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Tim Kehl

Wilms tumor (WT) is the most common renal tumor in childhood. We and others have previously identified oncogenic driver mutations affecting the microprocessor genes DROSHA and DGCR8 that lead to altered miRNA expression patterns. In the case of DGCR8, a single recurrent hotspot mutation (E518K) was found in the RNA binding domain. To functionally assess this mutation in vitro, we generated mouse Dgcr8-KO embryonic stem cell (mESC) lines with an inducible expression of wild-type or mutant DGCR8, mirroring the hemizygous mutant expression seen in WT.
Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles.
Motivation A major goal of personalized medicine in oncology is the optimization of treatment strategies given measurements of the genetic and molecular profiles of cancer cells. To further our knowledge on drug sensitivity, machine learning techniques are commonly applied to cancer cell line panels. Results We present a novel integer linear programming formulation, called MEthod for Rule Identification with multi-omics DAta (MERIDA), for predicting the drug sensitivity of cancer cells.

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