanced pathway enrichment analysis strategy that infers activity in undetected portions of the metabolome using the vast extent of knowledge in RaMP to expand pathway-level findings and improve reproducibility between experiments. I demonstrate the utility of these tools on a metabolite data set generated in patient-derived cell lines of dedifferentiated liposarcoma with varying amplification of the MDM2 oncogene.
Ewy Mathe, PhD (Advisor)
Kevin Coombes, PhD (Advisor)
Lang Li, PhD (Committee Member)
Rachel Kopec, PhD (Committee Member)
214 p.
Bioinformatics; Biomedical Research
Metabolomics; Pathway analysis; Pathway databases; Biological network analysis; Liposarcoma

Recommended Citations

Citations

  • Patt, A. C. (2021). Integrative and Network-Based Approaches for Functional Interpretation of Metabolomic Data [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu16257554115069

    APA Style (7th edition)

  • Patt, Andrew. Integrative and Network-Based Approaches for Functional Interpretation of Metabolomic Data. 2021. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu16257554115069.

    MLA Style (8th edition)

  • Patt, Andrew. "Integrative and Network-Based Approaches for Functional Interpretation of Metabolomic Data." Doctoral dissertation, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu16257554115069

    Chicago Manual of Style (17th edition)

osu16257554115069
319
© 2021, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.