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Huber Group

Computational Genetics

HuberS

Genomewide phenotypic map. a. Each of the 1,839 nodes represents an siRNA perturbation of cultured cells whose shape and morphology was monitored by automated microscopy. Nodes are linked by an edge when they are phenotypically similar. The graph is a two-dimensional representation of phenotypic diversity and similarity. b. Representative images for four siRNA perturbations. Cells were stained at the nuclei (DAPI, blue), actin (red) and tubulin (green).

Previous and current research

 The group studies genotypes and phenotypes on a genome-wide scale: how do variations in the genomes of individuals shape their complex phenotypes? To this end, we develop computational methods in statistics, signal and image processing, and probability models.
We work with experimental labs in systems genetics and functional genomics to design and analyse genome-wide experiments whose aim is to unravel the mechanisms of genetic inheritance, gene expression, molecular interactions, signal transduction and how they shape phenotypes. Most phenotypes, including human diseases, are complex, i.e., they are governed by large sets of genes and regulatory elements. Our aim is to map these complex networks and eventually devise strategies for designing phenotypes by engineering combinatorial perturbations.
Our research is stimulated by new technologies, and we employ data from high-throughput sequencing (ChIP-seq, RNA-seq, genotyping, polymorphism discovery), tiling microarrays, large scale cell based assays, automated microscopy, as well as the most advanced methods of computational statistics.We are a regular contributor to the Bioconductor project (www.bioconductor.org).

Future projects and goals

One of the most exciting questions in biology is the predictive modelling and engineering of phenotypic outcomes based on individual genomes. To get there, we need a better understanding of cellular regulation and physiological processes through advances in experimental technologies for the manipulation and observation of genetic model systems, and in computational biology for understanding the data and model building. Of particular interest to us are systematic genetic assays for phenotypic consequences of DNA sequence and copy number variation and of drug perturbation; as well as high-content phenotyping using automated microscopy. To make these advances fruitful for predictive models of biological systems, we aim to stay at the forefront of developments in data analysis, statistical software and mathematical modelling. An emphasis lies on project- oriented collaborations with experimenters.