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Dr. Wolfgang Huber

— abgelegt unter:

From genomes to phenotypes - statistical applications in transcriptomics, high-throughput RNAi and microscopy image based phenotyping.

  • FDM-Seminar
Wann 21.05.2010
von 11:15 bis 13:00
Wo Eckerstr.1, Raum 404
Kontakttelefon 0761/2037701
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Dr. Wolfgang Huber

European Molecular Biology Laboratory, Heidelberg

How do variations in the genomes of individuals shape their phenotypes?
Recent technological progress in high-throughput sequencing, genetic tools and automated microscopy imaging enable powerful experiments to address this question and place exciting challenges for data analysis and modelling.

The talk will have two sections:
First, I will report a statistical error model for high throughput nucleotide sequencing data. This technology provides quantitative readouts in assays for RNA expression (RNA-Seq) and protein-DNA binding (ChIP-Seq). Statistical inference of differential signal in these data needs to take into account their natural variability throughout the dynamic range. When the number of replicates is small, error modeling is needed to achieve statistical power. We propose an error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data. The method controls type-I error and provides good detection power. A free open-source R/Biondonductor software package, “DESeq”, is available.

Second, I will describe some aspects of the statistical modelling of large-scale RNAi experiments, where the response of cellular populations to the RNAi perturbations is monitored by live-cell microscopy. The data are analysed by automated image analysis, fitting of dynamic models of cell cycle progression, extraction of multivariate phenotypes, and definition of a multivariate phenotypic landscape.


[1] S. Anders, W. Huber. Differential expression analysis for sequence count data, Nature Preceedings doi:10.1038/npre.2010.4282.1

[2] R. Bourgon, R. Gentleman and W. Huber (2010) Independent filtering increases detection power for high-throughput experiments. PNAS 2010 in press.

[3] B. Neumann et al. Phenotypic profiling of the human genome by time-lapse microscopy reveals genes required for cell division, survival or migration. Nature 2010 Apr 1;464(7289):721-7.

[4] F. Fuchs et al. Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Molecular Systems Biology 2010 in press.

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