Bioinformatics of eukaryotic gene regulation

Abstract

Understanding the mechanisms which control gene expression is one of the fundamental problems of molecular biology. Detailed experimental studies of regulation are laborious due to the complex and combinatorial nature of interactions among involved molecules. Therefore, computational techniques are used to suggest candidate mechanisms for further investigation. This thesis presents three methods improving the predictions of regulation of gene transcription. The first approach finds binding sites recognized by a transcription factor based on statistical over-representation of short motifs in a set of promoter sequences. A succesful application of this method to several gene families of yeast Saccharomyces cerevisiae is shown. More advanced techniques are needed for the analysis of gene regulation in higher eukaryotes. Hundreds of profiles recognized by transcription factors are provided by libraries. Dependencies between them result in multiple predictions of the same binding sites which need later to be filtered out. Therefore, the second method presented here offers a way to reduce the number of profiles by identifying similarities between them. Still, the complex nature of interaction between transcription factors makes reliable predictions of binding sites difficult. Exploiting independent sources of information reduces the false predictions rate. The third method described here proposes a novel approach associating gene annotations with regulation of multiple transcription factors and binding sites recognized by them. The utility of the method is demonstrated on several well-known sets of transcription factors. Although the regulation of transcription is the major cellular mechanism of controlling gene expression, RNA interference provides a way of efficient down-regulation of specific genes in experiments. Difficulties in predicting efficient siRNA sequences motivated the development of a library containing siRNA sequences and related experimental details described in the literature. This library, presented in details in the last chapter, is publicly available at http://www.human-sirna-database.net.