Structure function predictions of translational products from novel Open Reading Frames with Dr Sudhakaran Prabakaran, Department of Genetics
Applications are invited for a 4-year PhD studentship in the BBSRC-funded Cambridge Doctoral Training Programme (DTP), to work with Dr. Sudhakaran Prabakaran within the theme of World-Class Underpinning Bioscience.
Translation of protein and protein-like products have been observed from regions of the genome that are traditionally classified as noncoding. These regions include small Open Reading Frames (sORFs), orphan genes, De Novo genes, pseudogenes, and other yet unannotated genomic regions. The primary reason why these novel translational products have not yet been identified and systematically investigated in spite of the technological advancements in genomic and proteomics fields is because unlike translations from the canonical ORFs, translation from the noncanonical ORFs are not pervasive, they occur in specific biological conditions such as specific disease states and hence only under a systems proteogenomics framework that was developed in our lab that these novel translational products can be identified. Using this framework we have provided evidence for translation from noncanonical ORFs.
The goal of this project is to understand the biological functions of these protein-like products based on predicting structures using EV-fold, QUARK, CATHH, and other structural genomics approaches. The project involves:- 1. identifying translation products from novel ORFs from TCGA cancer datasets using the cloud interface that we have developed in the lab, 2. predicting the structures of these identified novel translational products using structural genomics algorithms such as QUARK, EV-fold, CATHH, 3. mapping specific cancer-associated variants to these regions and inferring their 'pathogenicities' using machine learning approaches and modelling their interactions with their binding partners.
The project is therefore very interdisciplinary in nature. The student will learn to perform systems proteogenomics, analyse big data in the cloud environment, and develop machine-learning tools.
All applications should be made online via the University's Applicant Portal using the Department of Genetics course code (BLGE22). Details of how to apply are on: https://www.graduate.study.cam.ac.uk/courses/directory/blgepdphg/apply Applicants should hold or be about to achieve a First or Upper-Second (2.i) class degree in a relevant subject. We encourage students with strong mathematical background either in engineering or physics degree.
Funding will cover the student's stipend at the current Research Council rate and University Fees for 48 months, subject to eligibility. The studentships are available to UK nationals and EU students who meet the UK residency requirements. Further information about eligibility for Research Council UK funding can be found on the BBSRC website (http://www.bbsrc.ac.uk/documents/studentship-eligibility-pdf/). Applications from ineligible candidates will not be considered.
For further information, please contact Dr Sudhakaran Prabakaran (email@example.com)
Please quote reference PC14748 on your application and in any correspondence about this vacancy.
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