1. Gene Expression, Proteomics CSC8309 Phillip Lord

2. Time table notes

3. Central Dogma DNA  RNA  Protein Transcription Translation In general, most regulation of gene expression occurs at the level of transcription. Genes can be regulated at many other times.

4. Transcriptional control is important Drosophila with mutated antp gene - Legs appear in replacement of antenna ( a gene that regulates thoracic development called Antennapedia )

5. Is a gene active? Taken from doi:10.1371/journal.pbio.0060027.g001 Taken from doi:10.1371/journal.pbio.0000053.g001 In situ hybridisation Northern Blotting

6. Is a protein produced? doi:10.1371/journal.pbio.0050067.g002 Western Blotting Immunofluoresence microscopy

7. Which new genes are expressed now? This question cannot be answered practically with northerns or microscopy. Likewise, changes in protein composition are hard with western. In both cases, you have to guess what genes are going to be affected in the first place.

8. Hence microarrays doi:10.1371/journal.pbio.0020007.g001

9. And proteomics doi:10.1371/journal.pbio.0020160.g001

10. Why both together Although the experimental techniques are different, many of the questions are the same Much of the bioinformatics analysis overlaps.

11. An idealised experiment Have a biological question, which you can answer by comparing two conditions. Design your experiment. Run your experiment Analyse the results Publish the results

12. Have a biological question… Is entirely obvious yet, frequently forgotten

13. Design your experiment How many conditions are there? How much variability is there, where does it come from? How should I randomise my sampling? How do I enable tests for systematic errors? How should I ensure consistency?

14. Run your experiment This bit, we don’t talk about much. Except to note that the experiment design is not always stuck to

15. Analyse your results There are three important parts to results analysis –Statistics Three colour pictures of the statistics is also good.

16. Analyse your results Increasingly, results have to look real. Corroboration with additional knowledge No "cherry-picking" Data integration Multi-source analysis

17. Publish your results The traditional mechanism –Do lots of statistics –Make a three colour picture, throwing away most of the statistics results. –Stick it in a paper, throwing away 99% of the value of the data –Throw away the original data; otherwise if someone wants it, then finding the right CD can be painful –Just in case, you can hide an excel spreadsheet on the corner of your website.

18. Publish your data The new way –Describe your experimental purpose –Your experimental design –Organise all your data, in a clearly describe way. –Lodge your data in a public repository.

19. What next? Lectures to cover “design” Lectures to cover “running” 2 practical covers the “analysis” Lectures covering “publishing”