1. The challenge of bioinformatics Chris Glasbey Biomathematics & Statistics Scotland

2. Talk plan 1. DNA 2. mRNA 3. Protein 4. Genetic networks

3. 1. DNA

4. Frank Wright et al BioSS

5. 1.DNA

6. TOPALi

7. 2. mRNA Prepare cDNA targets Label with fluorescent dyes Combine Equal Amounts Hybridise for 5 -12 hours Scanning

8. 2. mRNA Scanner’s PMT setting is one of the sources of contamination. Scanner’s setting is to be raised to a certain level to make the weakly expressed genes visible. This may cause highly expressed genes to get censored (at 2 16 –1= 65535) expression values.

9. 2. mRNA Censored spot Imputed values 0 65535 With GTI (Edinburgh)

10. 2. mRNA Multiple scans

11. Mizan Khondoker

12. 2. mRNA Jim McNicol

13. 3. Proteins Electrophoresis gel Lars Pedersen DTU, Denmark

14. 3. Proteins Protein separation by 1.pH 2.Mol. Wt.

15. 3. Proteins gel 1 gel 2 How to compare gels 1 and 2?

16. 3. Proteins John Gustafsson, Chalmers University, Sweden WARP

17. 3. Proteins Two gels superimposed (in different colours)

18. 3. Proteins  Statistical Design  3 complete reps of  15 treatment combinations.  (3 ecotypes by 5 heavy metals)  Maximum of 1400 protein spots per gel  Statistical Analyses  Filter data – remove spots with low intensity values and low quality scores (leaving ~290 spots)  Individual proteins – ANOVA, main effects and interactions

19. 3. Proteins  Principal Components Analysis  Identify groups of proteins that are affected in a consistent manner by treatments Protein identity Loadings Jim McNicol

20. 4. Genetic networks

22. Is it possible to infer the network from gene expression data such as these? Dirk Husmeier

23. 4. Genetic networks Bayesian network

24. 4. Genetic networks truth inferred

25. “I genuinely believe that we are living through the greatest intellectual moment in human history.” (Matt Ridley, Genome, 1999) “Grand Unified Systems Biology”