Evidence networks for the analysis of biological systems

1. Evidence networks for the analysis of biological systems Rainer Breitling IBLS – Molecular Plant Science group Bioinformatics Research Centre University of Glasgow, Scotland, UK

2. Background Datasets and evidence networks in post-genomic biology

3. Genomics Fully sequenced genomes (1995-2004): 18 archaea 163 bacteria 3 protozoa 24 yeast species and fungi 2 plants (Arabidopsis, rice) 2 insects (flies, honey bee) 2 worms (C.elegans, C. briggsae) 3 fish (fugu, puffer, zebrafish) chicken, cow, dog, mouse, rat, chimp human  lots of “lists” of genes

4. Transcriptomics • microarrays measure gene expression levels (mRNA concentrations) • relative or absolute values • in organisms, tissues, cells • produce gene lists (e.g., which genes are up-regulated by a disease, by drug treatment, in a certain tissue)

5. Proteomics • 2D gels, liquid chromatography, and mass spectrometry measure protein concentrations • in tissues, cells, organelles • detect chemical modifications and processing of proteins • produces lists of protein variants that are different among conditions

6. Metabolomics • chromatography and mass spectrometry measure metabolite concentrations • in tissues, cells, body fluids, cell culture medium • produces lists of affected metabolites

7. Evidence networks • relate items (genes, proteins, metabolites) that “have something to do with each other” • relationship is based on objective evidence • represented as bipartite graphs • two classes of nodes: items and evidence • automated analysis of results possible • intuitive visualization and links to literature

8. Types of evidence networks • Relationship can be based on • physical neighborhood • phyletic pattern similarity • expressional correlation • biophysical similarity • chemical transformation • functional co-operation • literature co-citations

9. Types of evidence networks • Relationship can be based on • physical neighborhood • phyletic pattern similarity • expressional correlation • biophysical similarity • chemical transformation • functional co-operation • literature co-citations A O M P K Z Y Q V D R L B C E F G H S N U J X I T W phy: aompkzy--d-l-----------it – 22aompkzy--d-l-----------it- NtpA [C] H+-ATPase subunit A 17aompkzy--d-l-----------it- NtpB [C] H+-ATPase subunit B 17aompkzy--d-l-----------it- NtpD [C] H+-ATPase subunit D 18aompkzy--d-l-----------it- NtpI [C] H+-ATPase subunit I

10. Types of evidence networks • Relationship can be based on • physical neighborhood • phyletic pattern similarity • expressional correlation • biophysical similarity • chemical transformation • functional co-operation • literature co-citations

11. Types of evidence networks • Relationship can be based on • physical neighborhood • phyletic pattern similarity • expressional correlation • biophysical similarity • chemical transformation • functional co-operation • literature co-citations

12. Types of evidence networks • Relationship can be based on • physical neighborhood • phyletic pattern similarity • expressional correlation • biophysical similarity • chemical transformation • functional co-operation • literature co-citations

13. Types of evidence networks • Relationship can be based on • physical neighborhood • phyletic pattern similarity • expressional correlation • biophysical similarity • chemical transformation • functional co-operation • literature co-citations

14. Types of evidence networks • Relationship can be based on • physical neighborhood • phyletic pattern similarity • expressional correlation • biophysical similarity • chemical transformation • functional co-operation • literature co-citations

15. What is the big picture? Graph-based iterative Group Analysis for the automated interpretation of biological datasets lists + graphs = understanding

16. What does this list mean?

17. iterative Group Analysis (iGA) iGA uses simple hypergeometric distribution to obtain p-values Breitling et al., BMC Bioinformatics, 2004, 5:34

18. Graph-based iGA Breitling et al., BMC Bioinformatics, 2004, 5:100

19. Graph-based iGA 1. step: build the network Breitling et al., BMC Bioinformatics, 2004, 5:100

20. Graph-based iGA 2. step: assign ranks to genes Breitling et al., BMC Bioinformatics, 2004, 5:100

21. Graph-based iGA 3. step: find local minima p = 1/8 = 0.125 p = 6/8 = 0.75 p = 2/8 = 0.25 Breitling et al., BMC Bioinformatics, 2004, 5:100

22. Graph-based iGA 4. step: extend subgraph from minima p=0.014 p=0.018 p=0.125 p=1 Breitling et al., BMC Bioinformatics, 2004, 5:100

23. Graph-based iGA 5. step: select p-value minimum p=0.014 p=0.018 p=0.125 p=1 Breitling et al., BMC Bioinformatics, 2004, 5:100

24. Advantages of GiGA • fast, unbiased and comprehensive analysis • assignment of statistical significance values to interpretation • detection of significant changes even if data are too noisy to reliably detect changed genes • statistically meaningful interpretation already without replication experiments • detection of patterns even for small absolute changes • flexible use of annotations + intuitive visualization

25. Example 1 Microarrays Gene expression changes during the yeast diauxic shift

26. Yeast diauxic shift studyDeRisi et al. (1997)Science 278: 680-6

27. Yeast diauxic shift study

28. GiGA results – diauxic shift

29. small ribosomal subunit large ribosomal subunit nucleolar rRNA processing translational elongation

30. GiGA case study – diauxic shift

31. respiratory chain complex II glyoxylate cycle citrate (TCA) cycle oxidative phosphorylation (complex V) respiratory chain complex III

32. respiratory chain complex IV

33. Example 2 Metabolomics Changes in metabolic profiles in drug-treated trypanosomes

34. GiGA applied to metabolomics data • Challenge: No annotation available • Solution: Build evidence network based on hypothetical reactions between observed masses (=mass differences)

35. Metabolite tree of mass 257.1028 (glycerylphosphorylcholine) 6 generations

36. Metabolite tree of mass 257.1028 4 generations

37. Metabolite tree of mass 257.1028 2 generations

38. Metabolite tree of mass 257.1028 colors indicate changes of metabolite signals compared to untreated samples after 60 min pentamidine (red = down, green = up)

39. GiGA metabolite trees for one experimental example

40. Choline tree found by GiGA(most significant subgraph, p<10-13) extracted from

41. Summary • post-genomic technologies produces “lists” • neighborhood relationships yield “evidence networks (graphs) • lists + graphs = biological insights • GiGA graph analysis highlights and connects relevant areas in the “evidence network”

42. Acknowledgements • Pawel Herzyk – Sir Henry Wellcome Functional Genomics Facility • Anna Amtmann & Patrick Armengaud – IBLS Molecular Plant Science group • Mike Barrett – IBLS Parasitology Research group • FGF academic users: Wilhelmina Behan, Simone Boldt, Anna Casburn-Jones, Gillian Douce, Paul Everest, Michael Farthing, Heather Johnston, Walter Kolch, Peter O'Shaughnessy, Susan Pyne, Rosemary Smith, Hawys Williams

43. Contact Rainer Breitling Bioinformatics Research Centre Davidson Building A416 University of Glasgow, Scotland, UK R.Breitling@bio.gla.ac.uk http://www.brc.dcs.gla.ac.uk/~rb106x