Genomic and proteomic approaches offer unprecedented opportunities for discovery. Nevertheless, daunting difficulties need to be overcome before we can realize their potential. The first of these challenges is of computational nature, large datasets need computational processing during the interpretation phase.
From a life scientist’ perspective informatics is often perceived as a stand alone scientific field that requires extensive training and a specialized skill set that appears to be out of reach. I consider this an overly pessimistic sentiment. It is akin to believing that only English Literature majors could possibly write good prose.
I am not suggesting of course that Computer Science is a particularly easy branch of science. However, what most life scientists need to know to be successful is a a small sliver of the overall domain knowledge. Yet even that would be immensely empowering because it would allow these scientists to produce answers for most of the inquires that they are interested in.
My belief is that a systematic and level appropriate computational education targeting life scientists is the only way to enable them to become competitive and succesful in their research.
For such a computational training to be successful it needs be to be:
The page above has been written in 2008. By 2009 I have developed a new coursework shaped by the ideals presented above. The course is offered at Penn State starting with the Fall of 2009 under the title Practical Data Analyis for Life Scientists see the course page