Reproducible computational tools for acquiring, analysing and managing model organism metabolomes

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Lawson, Thomas N. ORCID: https://orcid.org/0000-0002-5915-7980 (2019). Reproducible computational tools for acquiring, analysing and managing model organism metabolomes. University of Birmingham. Ph.D.

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Abstract

Mechanistic insights into the effects of pollutants on the environment and to human health can be discovered by analysing the metabolic differences between control and stressed model organisms. However, the identification or annotation of the observed metabolites can be challenging and is further complicated by the limited availability of tools that allow metabolite annotations to be reproducibly derived and managed.
The focus of this work was to create computational tools and workflows to allow for reproducible approaches for acquiring metabolomics data, deriving annotations, and managing and disseminating the results. The primary use case for these tools and workflows is the mass spectrometry (MS) component of the Deep Metabolome Annotation (DMA) of the ecotoxicological model Daphnia magna (the water flea).
The characteristic gas-phase fragmentation patterns of MS are commonly used within metabolomics for the purposes of metabolite annotation. A method was developed here to assess the quality of MS gas-phase fragmentation (and by extension the quality of the annotation) by determining the contribution of a selected ion’s purity within a targeted isolation window. Tools were also developed for metabolite annotation including for spectral matching of fragmentation spectra to spectral libraries. Workflows were developed to aid in the acquisition of fragmentation spectra including approaches for optimising the scheduling of MS fragmentation. A tool for metadata extraction for the open source MS data file format was developed to aid in the creation of ISA-tab files for upload into public metabolomics data repositories. All the tools and workflows were integrated into the Galaxy workflow environment, where possible, and a metabolite annotation workflow was developed that incorporated both novel and existing tools. The Django MOGI application suite (Metabolomics Organisation with Galaxy and ISA) was then designed and developed for managing metabolomic analysis and used to create the DMA Database (DMAdb). The above tools and workflow were then used to analyse and manage data generated from the liquid chromatography tandem mass spectrometry experimental assays from the DMA of D. magna - substantially increasing the metabolome knowledge of this ecotoxicology relevant model organism and providing a resource a valuable resource for ecotoxicological research.
The complexity in both the experimental and computational steps in obtaining a metabolite annotation necessitate careful consideration for the management of data and meta-data. By integrating the Galaxy workflow environment and the ISA framework with the tools developed for this PhD, much needed tools for elucidating model organism metabolomes in a FAIR (findable, accessible, interoperable, re-usable) and reproducible manner have been made available to the community.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):