Child Health: Biomarker Discovery

The aim of this PhD project is to identify a single prognostic biomarker signature of autism spectrum disorder (ASD) using computational biology

Exploring the genes, proteins and signalling pathways from the cord blood of 300 babies who went on to be diagnosed with ASD and 300 who did not, using specific software tools for each, in order to identify any abnormalities in the ASD cohort that could be indicative of an infant being diagnosed with ASD. The data will be used as training material for machine learning tools in order to define a single biomarker signature to be used as a predictive tests for ASD in neonates.

The HRB award to Dr English at INFANT will generate valuable proteomic, transcriptomic, and metabolomic datasets. Briefly, the study will assess neonatal cord blood in international birth cohorts, with known ASD outcomes (n=300 ASD vs 300 Controls) to identify predictive blood-based biomarkers. Multi-omics data integration is one of the major challenges in the era of precision medicine and warrants investment.

Computational biology is the core component of this work and together the supervisor team has extensive experience in generation and analyses of clinical omics-datasets. As per Gantt chart the key objectives are;

  1. Training in the various bioinformatics software platforms for proteomic, metabolomic, and transcriptomic data analysis (MaxQuant, Skyline, MapDIA, Progenesis, etc.) as well as using multivariate statistical analysis of omics datasets to identify differentially expressed molecules (i.e. R/R Studio).
  2. Under the supervision of Dr Lopez the student will explore the proteomic data to provide protein-level evidence of gene expression which will refine the gene models and identify convergent molecular pathways.
  3. The student will explore a range of software tools to identify key biological processes dysregulated at a functional level (Ingenuity Pathway Analysis, STRING, Gene Ontology, etc.)
  4. Under the supervision of Dr Mooney the student will test various supervised machine learning methods (e.g. Random Forest, Neural Networks including Deep Learning) to integrate the multiple data types into a single biomarker signature.

The identification of one convergent dysregulated molecular pathway or a robust biomarker signature of ASD would be a major advancement in our understanding of the disorder at a systems level.

pregnant lady sitting

How To Apply

Application Process: Candidates applying for this studentship must have a first or second class degree or Masters in a relevant discipline. Previous experience in a health-related discipline is expected and relevant experience is welcomed.

A stipend of 18,500 euro per annum and a cover for registration fees will be available for the successful applicant (non-EU members may apply but additional fees are paid by the candidate).  The position is funded for 4 years with an allowance for travel and publication costs. 

The successful applicant will be asked to proceed through the Postgraduate Applications Centre (PAC, http://www.pac.ie) for formal registration.

Interested candidates should submit:

  1. a covering letter detailing their interest and reasons for applying for this position 
  2. (a CV (including the name and contact details of 2 referees)
  3. a summary of their relevant research experience to date

 

The above documents should be emailed to infantjobs@ucc.ie on or before the closing date of 11 July.

Please reference ‘PhD Studentship Biomarker Study’ on the subject line of your application.

The successful applicant is expected to commence the post by 1 October 2018. 

Further Enquiries: please contact Dr Jane English  j.english@ucc.ie

 

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