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
Summary
• Who am I ?
• Impact of Bioinformatics
• Lessons learnt
• Being a professional Bioinformatician
• Question time
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
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
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
Bioinformatics in this big picture
• Need major steps forward in
• Plant Science
• Crop yield
• Crops in poor environments
• Biofuels
• Medicine
Omic data -
• Biologists/Biomedical Scientists generate this data and more
• Genomic
• Transcriptomic
• Metabolomic
• Proteomic
• ...
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.
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.
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
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).
Thank you for your time !
Please ask questions !