Functional annotation and dendrogram representation of gene expression clustering results

The advances in genomic sciences have created vast amounts of gene expression data. To make sense of the expression information, various techniques have been applied. Clustering is among the unsupervised methods used to group the results according to gene expression level. Dendrogram visualization allows graphical representation of the clustering. The aim of this thesis is to enhance these techniques by adding another layer of functionality, namely, annotating the dendrogram with gene functional information. Presented is an application which visualizes yeast clustering results as a dendrogram along with color-coded gene keyword annotations. Gene keyword information was extracted from a major biological database and was used to create a database which was queried by the program according to the user preferences. Functional annotation with keyword information will help the biologists to integrate the different type of visual information quickly and provide an intuitive way of correlating the gene expression results with gene function.