|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||From £17,688 UK tuition fees, as well as a Stipend matching UK Research Council National per annum|
|Placed On:||20th September 2022|
|Closes:||2nd November 2022|
The focus of this data science PhD project is understanding which neural cell types are affected by the genetic and epigenetic variation associated with brain disorders. Genome-wide association studies (GWAS) have facilitated the identification of thousands of genetic variants associated with neurodegenerative and neurodevelopmental disorders. However, for the vast majority of genetic associations it is unclear how, and in what cell-type, they exert their effect. A fundamental conclusion is that many genetic risk factors mediate their effects by influencing the regulation of gene expression. As a consequence, there is a need to generate epigenomic profiles to annotate gene regulatory states across the genome in individual cell types. Such profiling across a range of brain cell types is both time-consuming and expensive, prohibiting analysis at scale. Yet, there is an abundance of data from profiling of bulk brain tissue where gene regulatory state is surveyed across the population of cell types.
This project will assess the extent to which we can use innovative mathematical methodologies to infer the constituent cell-level epigenomic profiles from these existing data profiled from bulk brain tissue. The proposed project consists of 3 objectives designed to provide the student with experience of a range of bioinformatics tools and genomic data types. Critically, we have matched epigenetic data from prefrontal cortex and three constituent neural cell types from the same individual permitting characterisation of the accuracy of the described computational methods. We have epigenomic data available from a range of experiments (DNA (hydroxy)methylation, ATAC-Seq, ChIP-Seq) and technologies (microarray, Illumina short read, Oxford Nanopore long read sequencing, 10x single cell RNA-Seq and ATAC-Seq), which the student can choose to incorporate into their project in order to tailor their research experience to their own personal objectives. In addition, the student would be expected to take ownership of the specific phenotype(s) we prioritise and identify the datasets for analysis. Specific objectives:
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