Develop mathematical, statistical, and computational methods

1. Develop mathematical, statistical, and computational methods to analyse biologically or technologically novel experiments in order to understand disease-relevant regulatory and genetic interaction networks high-density microarrays for RNA transcription and protein-DNA binding (ChIP-chip)  high-throughput reverse-genetic cell-based assays, phenotyping by high-content automated microscopy, and genetic interaction networks  Bioconductor

2. Typically, the projects involve 1:1 collaborations with experimentalists and involve all aspects  experiment design  quality control & normalization  statistical analysis  biological interpretation  bioinformatic integration / comparison with other data  design of follow-up experiment

3. Heart Repair Myocardial infarction: large numbers of cardiomyocytes die because of oxygen deprivation Cardiomyocytes usually do not regenerate Hope: Replace dead cells by new ones derived from stem cells or recruit other cells. But: Genetic basis of cardiomyocyte formation poorly understood Main experimental partner is S. Sperling / MPI Molecular Genetics in Berlin (Integrated EU Project)

4. Heart Transcription Factors Knowledge about the transcription factor network regulating myocyte development is sparse Heart Defects associated with mutations of certain TFs Expression levels of some TFs during in-vitro myocyte development But we are lacking the big picture.

5. Combined expression and DNA-protein binding profiling of congenital heart diseases transcription start DNA Nimblegen arrays to identify TF binding sites and dis- cover novel motifs Expression of target genes measured by expression arrays We develop statistical methods for data visualisation, peak detection and network inference ChIP probes Expression probes

6. differentiation events Hormone stimulation apoptosis Buffering, epistasis and complex phenotypes o Most diseases are not caused by single genes, rather by interactions of several genes o Buffering: knock-out of each individual gene has no phenotype, knock-out of both (or more) has o Epistasis: phenotype of one gene is modified by that of another Collaboration with M. Boutros (Heidelberg) A. Kiger (UCSD) R. Gentleman (Seattle)

7. C. elegans Drosophila Mammals E. coli dsRNA dsRNA siRNA T7 > 200bp > 200bp 21bp Injection and soaking Bathing Transfection Feeding bacteria Cell-culture Cell-culture Worms Precursor dsRNA Dicer siRNAs Degradation of target message RNAi in different organisms Slide by M. Boutros

8.  Detecting phenotypes Plate reader 96 or 384 well, 1…4 measurements per gene (average over all cells) Flow cytometry (FACS) 4…8 measurements per cell (1000s) Automated Microscopy unlimited Response Perturbation level

9. Automated high-throughput image analysis: feature extraction and cell phenotype classification

10. www. .org Bioconductor Publications about methods are important, but also the publication of methods: software Good scientific software is like a good article: reproducible, widely (or openly) accessible, building on work of others and to become a building block for others. Bioconductor: an open source and open development software project for the analysis of biomedical and genomic data. Initiated in 2001 by R. Gentleman (FHCRC Seattle). ~10 core developers, ~100 contributors, ~180 packages, thousands of users Gentleman, Carey, Huber, Irizarry, Dudoit (2005)

11. Selected publications 2005 Arlt D.*, Huber W.*, et al. (2005) Functional profiling: from microarrays via cell-based assays to novel tumor relevant modulators of the cell cycle. Cancer Research 65(17): 7733-7742 (2005) Durinck S., Y. Moreau, A. Kasprzyk, S. Davis, B. De Moor, A. Brazma & W. Huber (2005) BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21: 3439-3440 (2005). Gentleman R., Ruschhaupt M. and Huber W. (2005) On the Synthesis of Microarray Experiments. Huber W., Von Heydebreck A. and Vingron M. (2005) Low-level analysis of microarray experiments. Gentleman R., Carey V., Huber W., Irizarry R. and Dudoit S. (2005) Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer-Verlag

12. The group currently Matt Ritchie (Statistics) Lígia Brás (Electrical Eng.) Jörn Tödling (Bioinf.) Florian Hahne (Biology) Oleg Sklyar (Physics) Wolfgang Huber (Physics)

13. Gene expression matters

14. Genechip S. cerevisiae Tiling Array 4 bp tiling path over complete genome (12 Mio basepairs, 16 chromosomes) Sense and Antisense strands 6.5·106 oligonucleotides 5 mm feature size Chips manufactured by Affymetrix Application + analysis by L. Steinmetz (EMBL/Stanford Genome Center) and W. Huber (EMBL/EBI)

16. Probe specific response normali-zation remove ‘dead’ probes

17. High density tiling arrays o Conventional microarrays: measure transcript levels o High resolution tiling arrays: also transcript structure introns, exons transcription start & stop sites overlapping populations of transcripts non-coding RNA: UTRs, ncRNAs, antisense o Probe-response normalization: make signal comparable across probes o Model-based segmentation method with exact algorithm, including confidence intervals o Genome-wide evidence for association of non-coding RNA (antisense, UTRs) with function of the corresponding genes