Expression Analysis

Genes carry information that is crucial for the synthesis of RNA and proteins. When a gene is transcribed, it is active; that is, corresponding RNA is synthesized and serves as template for the synthesis of a protein. Gene activity can vary importantly depending on multiple factors (e.g. cell type, stage of differentiation, nutrients, temperature).

Since the introduction of microarrays in the late 1990s they consistently gained wider interest by allowing scientists to answer new biological questions and spurring the development of new analysis approaches. Microarrays enable to simultaneously capture the activity of thousands of genes. Standard experimental setups aim to characterize and compare cellular responses to multiple factors (e.g. disease, nutrients, different cell types). To target the different questions, a range of different microarray platforms have been developed with characteristic advantages or special target areas such as the detection of alternatively spliced genes or expressed non-coding RNAs.

We offer assistance for all stages of microarray experiments ranging from the planning stage to data pre-processing and advanced network based bioinformatics analyses. Data preprocessing steps include normalization to render different measurements comparable, clustering to detect patterns of coexpressed genes, sample classification to identify diagnostic features or predict clinical outcomes and the determination of the best genes to differentiate between different cellular states or experimental conditions. We also develop and regularly apply advanced methods, for instance the utilization of prior knowledge in the form of publication abstracts or gene ontologies that help to interpret experimental results in the context of biological networks.

RNA levels in a cell are regulated by RNA synthesis and decay. State-of-the-art gene expression profiling typically only allows measurements of total transcript abundance. It cannot distinguish whether changes in total mRNA are due to alterations in de novo transcription or in decay. Recently Dölken et al. presented a novel approach for measuring both RNA synthesis and decay by labeling newly transcribed RNA and separating total cellular RNA into newly transcribed and unlabeled pre-existing RNA.

Calculation of RNA half-life

In the collaboration with Lars Dölken, we developed novel computational methods for the analysis of this new type of expresssion data and the calculation of transcript half-life. Based on the separation of total RNA into newly transcribed and pre-existing RNA and the analysis of all three RNA subsets, array data of the different RNA fractions can be normalized in an intuitive way by linear regression analysis. Furthermore, by analyzing newly transcribed RNA, the temporal order and kinetics of transcriptional regulation following a stimulus can by analyzed at the time scale of minutes. Thus, both fast and transient primary effects and slow and long-term secondary effects can be identified in a single experiment. 2008 Papers Lars Dölken, Zsolt Ruzsics, Bernd Rädle, Caroline C. Friedel, Ralf Zimmer, Jörg Mages, Reinhard Hoffmann, Paul Dickinson, Thorsten Forster, Peter Ghazal, Ulrich H. Koszinowski. High-resolution gene expression profiling for simultaneous kinetic parameter analysis of RNA synthesis and decay. RNA, vol 14, no. 9, pp. 1959–1972, Sep 2008. DOI BibTex

Gene regulation analysis based on RNA half-life

By a comparison of transcript half-lives, we identified highly conserved regulatory principles in human and mouse cells and predict so far unknown regulatory mechanisms for a large number of biological processes. In this way, we demonstrate that protein complexes are regulated fast and efficiently by fast transcript decay for key protein subunits. In addition, we show that differences in transcript half-lives between functionally highly similar proteins can be used to identify differential regulatory patterns and, thus, better characterize the function and regulation of these proteins. 2009 Papers Caroline C. Friedel, Lars Dölken, Zsolt Ruzsics, Ulrich Koszinowski, Ralf Zimmer. Conserved principles of mammalian transcriptional regulation revealed by RNA half-life. Nucleic Acids Research, vol 37, no. 17, pp. e115, Sep 2009. DOI BibTex

Extraktion of relationships between objects from the biomedical literature

ConceptMaker facilitates the analysis of networks and large scale measurements at the same time. The measurements such as microarray experiments (e.g. comparing the activity of genes between healthy and diseased individuals) enable the selection of relevant functional contexts. Metrics of differential expression and corresponding significance levels are harnessed to automatically construct queries and pose them against indexed data sources. ConceptMaker identifies biological contexts as sub-networks of mutually interconnected proteins, genes, chemical compounds and molecular biology keywords. Such contexts are not required to be specified beforehand (e.g. known pathways), but are generated by our approach implicitly. Besides measurements, ConceptMaker requires a data matrix of hundreds of thousands of keywords matched in millions of biomedical publications. Thereby, known and new contexts can be found for the available experimental data. The figure shows an example gene expression analysis performed by our ConceptMaker software. The html report shows the analysis results in the context of different network and data views including hyperlinks to many molecular biology resources on the web. 2005 Papers R. Küffner, Katrin Fundel, Ralf Zimmer. Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts. Bioinformatics, vol 21, no. Suppl 2, pp. 259-267, 2005. DOI BibTex