Computational analyses of transcriptome regulation on RNA level

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My Bioinformatics group investigates the transcriptome of organisms such as the human, and how it is regulated. The transcriptome corresponds to the set of genes that are activated in a given cell. It consists of so-called transcripts, linear RNA molecules that either encode a protein product or function as so-called RNA genes. Both classes of molecules, i.e. proteins and RNA genes, are key to determining the state of a cell. In contrast to proteins and their interactions, comparatively little is known about the transcriptome and how it is regulated. We already know of specific mechanisms of regulation that play key functional roles (e.g. so-called miRNA-mRNA interactions, Nobel prize in 2006) and that contribute to the complexity of organisms such as the human (so-called alternative-splicing), yet we currently lack both a comprehensive and a detailed view. To address this, my Bioinformatics group develops new computational methods that will allow us to investigate the role that RNA structural features (i.e. interactions of a transcript with itself) and RNA-protein interactions play in regulating the transcriptome. A special goal of our work is to capture features that are likely to be relevant in the living cell and to detect regulatory features in transcripts that encode proteins. Our research has the potential to yield computational prediction methods that will allow us (1) to detect yet unknown mechanisms of transcriptome regulation and (2) to significantly increase our current understanding of known regulatory processes such as RNA-editing, alternative splicing, translation initiation, transcript localization and degradation.