Proteomics data generated by cutting-edge mass spectrometers play a crucial part in early disease diagnosis, prognosis and drug development in the biomedical sector. It can be used to understand the expression, structure, function, interactions and modifications of virtually any protein in any cell, tissue or organ. Moreover, proteomics can be used in conjunction with other “omics” technologies such as genomics, transcriptomics or metabolomics to further unravel the complexity of signalling pathways and other subcellular systems.

Although the majority of proteomics data is visualised and reported for convenience of the end user on the protein level, the mass spectrometer is actually measuring and quantifying protein fragments (peptides), which are then “converted” computationally into protein-centric data. This conversion from peptide to protein level is quite error-prone, and it is better – and often even required - to present proteomics data on the peptide level and not on the protein level. However, appropriate visualisation strategies and comparative analyses are not well established yet.

This project will focus putting computational algorithms and statistical strategies in place to enable a peptide-centric analysis when interrogating and visualising proteomics data .

The student will be required to use programming languages such as R and/or Python to interrogate, manipulate, analyse and visualize proteomic datasets and to implement these methodologies within the Monash Proteomics and Metabolomics facility. They will get hands-on experience with real-world biomedical data, and learn critical data science skills including programming, data manipulation, statistical analysis and reporting.

For more information, contact the primary supervisor A/Prof Ralf Schittenhelm <ralf.schittenhelm@monash.edu>