Funded PhD Opportunity Integrating whole genome sequencing (WGS), transcriptomics, proteomics, and patient phenotypic data to identify predictive biomakers for rheumatoid arthritis biologic treatments

This opportunity is now closed.

Subject: Biomedical Sciences


Rheumatoid arthritis (RA) is a chronic destructive disease of joints, which affects ~ 25 million adults in the world. As a painful and disabling disease RA presents significant healthcare burden to society. The standard treatment route of RA starts with traditional DMARDs (Disease-modifying anti-rheumatic drugs), such as Methotrexate, to which 30-40% of patients would not respond. Those non-responders will undergo anti-TNFa based biologic treatments, but again 30-40% of them would not respond to TNFa biologics and they are referred to as secondary non-responders. While we conduct related research to identify potential drugs for the secondary non-responders, it would be highly desirable to be able to accurately predict using effective biomarkers which patients are secondary non-responder, in order to triage high cost of biologic drugs.

In this project, we are exploiting gene expression data from public databases,  WGS and proteomics data from our own patient cohort, together with patient demographic and clinical phenotypic data, to develop integrative bioinformatics analysis pipelines with  the aim is to identify high quality genomic biomarkers of secondary non-responder among RA patients.

The overall aim of the project  will be achieved through the following steps:

1)We have identify a number of suitable datasets containing the gene expression profiles of secondary non-responders and responders; differential expression analysis on the transcript level will allows us to identify genes that are strongly associated with biologic drug response.

2)We have conducted proteomics analysis using olink technologies to profile our patient cohort with selected panels of proteins related to immune response, inflammation etc. Bioinformatics analyses of the proteomics data and patients’ secondary response status data will identify candidate proteins that are strongly associated with biologics response.

3)All the patients in our cohort will have their whole genome sequenced as a research collaboration with Genomics Medicine Ireland. This provides a rich source of information on the full genetic makeup of all our patients. We will develop and test robust bioinformatics pipelines for the processing and analysis of WGS data.

4)Integrating the three streams of data as described above and patients drug response data, we will develop and test an integrative bioinformatics approach to identifying highly promising genomic biomarkers that are predictive of RA patients’ response to biologic treatments. The integration of all three types of omics data is essential as it helps to narrow down the search space of WGS, increase confidence, and converge to the most effective genomics biomarkers for biologic drug response.

5)We will perform validations on the predictive biomarkers with an independent public dataset and/or new cohort of RA patients.

This is primarily a Bioinformatics and BigData research project, with data obtained from both public domain and our own centre; it requires an individual with good computational skills and statistical knowledge.  A good understanding of basic biological processes and some experience with basic biological lab experiments would be desirable, but training on these aspects will be provided during the course of PhD study.

Essential Criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)
  • Sound understanding of subject area as evidenced by a comprehensive research proposal
  • A comprehensive and articulate personal statement

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Completion of Masters at a level equivalent to commendation or distinction at Ulster
  • Research project completion within taught Masters degree or MRES
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed
  • Experience of presentation of research findings


    Vice Chancellors Research Scholarships (VCRS)

    The scholarships will cover tuition fees and a maintenance award of £15,009 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.


    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,009 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided).  For Non EU nationals the candidate must be "settled" in the UK.

Other information

The Doctoral College at Ulster University

Launch of the Doctoral College

Current PhD researchers and an alumnus shared their experiences, career development and the social impact of their work at the launch of the Doctoral College at Ulster University.

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My experience has been great and the people that I have worked with have been amazing

Kieran O'Donnell - 3D printing of biological cells for tissue engineering applications

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Completing the MRes provided me with a lot of different skills, particularly in research methods and lab skills.

Michelle Clements Clements - MRes - Life and Health Sciences

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Throughout my PhD I’ve been provided with continuous support and guidance by my supervisors and the staff at the University.I’ve also received many opportunities to further enhance my professional development in the form of teaching experience and presenting my work at conferences which will aid in my pursuit of a career in academia or industry.

William Crowe

Key Dates

Submission Deadline
Monday 19 February 2018
Interview Date
6, 7 and 8 March 2018

Contact Supervisor

Dr Shu-Dong Zhang

Other Supervisors

Apply online

Visit and quote reference number #238234 when applying for this PhD opportunity