Morris, JA; (2012) A bioinformatics framework for management and analysis of high throughput CGH microarray projects. Doctoral thesis , UCL (University College London).

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Abstract

High throughput experimental techniques have revolutionised biological research; these techniques enable researchers, in an unbiased fashion to survey entire biological systems such as all the somatic mutations in a tumour in a single experiment. Due to the often complex informatics demands of these techniques, robust computational solutions are required to ensure high quality reproducible results are generated. The challenge of this thesis was to develop such a computational solution for the management and analysis of high throughput microarray Comparative Genomic Hybridisation (aCGH) projects. This task also provided an opportunity to test the hypothesis that agile software development approaches are well suited for bioinformatics projects and that formalised development practices produce better quality software. This is an important question as formalised software development practices have been underused so far in the eld of bioinformatics. This thesis describes the development and application of a bioinformatics framework for the management and analysis of microarray CGH projects. The framework includes: a Laboratory Information Management System (LIMS) that manages and records all aspects of microarray CGH experimentation; a set of easy to use visualisation tools for aCGH experimental data; and a suite of object oriented Perl modules providing a exible way to construct data pipelines quickly using the statistical programming language R for quality control, normalisation and analysis. In order to test the framework, it was successfully applied in the aCGH pro ling of 94 ovarian tumour samples. Subsequent analysis of these data identi ed 4 well supported genomic regions which appear to in uence patient survival. The evaluation of agile practices implemented in this thesis has demonstrated that they are well suited to the development of bioinformatics solutions as they enable developers to react to the changes of this rapidly evolving eld, to create successful software solutions such as the bioinformatics framework presented here.

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