ayout 1 - EMBL Grenoble

Gene Expression UnitComputational GeneticsPrevious and current researchThe group studies genotypes and phenotypes on a genome-wide scale: how do variations in thegenomes of individuals shape their complex phenotypes? To this end, we develop computationalmethods in statistics, signal and image processing, and probability models.We work with experimental labs in systems genetics and functional genomics to design and analysegenome-wide experiments whose aim is to unravel the mechanisms of genetic inheritance, geneexpression, molecular interactions, signal transduction and how they shape phenotypes. Mostphenotypes, including human diseases, are complex, i.e., they are governed by large sets of genesand regulatory elements. Our aim is to map these complex networks and eventually devise strategiesfor 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, largescale cell based assays, automated microscopy, as well as the most advanced methods of computationalstatistics. We are a regular contributor to the Bioconductor project (www.bioconductor.org).Future projects and goalsWolfgang HuberPhD 1998, University ofFreiburg.Postdoctoral research at IBMin San Jose, California and atthe German Cancer ResearchCentre (DKFZ), Heidelberg.Group leader at EMBL-EBIsince 200. Jointappointment with the GeneExpression Unit.Group leader in the GeneOne of the most exciting questions in biology is the predictive modelling and engineering of phenotypicoutcomes based on individual genomes. To get there, we need a better understanding ofExpression Unit since 2009.cellular regulation and physiological processes through advances in experimental technologies forthe 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 ofdrug perturbation; as well as high-content phenotypingusing automated microscopy. To make theseadvances fruitful for predictive models of biologicalsystems, we aim to stay at the forefront of developmentsin data analysis, statistical software andmathematical modelling. An emphasis lies on project-orientedcollaborations with experimenters.Genome-wide phenotypic similarity map. (A) Each ofthe 1,839 nodes represents an siRNA perturbation inHeLa cells whose shape and morphology wasmonitored by automated microscopy. Representativeimages for four siRNA perturbations, for the targetgenes DONSON, NUF2, RRM1 and SH2B2, are shownin panel B. Nodes in the map are linked by a grey edgewhen they are phenotypically similar. The graph is atwo-dimensional representation of the phenotypicdiversity observed after a genome-wide siRNAperturbation screen.Selected referencesXu, Z. et al. (2009). Bidirectional promoters generate pervasivetranscription in yeast. Nature, 57, 1033-7Hahne, F. et al. (2008) Bioconductor Case Studies (Use R series).SpringerLin, S.M. et al. (2008). Model-based variance-stabilizingtransformation for Illumina microarray data. Nucleic Acids Res., 36,Article e11Mancera, E. et al. (2008). High-resolution mapping of meioticcrossovers and non-crossovers in yeast. Nature, 5, 79-85Tödling, J. & Huber, W. (2008). Analyzing ChIP-chip data usingBioconductor. PLoS Computational Biology, , e100022735