1. Why do we do Bioinformatics ? Hugh Shanahan, Department of Computer Science, Royal Holloway, University of London FAFU, Fuzhou, Fujian 4 Sep 2012 This talk is available at http://gene.cs.rhul.ac.uk/CCC12/Lectures/bioinformatics.ppt

2. Summary Who am I ? Impact of Bioinformatics Lessons learnt Being a professional Bioinformatician Question time

3. Summary Background in High Energy (Particle) Physics In 2000 moved into Bioinformatics at UCL/EBI Worked on Protein Structures - identifying evolution of hydrophobic patches Identifying DNA-binding proteins from structure alone In 2005 started lectureship at Computer Science at Royal Holloway Working on transcriptomics - Plant Science and Human Data

4. Big Picture Best estimate by the end of century human population will plateau at 10 Billion. Some countries will face an increasingly older demographic (some will still be very young). Climate change is a reality - Permanent Artic ice cap could be gone in FOUR years. We live longer and have healthier lives than our parents/grandparents/.... Large disparities between different populations Human migration occurs on a huge scale

5. Challenges from the big picture Need to feed more people with a better diet - 3-fold improvement of yield for crops. Need to ensure that everybody stays happy with an older demographic - healthier for longer Need to ensure this happens across the world (otherwise the world comes to your doorstep) Need to do this with a much more variable weather systems/reduce greenhouse gas emissions

6. Bioinformatics in this big picture Need major steps forward in Plant Science Crop yield Crops in poor environments Biofuels Medicine

7. Omic data - Biologists/Biomedical Scientists generate this data and more Genomic Transcriptomic Metabolomic Proteomic...

8. Omic data - Where you fit in Biologists and Biomedical Scientists generate this data. They are not capable of making the most of this data. That is your job. You will guide and help them to learn from that data. This will ultimately feed back into the challenges discussed above.

9. Lesson learnt Data is always more important than algorithms. Algorithms are always more important than conjecture. The best computational biology algorithms use evolution. Understand evolution. Most of the time you have to deal with reading in data so think hard about the best way of storing data.

10. Being a professional Two modes of research work with other Bioinformaticians and publicly available data work with wet lab scientists and their pre-published data The second mode means you need to think like a professional

11. Responsibilities to wet lab Scientists Point out if there are problems with the data. Do everything that can be done with the data. Do not promise miracles. Encourage them to make the data available after publication(s).

12. Thank you for your time ! Please ask questions !