Computational genomics for prokaryotes : novel applications

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The use of DNA microarray-based transcription profiling techniques is providing large quantities of genome-wide data on gene-expression patterns. This transcriptome data provides researchers with alternative ways to address and interpret biological questions in a functional and regulatory context. Although its application is ubiquitous in various areas of research, the analysis of the data that result from these experiments still remain a challenge.

The main research theme of this thesis is the development of analytical software approaches and tools to analyze, visualize and exploit the information contained in global gene expression data. We have addressed the analysis of trancriptome data using various applications that were developed in-house; ranging from functional enrichment analyses, identification and visualization of regulatory elements, annotation of clustering results and the inference of putative gene functions using gene expression data.

The last experimental chapter describes the results of the analysis of a large time-resolved transcriptome dataset (40 timepoints) of the model bacterium B. subtilis using several of the earlier described tools. The most remarkable outcome of our analysis is the occurrence of stress during the transitions between the different growth phases. These stress responses are not induced by added external stimuli of artificially induced starvation conditions, but are naturally occurring during the growth phases of B. subtilis on rich medium.

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