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Title: Functional genomics and bioinformatics protocols for the elucidation of pain
Author: Perkins, J. R.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
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Microarray technologies enable us to profile the expression of thousands of gene transcripts within a given cell or tissue. Within pain research they have been used extensively to search for genes that change in expression as a result of the induction of a clinically-relevant pain state, often using an animal model of pain. Studying these genes has led to improvements in our understanding of the genes, pathways and other biological processes involved in pain. These themes are explored further in the first (introductory) chapter of this thesis. These experiments result in large numbers of genes declared differentially expressed between samples, many of which are not directly involved in pain. There is often little overlap of these genes between different pain models. The second chapter of this thesis is concerned with the use of systems biology methods to prioritise these genes based on their likelihood of being pain-related. In the third chapter a web-based software application is described. It allows a pain researcher to combine data from various pain-related microarray experiments with other data sources in order to build their own pain networks. Exemplary usage scenarios are presented. The fourth chapter describes a comparison between microarrays and a new technology, RNA-seq, which uses next generation sequencing technology to quantify the RNA present within a tissue. Samples obtained using a well characterised animal pain model, spinal nerve transection, are used for this purpose. In the fifth chapter the effects of RNA-seq sequencing depth on the detection of differentially expressed genes and the discovery of novel transcribed regions of the genome are investigated. In keeping with the theme of gene expression profiling using animal models of pain, the sixth chapter of this thesis reports a software package for the analysis of high-throughput RT-qPCR data and presents an experiment in which this package was used to analyse cytokine expression.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available