Supervisors: Runxuan Zhang, Piers Hemsley
Project Description:
Mass spectrometry (MS) based proteomics is the method of choice for characterizing proteins to understand biological functions and processes, elucidate signalling networks, discover disease biomarkers for human and identify key genes underlying important traits in plants. Computational methods for proteomics play an essential role in interpreting MS data and generating biological insights, but their potentials remains to be fully exploited. Particularly in a plant proteomics experiments, fewer than 20% of the high-quality MS/MS spectra acquired can be meaningfully interpreted. This largely reflects the limited sensitivity of current computational methods and the lack of complete, accurate and concise protein isoform sequence information. In this project, we aim to address these critical issues in proteomics by developing a novel probability based computational approach for Peptide Spectrum Match (PSM), which transforms the mass information to sequence information and improves peptide identification by increasing accuracy, sensitivity, and capability for resolving mass shifting events and assessing false discoveries.
The PhD candidate will develop in-depth knowledge about the technology of mass spectrometry-based proteomics and carry out literature reviews on the computational methods for proteomics data processing, including peak detection, peptide spectrum match, searching for post translational modifications and statistical methods on false positive control. The PhD candidate will also learn to use a variety of tools for bio-informatic analysis. The student will learn to pre-process large scale proteomics data, perform proper quality controls, understand and master various tools on peak detection, peptide/protein/PTM identifications. Learn to carry out customized analysis in R/python language and use shell script under Unix. The PhD candidate make use of the extensive shotgun proteomics data of Arabidopsis and barley available in Hemsley lab and Waugh lab and PTM proteomics data sets for phosphorylation, S-acylation and ubiquitination from University of Dundee collaborators.The PhD candidate will develop a novel method that transforms the mass information to sequence information and improves peptide identification and validate and evaluate the developed methodology on proteomics datasets in plants.
This project provides a great opportunity for the student to learn a diverse set of skills spanning a variety of subjects for multi-disciplinary research, including bioinformatics, plant biology and modern bio-technology.
• Bioinformatics: The student will learn to pre-process large scale proteomics data, perform proper quality controls, understand and master various tools on peak detection, peptide/protein/PTM identifications. Learn to carry out customized analysis in R/python language and use shell script under Unix.
• Plant biology: the student will learn about post-translational modification, particularly S-acylation, and its regulatory mechanism under different conditions.
• Bio-technology: The student will learn mass spectrometry-based proteomics technology, methods to prepare samples for proteomics experiments, and carry out validations using plant antibody resources in house to pull down proteins with novel peptides or PSMs.
This project will provide an exceptional opportunity for a student to become expert in both data analysis and wet lab experiments. This cutting-edge research will also expose the student to new methods and new technologies through the four years of study. In addition, both programs offer multiple opportunities for students to present their work in poster and oral formats. The student will therefore enter a stimulating intellectual and supportive environment.
To apply for this project, please go to this link.
