Student research project
The Metabolomics laboratory uses state-of-the-art mass spectrometry to measure high-quality lipidomes in biological samples, with currently 700+ lipid species measured, many with complete structural resolution. This enables the investigation of associations between lipid structural elements (rather than whole lipid species) and clinically relevant outcomes.
Until recently, the computational analysis of lipidomics data was restricted to investigating associations between the measured lipid levels and pathological outcomes of interest, and integrating results to higher-level abstractions (such as metabolic pathways, dietary sources, etc.) was done manually. However, with technological improvements leading to the higher precision measurement of more numerous, structurally resolved lipids, it has become possible to computationally investigate the associations between lipid structural elements, lipid pathways, and other lipid meta-data (“annotations”), with clinically relevant outcomes.
The student will contribute to various facets of this ongoing research to a level in adequacy with their skills and interests. The overarching aims include:
- Investigate the biological relevance and applicability of existing efforts towards lipid annotation (e.g. reference 1).
- Conceive and implement an annotation database using expert in-lab knowledge of lipids.
- Run annotation enrichments analyses using this database on lipid sets of interest derived from lipidomics analyses (regarding outcomes such as cardiovascular disease, diabetes, obesity, and Alzheimer’s Disease), available in the lab.
- Contribute to the resulting papers.
- If available, interface with our other student project on summarising the information in groups of lipids.
This project is suitable for a Masters, Honours or PhD student and the student must be comfortable with the general notions behind database design, and have some experience working in the statistical programming language R. Basic knowledge in organic chemistry and metabolism would be very helpful, as would familiarity with an existing ontology, such as the gene ontology2.
- LION/web: a web-based ontology enrichment tool for lipidomic data analysis GigaScience 2019.
- The gene ontology consortium. The gene ontology resource: 20 years and still GOing strong Nucleic Acids Res 2019.