Integration of health informatics: ‘big data’ for clinical translation in paediatric inflammatory bowel disease
Lead Institute / Faculty: Faculty of Medicine
Main Supervisor: Sarah Ennis
Other members of the supervisory team: Ben Macarthur, R Mark Beattie, Enrico Mossotto
Duration of the award: 36 months full time
This project will involve the analysis of ‘big data’ in a cohort of children diagnosed with autoimmune disease. The supervisory group have access to one of the largest and best clinically characterised cohorts of children diagnosed and recruited through close partnership with Southampton Children’s Hospital . The project utilises detailed clinical characteristics for these paediatric patients in addition to ‘omic research data. Genomic data represent one of the central data types for analysis. We have applied next generation sequencing to sequence the whole exomes of these children in addition to generating transcriptomic, microbiome and metabolomic data. We have existing pipelines for processing these data and have applied methods to gather and integrate these diverse data types and apply machine learning methodology to better understand the molecular biology of disease. By using these next generation health data in this way, we aim to distinguish patient subgroups using new mathematical methods e.g. distinguish patients that present similarly in clinic by identifying those with disease caused by immune dysfunction versus those cause by a ‘leaky’ gut. Our goal is to better integrate and interpret complex data with a view to understanding what causes disease on a patient-by-patient basis. By working closely with the clinical teams, we always aim to bring our findings from bench back to bedside to help inform patient treatment.
This PhD studentship application aims to train a talented postgraduate in key areas of bio- and health informatics, genomics and mathematical modelling of ‘omics data. The student will: apply programming skills to manipulate and process raw data through relevant pipelines; learn and apply critical procedures for data curation and quality control; generate and collate key output data from genomics, transcriptomics, microbiome and metabolomics; work at the interface between health informatics and maths to harmonise and integrate data for the training and testing of mathematical models; interpret and translate key findings back to clinical research staff and the wider scientific community. This will be performed under the guidance of senior academics
experienced in genomic informatics and advanced mathematical modelling and an internationally recognised clinical expert in paediatric IBD - all of whom have experience of successful PhD supervision.
Person Specification: See link below
The successful candidate is expected to have either excellent qualifications in bioinformatics or a firm mathematical background, with a degree in a quantitative discipline such as mathematics, physical sciences, or computer sciences coupled with a strong interest in genetics and genomics of human disease. The project encompasses mainly “dry lab” experimentation and computational modelling but requires excellent communication skills to join a multidisciplinary team. Complementary skills in statistical analysis and computer programming will be a distinct advantage. A genuine excitement for, and interest in mathematical modelling of biological processes to address complex biomedical questions is essential for this project.
The successful candidate is likely to have the following qualifications:
• A 1stor 2:1 degree in a relevant discipline and/or second degree with a related Masters
Administrative contact and how to apply:
Please complete the University’s online application form, which you can find at
Please contact: You should enter Sarah Ennis ([Email Address Removed]) 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 Sarah Ennis ([Email Address Removed]).
Closing date: Friday 15th June
Interview date from: Friday 22nd June
3-year full-time PhD fully-funded to cover student fees and stipend (at standard RCUK rate).
This studentship is part funded by the Institute for Life Sciences and the NIHR Southampton Biomedical Research Centre PhD Studentship Schemes.