Microarray data analysis of M.tuberculosis whole genome using Genesis » książka
Microarray data analysis of M.tuberculosis whole genome using Genesis
ISBN-13: 9783659185168 / Angielski / Miękka / 2012 / 56 str.
DNA microarrays are a powerful tool to investigate differential gene expression for thousands of genes simultaneously. Although DNA microarrays have been widely used to understand the critical events underlying growth, development, homeostasis, behavior and the onset of disease, the management of the resulting data has received little attention. Presently, the fluorescent dyes Cy3 and Cy5 are most often used to prepare labeled cDNA for microarray hybridizations. Raw microarray data are image files that have to be transformed into gene expression formats - a process that requires data manipulation due to systematic variations which may be attributed to differences in the physical and chemical dye characteristics. There are different approaches to analyse the large-scale gene expression data in which the essence is to identify gene clusters. This approach has allowed us to determine expression profiles of novel developmentally regulated genes. Finally we get some genes which highly coexpressed and may be involved in pathogenesis.
DNA microarrays are a powerful tool to investigate differential gene expression for thousands of genes simultaneously. Although DNA microarrays have been widely used to understand the critical events underlying growth, development, homeostasis, behavior and the onset of disease, the management of the resulting data has received little attention. Presently, the fluorescent dyes Cy3 and Cy5 are most often used to prepare labeled cDNA for microarray hybridizations. Raw microarray data are image files that have to be transformed into gene expression formats - a process that requires data manipulation due to systematic variations which may be attributed to differences in the physical and chemical dye characteristics. There are different approaches to analyse the large-scale gene expression data in which the essence is to identify gene clusters. This approach has allowed us to determine expression profiles of novel developmentally regulated genes. Finally we get some genes which highly coexpressed and may be involved in pathogenesis.
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