Metabolomics is the study of the metabolome, defined as the complete collection of metabolites found within a cell, tissue, biofluid or organism. A metabolite is a small molecule, with a molecular weight < ~1.5 KDa, which is either an intermediate or end product of a biological pathway. The collection of metabolites are thought to reflect the underpinning biology and changes in response to environment, stimuli and time. The metabolic profile is also thought to be indicative for disease and potentially can provide for diagnostic tools well ahead of the actual appearance of disease symptoms and provide for an avenue of personalised health care.
Currently, there are two major techniques that are used for metabolic profiling. The first, mass-spectrometry (MS), is very sensitive in detecting the various molecules but suffers from limitations due to sample preparation requirements. The second commonly used technique, Nuclear Magnetic Resonance (NMR), has the advantage of an exquisite ‘chemical resolution’ and much less stringent sample preparation requirements. However, the sensitivity of NMR is several orders of magnitude less than MS. Combinations of the two techniques have also been employed, where the weakness of one is being alleviated by the strength of the other technique.
Aim
The overall of this project is to develop a set of integrated and new tools for the analysis of NMR metabolomics data, encapsulated in tested and validated workflows.
The project encompasses the following steps:
• Technical evaluation of existing NMR software packages and/or routines for the analysis of metabolomics NMR data; this will yield a full understanding of the merits of different packages.
• Design, testing and implementation of suitable tools for visual inspection, including automatic spectral colouring by experimental condition and automated alignment and normalisation of spectra.
• Design, testing and implementation of suitable routines for accessing various metabolomics databases, including scientific evaluation of the quality and consistency of their contents.
• Design, testing and implementation of suitable tools for identification of metabolites and the determination of metabolite concentrations across a series of spectra. This needs tools for automatic peak identification, as well as binning, integration and peak-fitting capabilities; scientific assessments of robustness and consistency.
• Development of chemometric analysis by new and improved pattern recognition and data analysis tools.
• Encapsulation of the different data analysis steps in tested and verified workflows by using existing and newly acquired metabolomics datasets. Specifically for the aspect, we will draw upon our expert CCPN partners in metabolomics at the University of Birmingham (Dr. Christian Ludwig) and the University of Liverpool (Dr. Marie Phelan).
The project will be conducted using the MRC-funded Collaborative Computational Project for NMR (CCPN; www.ccpn.ac.uk) Analysis version-3 (Analysis-V3) as a development platform and with support of its four team members. CCPN develops new state-of-the-art tools in NMR data analysis and NMR structural biology. The many national- and international partners of CCPN jointly cover all aspects of biomolecular NMR.
Profile of a successful candidate
A successful candidate will have a background in Chemistry/Biological Sciences/Bioinformatics or optionally Computer science with a strong biological interest. He/she has a strong interest and preferably experience in programming (python). The candidate is a good team player with excellent communication skills.
The project offers an exciting opportunity to work at the interface of biological-, medical- and computer-sciences. It concerns technology development for a fascinating approach towards the detection, monitoring and controlled studies of disease at a molecular level that will provide for key information in the development of personalized health care.
Application: https://le.ac.uk/study/research-degrees/funded-opportunities/liscb-vuister
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