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Applications are invited for a student to work on a fully funded research project in Collaborative Research Tools for Metabolomic Data Analyses, supervised by Fera.
A large-scale project to speed up the development of drought and disease resistant crops is being led by the Food and Environment Research Agency (Fera) in collaboration with European partners. The approach will be developed using a clover-like plant as a model. Under laboratory conditions, hundreds of these plants will be subjected to drought and/or infection with a type of soil fungus called Fusarium. The information obtained from studying the model plant will then be applied to breeding new pea varieties. These new varieties will be compared with existing commercial crops, identifying those that perform better when challenged with a combination of Fusarium and drought. The best of the plants will undergo field trials at different sites across Europe.
The student will be required to develop a system to support the computational analysis of very large multivariate datasets obtained from High Resolution Nuclear Magnetic Resonance (HR-NMR) spectroscopy and High Resolution Liquid Chromatography Mass Spectrometry (HR-LC-MS). These will be used to record broad ranging metabolite profiles from model (Medicago truncatula) and crop (Pisum sativum) plants.
These profiles must be analysed using appropriate data mining tools to identify, for example, potential biomarkers of Fusarium resistance. Standard methods for multivariate analysis, such as Principal Components Regression (PCR) and Partial Least Squares Linear Discriminant Analysis (PLS-LDA) will be developed to identify key metabolites as well as less well-used techniques such as Genetic Programming and, in particular, Neural networks. New bio- and chemo-informatics tools must be developed to combine the data from different analytical techniques.
The aim is to identify the processes associated with the way drought and disease combine and will require search of multiple databases to assign discriminatory variables to the compounds responsible.
At present, different chemical techniques are associated with different databases. There is no system to combine information from different techniques.
In addition to the fusion of data from multiple metabolomic techniques, genomic data will also be available, providing the possibility to integrate information from different –omic technologies in order to identify novel genes and link these to the biochemical pathways that improve plant resistance to drought and disease.
The project will require the design of a unified database that will provide access to information from multiple analytical methods and therefore facilitate compound identification. The student will develop a graphical user interface that will allow various data pre-processing steps (e.g. binning and denoising) as well as uni-variate (t-tests, ANOVA) and multivariate (PCA, PLS, etc) methods to be implemented interactively.
There is a particular challenge in the development and deployment of this system in the context of the large (pan-European) project. One possible platform for providing secure sharing of large datasets and workflow management is the youShare system. Ultimately, the project aims to use the system to identify novel genes and biochemical pathways that improve plant resistance to these stresses of drought and disease.
Funding Notes:
The successful applicant will receive fees and a tax-free stipend from the Engineering and Physical Sciences Council (EPSRC) of £16,746 p.a. Please note there are eligibility requirements – see
http://www.epsrc.ac.uk/funding/students/pages/eligibility.aspx
Additional support to cover travel to conferences and lectures will also be available.
The research will be carried out in conjunction with studying for an Engineering Doctorate at the University of York. The Programme is a full time, 4-year doctoral level research degree involving both a taught and research component.
References:
Applicants should be highly motivated and have a minimum of an upper second-class honours degree in Computer Science or related discipline (e.g. Maths with Computing, Electrical Engineering). The ideal candidate will have significant programming experience and practical knowledge of data analysis/machine learning techniques. Applicants with experience with databases or interface design are particularly welcome to apply.
Research Assessment Exercise (RAE) 2008 Results