1. 6 November 2007 © ETH Zürich | Genevestigator Gene expression analysis and network discovery: Genevestigator Philip Zimmermann, Genevestigator Team, ETH Zurich

2. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 2 Presentation flow  Gene networks – biological context  Microarray compendium: how, and what for?  Meta-profile analysis: concepts and validation  Genevestigator ® V3  Data integration  Summary & conclusion

3. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 3 Presentation flow  Gene networks – biological context  Microarray compendium: how, and what for?  Meta-profile analysis: concepts and validation  Genevestigator ® V3  Data integration  Summary & conclusion

4. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 4 Gene networks - biological context  What is the interpretational value of a gene network derived by graphical modeling or correlation analysis?  a snapshot in time?  a snapshot in space?  an average trend?

5. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 5 Gene networks - biological context  From what experiment(s) was this network derived?  time-course?  cell culture, whole organism?  stimulus, drug response?  anatomy part?  stage of development?  genetic modification?

6. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 6 Context and dynamics of networks  Hypothesis: networks are dynamic and context-dependant  => networks evolve!  => networks may have different functions in different contexts!  Question: how can we quantify the role of the context in shaping the network?

7. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 7 Context: the time-space-response dimensions  Time => time-course, development  Space => anatomy parts, intracellular localization  Response => response to external perturbations => response to modifications in the genome

8. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 8 Context and dynamics of networks  Modeling the time, space and response dimensions requires:  experiments testing time, space and response variables  storage of measurement data and its meta-data  developing analysis methods that incorporate these dimensions (→ meta-profiles)

9. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 9 Presentation flow  Gene networks – biological context  Microarray compendium: how, and what for?  Meta-profile analysis: concepts and validation  Genevestigator ® V3  Data integration  Summary & conclusion

10. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Analysis versus meta-analysis Data storage Data analysis 100 genes – what to do next? 10 billion data points – what to do next? Microarray experiment

11. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch heterogenous datasets Data repositories unsystematic or poor annotation Data Annotations + meta-analysis impossible! ? =

12. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Data warehouses Data quality control + ordered datasets meta-analysis possible! = systematic annotation Expert annotation with systematic ontologies anatomy development stimulus mutation

13. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 13 Data quality control RLENUSE Border elementsCorrelation matrix Affy QC metrics RNA degradation Unprocessed values

14. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Ontologies – example of Anatomy  Mouse / Rat:  Edinburgh Mouse Atlas  Human:  mapping to Mouse and Rat anatomy tree  Arabidopsis / Barley:  terms from Plant Ontology  tree created by Genevestigator Expert annotation with systematic ontologies anatomy development stimulus mutation

15. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Ontologies – example of Development  Mouse: Theiler stages  Rat: Witschi stages  Human: Carnegie table  Arabidopsis: Boyes key

16. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Meta-analysis tools Who is most interested to mine this data? Who can best interpret the results? THE BIOLOGIST! Genevestigator ® – a tool for biologists

17. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 17 Presentation flow  Gene networks – biological context  Microarray compendium: how, and what for?  Meta-profile analysis: concepts and validation  Genevestigator ® V3  Data integration  Summary & conclusion

18. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 18 Expression meta-profiles [space] [time] [response] [response]

19. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 19 Data validation Category type Probe set e.g. heart ventricle e.g. Mm.23432 [space] [time] [response]

20. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 20 Data validation Category type Probe set e.g. heart ventricle [space] [time] [response]

21. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 21 Mouse anatomy meta-profiles [space]

22. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 22 Data validation Category type Probe set e.g. Mm.23432 [space] [time] [response]

23. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 23 Transcription of Rnf33 has been shown to occur already in the mouse oocyte but not beyond the eight-cell stage nor in adult tissues Rnf33 Hoxa1 expression starts at E7.5 and begins to retreat caudally by day E8.5 hemopexin (hx), is known to be only lowly expressed in embryos and newborn mice and reaches it’s highest expression level not until the first year of age Hoxa1 hemopexin a – f: pre-natal g – l: post-natal

24. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch light-harvesting chlorophyll a/b binding protein (AT4G14690 ) protochlorophyllide reductase A (At5g54190 )

25. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 25 Presentation flow  Gene networks – biological context  Microarray compendium: how, and what for?  Meta-profile analysis: concepts and validation  Genevestigator ® V3  Data integration  Summary & conclusion

26. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 26 Development of Genevestigator ®  14‘500 Affymetrix arrays (Nov 2007)  Human, mouse, rat, arabidopsis, barley  Metabolic and regulatory pathway maps for mouse and arabidopsis  > 10‘000 registered users  > 500 citations in peer reviewed journals Anatomy Development Stimulus Mutation Microarray data Public repositories Genevestigator database Curation & Quality control Biological experiments Application server Client Java application Genevestigator

27. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 27 Genevestigator ® V3 WebsiteJava Client Application Database and Application Server Cluster

28. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 28 Toolsets and tools

29. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 29 [space] [time] [response]

30. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 30

31. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 31

32. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Biomarker Search toolset

33. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 33 Abiotic stresses and hormonal responses salt (+) osmotic (+) cold (+) ABA (+) 2,4-D glucose salt (+) osmotic (+) ABA (+) norflurazon (-) mycorrhiza (-) anoxia (-) hypoxia (-) BL / H 3 BO 3 (+) syringolin (-) cycloheximide (-) H 2 O 2 (-) salt (-) osmotic (-) --- ozone (-) genotoxic (-) salt (+) drought (+) MeJA (+) syringolin (-) P. syringae (+) ozone (+) B. cinerea (+) hypoxia (-) ethylene (+) AVG (+) chitin (+)

34. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 34 [space] [time] [response]

35. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 35 Biclustering  Searches subsets of genes coexpressed across subsets of conditions  BiMax algorithm  Finds all maximal bicliques [space] [time] [response]

36. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Example of a bicluster 36

37. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 37 ABA response Beta-alanine Starch / sucrose Inositol phosphate Cold response Phenylalanine / Tyrosine Proline ABA biosynthesis [space] [time] [response]

38. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 38 Presentation flow  Gene networks – biological context  Microarray compendium: how, and what for?  Meta-profile analysis: concepts and validation  Genevestigator ® V3  Data integration  Summary & conclusion

39. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 39 Biomarker search [time]  Genes expressed specifically in seeds and germinating seedlings  De-novo identification of cis-regulatory elements

40. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 40 Biomarker search [space] z = 18.2 z = 5.8 z = 5.4

41. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 41 Biomarker search [response]  „Supervised biclustering“  isoxaben (+)  norflurazon (-)  light (+)  nitrate_low (-)

42. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Anatomy clustering and promoter analysis  Clusters of genes expressed specifically in:  cell suspension  petals  roots  seeds  stamen  xylem z > 5.0

43. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Development clustering and promoter analysis  Clusters of Arabidopsis genes expressed specifically at:  dev. stage 1  dev. stage 3  dev. stage 9 z > 5.0

44. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Stimulus clustering and promoter analysis  „Supervised biclustering“ of stimulus meta-profiles:  cluster 1  cluster 2  cluster 4  cluster 5  cluster 7 z > 5.0

45. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Data integration: transcriptome - proteome cell suspension cotyledons flowers leaves roots seeds cell suspension cotyledons flowers leaves roots seeds Transcripts Proteins

46. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Arabidopsis leaf transcripts and proteins Protein quantification measure Transcript quantification measure Frequency general background range for transcript quantification measure proteins detected in leaves proteins not detected in leaves but for which there is a probeset on the ATH1 array

47. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Protein detection and transcript abundance Fraction of „present“ transcripts that were detected on the protein level probe sets called “absent” on ATH1 (p >= 0.05) probe sets called “present” on ATH1 (p < 0.05) leaf proteins detected by peptide identification 0 500 1000 1500 2000 2500 3000 3500 4000 123456789101112131415 Transcript abundance measure (log2 signal) Number of transcripts/proteins leaf proteins detected 0 0.2 0.4 0.6 0.8 1 1.2 6789101112131415

48. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch GO analysis cell wall chloroplast cytosol ER extracellular Golgi apparatus mitochondria nucleus other cellular components other cytoplasmic components other intracellular components other membranes plasma membrane plastid ribosome ATH1 array (control) Proteins not detected but transcripts have high abundance ( >13 ) 0 0.2 0.4 0.6 0.8 1 1.2 67891011121314 15 GO Cellular Component n = 221 specific probesets with average signal in leaves >13

49. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch GO analysis cell organization and biogenesis developmental processes DNA or RNA metabolism electron transport or energy pathways other biological processes other cellular processes other metabolic processes protein metabolism response to abiotic or biotic stimulus response to stress signal transduction transcription transport ATH1 array (control) Proteins not detected but transcripts have high abundance ( >13 ) 0 0.2 0.4 0.6 0.8 1 1.2 67891011121314 15 GO Biological Process n = 221 specific probesets with average signal in leaves >13

50. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch GO analysis 0 0.2 0.4 0.6 0.8 1 1.2 67891011121314 15 GO Molecular Function n = 221 specific probesets with average signal in leaves >13 DNA or RNA binding hydrolase activity kinase activity nucleic acid binding nucleotide binding other binding other enzyme activity other molecular functions protein binding receptor binding or activity structural molecule activity transcription factor activity transferase activity transporter activity ATH1 array (control) Proteins not detected but transcripts have high abundance ( >13 )

51. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Data integration – pathway analysis Protein abundance Transcript abundance Carotenoid biosynthesis Phenylpropanoid metabolism Chlorophyll / Porphyrin metabolism Riboflavin metabolism Mevalonate biosynthesis

52. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Relative protein-to-transcript ratio Calvin cycle Fatty acid biosynthesis serine, glycine, cystein starch and sucrose metabolism

53. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Relative protein-to-transcript ratio Chlorophyll / Porphyrin metabolism Fatty acid biosynthesis Glycolysis / Gluconeogenesis Purine metabolism Pyrimidine metabolism

54. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Proteomic and transcriptomic biomarkers „Root-specific“expression Search by scoring the proteomic dataset Search by scoring the Genevestigator dataset

55. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Proteomic and transcriptomic biomarkers Search by scoring the proteomic dataset Search by scoring the Genevestigator dataset

56. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 56 Presentation flow  Gene networks – biological context  Microarray compendium: how, and what for?  Meta-profile analysis: concepts and validation  Genevestigator ® V3  Data integration  Summary & conclusion

57. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 57 Summary and conclusions  Biological networks: importance of the biological context  Meta-profiles: context-driven analysis  Biological validation of meta-profiles and clusters  Genevestigator – a tool for biologists!  Data integration: challenging biological complexity

58. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Experimental context? Organism? Data type? Modes of interactions? Network dynamics? Reproducibility?

59. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch Acknowledgements  ETH Zurich  Prof. Gruissem  Developer Team:  Tomas Hruz, Oliver Laule, Stefan Bleuler, Philip Zimmermann  Gabor Szabo, Frans Wessendorp, Lukas Oertle, Dominique Dümmler, Matthias Hirsch-Hoffmann

60. 6 November 2007 P. Zimmermann / ETH Zurich / phz@ethz.ch 60 Thanks for your attention!