New graduate school: Munich School for Data Science.
Our goal is an improved understanding of the genetic basis of gene regulation and its implication in diseases. To this end, we employ statistical modeling of 'omic data and work in close collaboration with experimentalists.
Kremer et al. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nature Communications (2017)
Schwalb et al. TT-seq maps the human transient transcriptome. Science (2016)
Eser et al. Determinants of RNA metabolism in the S. pombe genome. Mol Syst Biol (2016)
Bader et al. Negative feedback buffers effects of regulatory variants. Mol Syst Biol (2015)
Gagneur et al. Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype. PLoS Genet (2013)
Xu et al. Antisense expression increases gene expression variability and locus interdependency. Mol Syst Biol (2011)
Bauer et al. GOing Bayesian: model-based gene set analysis of genome-scale data. Nucleic Acids Res (2010)
Zinzen et al. Combinatorial binding predicts spatio-temporal cis-regulatory activity. Nature (2009)
Xu et al. Bidirectional promoters generate pervasive transcription in yeast. Nature (2009)