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This thesis is a comparative study of gene arrangements in Pseudomonas species, and is organized into three major sections. The first section compares gene arrangements for different pathways in Pseudomonas aeruginosa PAO1 to determine if the gene arrangements are similar to previous studies. It also serves as a reference for pyrimidine gene arrangements in P. aeruginosa. The second part compares the physical, and genetic maps of P. aeruginosa PAO1 with the genome sequence. The final section compares pyrimidine gene arrangements in three species of Pseudomonas. Pyrimidine biosynthesis and salvage genes will be aligned for P. aeruginosa PAO1, P. putida KT2440, and P. syringae DC3000. The whole study will gives insight into gene patterns in Pseudomonas, with a focus on pyrimidine genes.

The genetic basis of the different cancer phenotypes has been a continuous and accelerating subject of investigation. Data accumulated thanks to recently introduced genome-wide scanning technologies have revealed that human diversity and diseases susceptibility is also greatly influenced by structural alterations in the human genome, such as DNA copy number variants (CNVs) and copy number alterations (CNAs), which influence gene expression in both healthy and pathological cells. Our research aims to investigate the influence of structural alterations on gene expression in cancer cells using SNP microarray data. Specifically, we focus on analyzing DNA copy number alternations (CNAs), which can significantly influence gene expression in cancer cells. Several cancer-predisposing mutations affect genes that are responsible for maintaining the integrity of the chromosomes during cell division, which can result in translocations, gains or losses of large parts of chromosome. To our knowledge, there have been no publications that link whole-genome copy number alterations in cancer to gene expression variations using the full range of possibilities offered by SNP arrays. The accurate use of SNP arrays in the analysis of cancer has been difficult due to tumour purity, tumour heterogeneity, aneuploidy/polyploidy and complex patterns of CNA and loss-of-heterozygosity (LOH). In our work, we use and further extend a recently developed novel tool for tumour genome profiling called OncoSNP (Yau, Mouradov et al. 2010), in order to resolve some of those problems and accurately estimate copy number alterations (CNA) and loss-of-heterozygosity (LOH) from SNP array data in cancer cell samples. The methods developed in this thesis tackle the problem of cancer genomic investigation by developing and validating an extension (DPS smoothing) of a new method (OncoSNP). This approach is used in the analysis of global expression versus CNA patterns in experimental systems and large clinical datasets. We analyse various cancer SNP and gene expression arrays of increasing complexity and heterogeneity, starting with a dataset of head and neck squamous cell carcinoma (HNSCC) cell lines, followed by leukaemia samples and finally a large breast cancer dataset. The central findings of our research are multifold. In the HNSCC dataset we find that the level of genetic instability is not indicative of the pathological state; i.e. there are premalignant lesions displaying extensive mutations. However some genetic features are typical of certain lesion type; e.g. we consistently observe copy loss in the short arm of chromosome 3 in carcinoma. The pattern of homozygous deletion in the dataset reveals common deletion of cancer related genes, especially CDK4 (pI6). Furthermore we notice a significant positive correlation between the copy number and the expression on a systematic level. In Leukaemia, we do not observe extended uniparental disomy as previously published (Akagi, Shih et al. 2009) and expected. However large alterations (whole arm amplification) are observed in individual patients: copy loss in chromosome 7 (2 patients), copy gain in chromosome 8 (3 patients) as well as common alterations around the centromeres and telomeres. In the breast cancer dataset significantly different level of mutations were observed in the different subtypes in the cohort. Furthermore 499 genes were identified with significant correlation between their gene expression (GE) and underlying genomic alterations (either copy number (CN) or loss-of-heterozygosity (LOH)). Performing hierarchical clustering on the cohort using the 499 correlated genes enabled us to recover the subtypes' separation previously based on gene expression alone.