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Title: Bioinformatic analysis of peptide microarray immunoassay data for serological diagnosis of infectious diseases
Author: Nambiar, Kate
ISNI:       0000 0004 7427 061X
Awarding Body: University of Brighton
Current Institution: University of Brighton
Date of Award: 2017
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Understanding antibody - antigen interactions occurring in infectious diseases is important in understanding aetiology, can help facilitate diagnosis, and could offer potential targets for vaccine or therapeutic antibody development. Peptide arrays – collections of short peptides immobilised on solid planar supports – offer a high throughput and highly parallel method of identifying immunogenic epitopes and relating patterns of antibody identification to clinical disease states. As technology advances, so the density and complexity of peptide arrays of becomes ever higher. Managing the large volume of data that modern high density microarrays generate requires sophisticated bioinformatics in order to minimise errors and biases. In this thesis I introduce a new software package, pmpa, that uses R, the open source statistical programming platform and an object orientated framework from the Bioconductor project. The package facilitates analysis of peptide microarray data including functions for reading scanned data files, quality assessment and pre-processing. It is both flexible and modular – integrating with existing software in the Bioconductor repository. Data pre-processing is key to any microarray analysis. Noise due to technical variation can obscure true biological effects if careful steps are not taken. The aim of pre-processing is to minimise noise while preserving biological variation. No consensus exists as to the optimal method of pre-processing making comparison between studies difficult. This thesis explores two key aspects of pre-processing: background correction and normalisation using two experimental datasets – a titration series of a monoclonal anti C.difficle Toxin B monoclonal antibody, and dataset with an anti-Toxin A antibody spiked into non immune sera to examine biases introduced by the pre-processing and whether they improve measures such as precision and differential identification. Finally the analysis method is applied to two studies identifying antibody signatures in infectious diseases: the first investigating immune responses to C. difficile – a major hospital acquired infection and the leading identifiable cause of antibiotic associated diarrhoea, and the second characterising antibody signatures that define paediatric tuberculosis infection. The real world application of the methodology identifies signatures of immune responses characterising clinical disease eg. relapsing vs. single episode C. difficile infection, but also highlights a number of limitations of the technique such as batch confounding and response variability.
Supervisor: Llewelyn, Martin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available