Discovery proteomics
Research in the life sciences and translational medicine is being driven forward by cutting-edge techniques for studying the molecules acting in cells. We are interested in studying what proteins are present in diseased cells and in what quantities, compared with normal cells, since the identity of the proteins may help us understand the disease process, the search for new drug targets, and act themselves as diagnostic tests for the disease. The technologies used to study proteins on a large scale are collectively called discovery proteomics, and the main method used in proteomics is mass spectrometry (MS).
Improved and novel data processing for mass spectrometry
The Dowsey group has been working on improved and novel data processing for MS for over a decade. In collaboration with proteomics laboratories in Manchester and Liverpool, they have developed a Bayesian model called BayesProt which has recently been extended to take in Bristol’s TMT data. BayesProt has proved the cornerstone of several large-scale translational studies.
BayesProt
BayesProt is fundamentally novel and enables analyses previously not possible, such as determining the relative protein levels derived from different transcripts of a gene, or the products of in-cell proteolysis. The purpose of this project is to port BayesProt to Bristol’s BlueCrystal, the University of Bristol’s High Performance Computing (HPC) machine, and integrate its functionality into the Proteomics Facilities’ workflow so that all studies passing through will benefit.
Integration of BayesProt into Galaxy Integrated Omics
BayesProt has now been ported to PBS and SLURM cluster managers utilised by BlueCrystal Phase 3 and 4. We have also integrated BayesProt into the Galaxy Integrated Omics’(GIO) environment available on BlueCrystal 3. GIO integrates a collection of state-of-the-art tools for genomics and proteomics to enable ‘proteomics informed by transcriptomics’. This system is key to the study of the effects of differential transcription or ‘gene switching’ caused by e.g. viral infection. BayesProt’s new deconvolution functionality will be critical to quantitative understanding of gene switching, and hence bringing this tool into GIO will enable us to demonstrate these possibilities
Acknowledgements / People involved in this project
Andrew Dowsey & Ranjeet Bhamber, Population Health Sciences; Kate Heesom, Biochemistry; Andrew Davidson, David Matthews, Christoph Wuelfing, & David Lee, Cellular and Molecular Medicine, all at the University of Bristol.
This project was funded by the Jean Golding Institute Seed Corn Funding Scheme 2018. To find out about other projects supported by this scheme, take a look at the Jean Golding Institute Projects.