Junior Research Group Computational Genome Biology

Dr. Bernd Fischer

Cells are automatically segmented in images from a high-throughput screen. Nucleus area, eccentricity, fluorescence intensity and other morphological and textural features are extracted for each cell and summarized per experiment. Epistatic relationships between genes are inferred by statistical and machine learning methods using the extracted features.
© dkfz.de

At the German Cancer Research Center we are mourning the loss of our colleague Bernd Fischer who died much too early and unexpectedly on February 22, 2017. We have lost an outstanding scientist and esteemed colleague.

Our group uses computational statistics and machine learning methods to understand and predict genotype to phenotype relationships. Our aim is to derive systems-level maps of interactions between genes and proteins, in order to model and predict the functional consequences of sequence variations in the human population and in cancer. With the tremendous increase in biological data recorded with current high-throughput techniques, it gets more and more challenging to mine and interpret the available data. Our contribution is the development of computational statistics for high-dimensional data, including feature selection tools, differential testing and network modeling. One research focus is the modeling of genetic and physical interactions. Genetic interactions describe how genetic perturbations influence each other, e.g. with respect to their cellular phenotypes. We have developed methods for quality control, normalization and statistical analysis of image-based genetic interaction screens. To better understand molecular mechanisms, it is also important to study physical interactions between molecules. We have developed analysis methods for protein quantification by mass spectrometry data. With this and downstream bioinformatics workflows, we have contributed to obtaining a better understanding of RNA-binding proteins. The original software packages are made publicly available as open source, mainly as a contribution to the Bioconductor project.

We will develop computational and statistical methods required for the analysis of emerging, cutting-edge data, especially from high-throughput functional and genetic assays, and advanced applications of spectrometry. Specifically, the computational research in proteomics will focus on integrating individual genome sequences into the analysis of proteomic mass spectrometry data. Furthermore, we will develop the computational methods needed to understand how gene products and their mutations are interacting with each other physically and functionally, and how genetic and environmental variables combinatorially result in a given phenotype, including disease. We will develop computational methods that aim to predict the phenotypic outcome of drug treatments and combinations of drugs under a known genetic background. For this, we will combine observational population data with high-throughput perturbation screens. The computational methods will be instrumental for cancer proteomics in basic and biomarker research and for the construction of predictive, translational platforms for the next generation of individualized, genome-informed drug therapies.


Dr. Bernd Fischer
Computational Genome Biology (B210)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 580
69120 Heidelberg

Selected Publications

  • Horn, T., Sandmann, T., Fischer, B., Axelsson, E., Huber, W., & Boutros, M. (2011). Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nature Methods, 8, 341-6
  • Castello, A., Fischer, B., Eichelbaum, K., Horos, R., Beckmann, B.M., Strein, C., Davey, N.E., Humphreys, D.T., Preiss, T., Steinmetz, L.M., Krijgsveld, J. & Hentze, M.W. (2012). Insights into RNA Biology from an Atlas of Mammalian mRNA-Binding Proteins. Cell, 149, 1393-1406
  • Fischer B. et al. (2015). A map of directional genetic interactions in a metazoan cell. eLife, 4, e05464.
  • Fischer, B., Roth, V., Roos, F., Grossmann, J., Baginsky, S., Widmayer, P., Gruissem, W. & Buhmann, J.M. (2005). NovoHMM: a hidden Markov model for de novo peptide sequencing. Analytical Chemistry, 77, 7265-7273
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