Improved computational methods for quantitative proteomics
- Supervisor: Prof. Conrad Bessant
- Deadline: None
- Funding: CONACYT
A single sample of human tissue or bodily fluid typically contains thousands of individual proteins, from a possible set of over one million if all protein variations are considered. Identifying and quantifying these proteins (so-called quantitative proteomics) is essential if we are to fully understand human physiology and uncover the processes behind complex human diseases. Various laboratory protocols based on liquid chromatography tandem mass spectrometry (LC-MS/MS) have been developed for quantitative proteomics, each producing gigabytes of data per sample. Processing the acquired data to produce useful protein abundance data is a major challenge and existing software for doing this is generally slow, difficult to use, and sometimes of questionable accuracy.
The aim of this PhD project is twofold. Firstly you will perform a thorough evaluation of existing quantitative proteomics tools, identify the limitations of these tools in terms of computational efficiency, accuracy and usability. You will then develop improved algorithms that overcome some of these limitations, ultimately making these algorithms publicly available by implementing them in QMUL’s web-based GIO proteome informatics framework (gio.sbcs.qmul.ac.uk). You will have access to state-of-the-art computing facilities and there is substantial flexibility in terms of the algorithms and technologies that you may use to tackle the project.
You will already have a strong background in data analysis and coding through studies in computer science, bioinformatics, or a similar subject. You will have the opportunity to further develop these skills throughout the PhD, and in your first weeks at QMUL you will receive intensive training in proteomics, bioinformatics and related subjects by attending selected modules from our MSc Bioinformatics programme. The PhD will provide the perfect preparation for a career in bioinformatics or other areas of “big data” research.
You will already have a strong background in data analysis and coding through studies in computer science, bioinformatics, or a similar subject.
Applicant requirements are listed on the CONACYT foreign scholarship pages.
International students must provide evidence of proficient English language skills, see our entry requirements page for further information.
NB If you are interested in self-funding please contact Prof. Bessant by e-mail (firstname.lastname@example.org) to discuss your eligibility for this project.
- Potential candidates should contact Prof. Bessant by e-mail (email@example.com) and submit their CV and a cover letter explaining their eligibility and interest in this project.
- Applications to Queen Mary are accepted all year round but we encourage you to contact Prof. Bessant as soon as possible. If he agrees to take your application further you will be required to submit an online application.
- If you are successful we will give you an offer on the condition that you are given a funding award from CONACYT.
- When you have received a conditional offer from us, you should apply directly to CONACYT.