[PhD Studentship with 3 year's UK/EU fees at the University of St Andrews for which Bioconductor experience is advantageous.]
Approximately one in eight men will be diagnosed with prostate cancer (PCa) in his life, while a substantially higher proportion of men would show evidence of PCa under histopathological examination. For many, the disease will cause no symptoms and the burden of treatment on such cases would be entirely disproportionate. There is therefore a long-standing interest in methods to sub-classify PCa cases, with RNA expression being one of the most commonly used tools in the literature.
RNA expression profiles of prostate cancer have been used to develop diagnostic and prognostic signatures, as well as to make inference about characteristics such as cell-type mixture levels, BMI, glycosylation activity etc. However many of these signatures are empirically derived, and their individual components are difficult to interpret – all the more so since messenger RNA (mRNA) expression and protein expression profiles are typically not highly correlated.
As well as mRNA and protein expression, we have several other expression assays available to us. Small RNA-seq allows us to profile several classes of non-coding RNA that contribute to the regulation of transcription and translation, SHAPE-seq and Ribo-seq are less-commonly used technologies that inform about translation of mRNA to protein (reporting on RNA conformation and Ribosomal occupancy respectively).
Several large-scale sequencing studies have profiled mRNA and small RNA expression in prostate cancer including The Cancer Genome Atlas (TCGA) and several International Cancer Genome Consortium (ICGC) projects. This project is affiliated to the UK ICGC Prostate project headed by Professor Ros Eeles at the Institute of Cancer Research and additionally involving PIs in Cambridge, Norwich, Oxford, St Andrews and Tampere.
This project is a mixture of methodology development and data analysis, and will focus on interpreting and integrating these different types of expression data. This will include a first analyses of some data-types in prostate cancer, while contributing to the primary analysis of small RNA-seq data for the ICGC project, and development and application of methods for integrating all of these expression data-types to gain insight into the biology of the disease. There will also be investigation of existing RNA expression signatures to understand how they translate to protein expression and thus how they should be interpreted. Additional data for these PCa cases, including clinical and pathological data will be available
Applications must have graduated from a numerate discipline (e.g. bioinformatics, statistics, mathematics, or computer science). The ideal candidate will have an interest in genomics and an enthusiasm for learning about biology. A Masters degree in statistics, computational biology, or similar would be desirable. Some experience of working with genetic/genomic data would be desirable but not essential. Experience of coding and scripting in R (and ideally Bioconductor) is desirable.
For further details on the project and informal enquiries please contact Prof. Andy Lynch (email@example.com) with a CV and a covering letter. Start date: 2018.