PhD Studentship: Integrating transcriptomic and genomic datasets to investigate human tuberculosis

Project Title:  Integrating transcriptomic and genomic datasets to investigate human tuberculosis

Lead Institute / Faculty: Medicine

Main Supervisor:  Dr Michaela Reichmann

Other members of the supervisory team: Professor Paul Elkington, Professor Sarah Ennis and Dr Guo Cheng

Start date:  25/09/2023

Project description:

This project will develop a novel bioinformatic approach to integrate currently emerging transcriptomic datasets with historical genomic datasets to study human infection, using tuberculosis as an exemplar.  The student will develop expertise in Next Generation Sequencing analysis to build a multi-omic pipeline which can be applied to other human diseases.

The field of translational bioinformatics is rapidly advancing with Next Generation Sequencing (NGS)  generating vast transcriptomic datasets that provide unprecedented insight into human disease.  However, a major challenge is analysing the wealth of information to identify potential biological targets with clinical relevance.  Highly valuable datasets based on previous methods such as genomic studies are often overlooked, and approaches to integrate the latest datasets with earlier analytical methods remain underdeveloped. 

To address this, the project will develop a new multi-omic pipeline, integrating the latest published transcriptomic datasets and utilising historical genomic studies to validate the finding, with the aim of finding new therapeutic targets in human disease.  Tuberculosis is an ideal exemplar as there are a wealth of historical and emerging datasets, and is a globally important pandemic, killing more humans than any pathogen after SARS-CoV-2.  Furthermore, the immunopathology of tuberculosis is poorly understood, with an absence of new therapies for several decades. 

The identified biological targets identified through this bioinformatic approach will be investigated using Southampton’s advanced cell culture model, with the potential to translate to in vivo studies.  This project will develop expertise in both transcriptomic and genomic analysis, by training in relevant tools such as R programming language and Python, to gain a unique skillset across bioinformatic disciplines that can provide mechanistic and diagnostic insight into disease processes.  They will also gain experience in advanced 3-dimensional cell culture.  This multi-omic approach can be applied to any human disease and therefore has huge potential to impact across bioscience.

The successful candidate is likely to have the following qualifications:

• A 1^st or 2:1 degree in a relevant discipline and/or second degree with a related Masters in a biological, medical, mathematical or computer science sphere

Funding information:
Due to funding restrictions this position is only open to UK applicants.  This PhD studentship is funded from the Southampton NIHR Biomedical Research Centre for four years of stipend at Research Council rate of £18,154.00 including Research Training Support Grant plus fees at UK residency rate up to and including nominal registration.

Administrative contact and how to apply:

Please complete the University's online application form, which you can find at:

https://student-selfservice.soton.ac.uk/BNNRPROD/bzsksrch.P_Login?pos=7201&majr=7201&term=202324

You should enter Dr Michaela Reichmann as your proposed supervisor. To support your application provide an academic CV (including contact details of two referees), official academic transcripts and a personal statement (outlining your suitability for the studentship, what you hope to achieve from the PhD and your research experience to date).

Informal enquiries relating to the project or candidate suitability should be directed to Michaela Reichmann (m.reichmann@soton.ac.uk) and Professor Paul Elkington (p.elkington@soton.ac.uk).

Closing date:  21/04/2023

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