Why do we do Bioinformatics ?

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 !