Applied Bioinformatics in Saccharomyces cerevisiae: Data storage, integration and analysis

Doktorsavhandling, 2014

The massive amount of biological data has had a significant effect on the field of bioinformatics. This growth of data has not only lead to the growing number of biological databases but has also imposed the needs for additional and more sophisticated computational techniques to proficiently manage, store and retrieve these data, as well as to competently help gaining biological insights and contribute to novel discoveries. This thesis presents results from applying several bioinformatics approaches on yeast datasets. Three yeast databases were developed using different technologies. Each database emphasizes on a specific aspect. yApoptosis collects and structurally organizes vital information specifically for yeast cell death pathway, apoptosis. It includes predicted protein complexes and clustered motifs from the incorporation of apoptosis genes and interaction data. yStreX highlights exploitation of transcriptome data generated by studies of stress responses and ageing in yeast. It contains a compilation of results from gene expression analyses in different contexts making it an integrated resource to facilitate data query and data comparison between different experiments. A yeast data repository is a centralized database encompassing with multiple kinds of yeast data. The database is applied on a dedicated database system that was developed addressing data integration issue in managing heterogeneous datasets. Data analysis was performed in parallel using several methods and software packages such as Limma, Piano and metaMA. Particularly the gene expressions of chronologically ageing yeast were analyzed in the integrative fashion to gain a more thorough picture of the condition such as gene expression patterns, biological processes, transcriptional regulations, metabolic pathways and interactions of active components. This study demonstrates extensive applications of bioinformatics in the domains of data storage, data sharing, data integration and data analysis on various data from yeast S.cerevisiae in order to gain biological insights. Numerous methodologies and technologies were selectively applied in different contexts depended upon characteristics of the data and the goal of the specific biological question.