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The challenge of bioinformatics

Explore the power of bioinformatics in understanding DNA, mRNA, proteins, and genetic networks. Learn about scanning mRNA, comparing protein gels, inferring genetic networks, and more. A fascinating journey in the world of bioinformatics awaits!

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The challenge of bioinformatics

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  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. 1. DNA Frank Wright et al BioSS

  5. 1.DNA

  6. DNA TOPALi

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

  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 216–1= 65535) expression values.

  9. 2. mRNA 65535 Censored spot 0 Imputed values 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 • pH • Mol. Wt.

  15. 3. Proteins How to compare gels 1 and 2? gel 1 gel 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. Loadings Protein identity 3. Proteins • Principal Components Analysis • Identify groups of proteins that are affected in a consistent manner by treatments Jim McNicol

  20. 4. Genetic networks

  21. 4. Genetic networks

  22. 4. Genetic networks 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”

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