BBSRC EASTBIO PhD Programme: Deep learning approaches for efficient interpretation of MS peptide spectra.

Investigation of the application of deep neural networks to identify signals in raw MS spectra that can be reliably associated with specific cell phenotypes and/or disease states. This can potentially identify new complex biomarkers with clinical value and also help to improve the efficiency of analysing MS-based proteomics data by avoiding algorithmic pre-processing of the raw MS data, which is time consuming and introduces sources of error. The project will benefit from the availability of existing very large collections of highly annotated MS proteomics data in the Lamond Laboratory and access to existing large-scale computational infrastructure.

OBJECTIVES: Compare different deep learning structures to design neural networks best suited to identifying indicative features within large sets of complex MS-based proteomics data at different levels of granularity (e.g. MS1 spectra, MS2 spectra, peptide IDs, proteins). To apply the resulting optimal neural network to interpret MS data derived from analysis of healthy and diseased cells and thereby identify complex biomarkers diagnostic for specific cell phenotypes.

SUPPORTING INFORMATION: Professor Lamond heads the Laboratory for Quantitative Proteomics, one of the largest research facilities in this field in Europe. The Lamond group has a >10 year track record in working with colleagues from the University of Dundee School of Computing to build new computational tools for the analysis and interactive interpretation of large-scale, complex proteomics data sets from human cells and model organisms (see; www.peptracker.com/epd). Professor Lamond has previously supervised and co-supervised, in collaboration with colleagues from the School of Computing, numerous PhD and Master’s projects involving investigations of how state of the art Data Science can be applied to enhance analysis of proteomics data.