Supporting analysis, visualisation and biological interpretation of metabolomics datasets

Gloaguen, Yoann (2017) Supporting analysis, visualisation and biological interpretation of metabolomics datasets. PhD thesis, University of Glasgow.

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Printed Thesis Information: https://eleanor.lib.gla.ac.uk/record=b3289936

Abstract

Over the past decades, the emerging omics technologies have enabled scientists to take a step
further in the investigation of biological systems. From food safety to stratified medicine,
omics technologies are now an essential and powerful means to study biological processes.
Omics technologies are however at different stages of maturity, and the most recent field of
the omics family, metabolomics, is still in its infancy. Metabolomics attempts to catalogue,
characterise and quantify all small molecules constitutive of a biological system. Liquid
Chromatography - Mass Spectrometry (LCMS) is now the most commonly used technique
to generate metabolomics data. The method allows the detection of hundreds of metabolites
from a single sample and can provide a rapid assignment of formulae to detected masses
using high accuracy mass spectrometers. While analytical methods are well developed, support
for linking metabolites to detected features and interpreting the results of a data analysis
in a biological context is still poorly developed. Significant challenges also arise from the
additional steps required to export the data to third party environments to create a biological
context. The study of integrated omics datasets as a single system has also shown to
provide greater inferences than the study of each omics separately. Methods to integrate the
different omics layers of biological systems are, however, at an early stage of development
and no standard approach currently exists to provide a holistic view of organisms systems
organisation.
The objective of this thesis is to formalise, standardise and unify the data analysis of the
metabolomics field, by providing to biologists the tools to support them from planning to
analysis to biological impact reporting. The work presented here focuses particularly on
untargeted LC-MS metabolomics approaches and attempts to assist non-expert users in performing
their own analysis of metabolomics datasets. The project also aims to enable systematic
biological interpretation of metabolomics datasets. The first part of the thesis focuses
on creating the foundation of a unified environment for LC-MS metabolomics data analysis.
Subsequently, the created environment will be expanded to integrate and support the latest
technological advances in the field and provide better support for both designing studies and
interpreting analysis results in a biological context. Finally, the last part of this thesis concentrates
on integrating metabolomics data with other omics datasets in an attempt to provide
a holistic view of a biological system.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Metabolomics, LCMS, data analysis, data visualisation, software, pipeline, web.
Subjects: Q Science > QR Microbiology
Colleges/Schools: College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
Supervisor's Name: Burgess, Dr. Karl and Barrett, Prof. Mike
Date of Award: 2017
Depositing User: Mr Yoann Gloaguen
Unique ID: glathesis:2017-8433
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 09 Oct 2017 13:22
Last Modified: 22 Nov 2017 10:02
URI: http://theses.gla.ac.uk/id/eprint/8433
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