Bioinformatics of eukaryotic gene regulation

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