For those of you who don’t dive into the comments section of posts, I wanted to share some really good points others made commenting on my previous entry:
William Nelson points out that whatever choice you make for computation support (collaboration/hiring in house/farming it out) it makes sense to involve them from the initial design phase, rather than coming to them with a bunch of pre-generated data. Generating a dataset that will play nice with computational pipelines often means an experiment that has a stronger overall design, so this is really a win-win for all involved. The same logic extends to designing follow up analyses. Explain what you’re interested in finding out, not just the specific analysis you’d like run. There may be much more effective or faster ways to find out the answer to your question that someone familiar with the nuts and bolts of computational work can suggest if they understand the whole problem.
He also very correctly points out that as expensive as hiring computational biologists or programmers who understand biology can be, people with those same skill sets make a lot more money outside of academia. So keep your programmers (whether co-workers, employees, or collaborators) feeling happy and valued! (A woman who entered grad school the same time as I did brings her lab’s programmer cookies each time she needs something done, and she’s getting papers so fast she is on schedule to graduate before anyone else in our incoming class. 😉 )
Meanwhile Matt (the scientist gardener) reminds everyone that being a whiz with computers and a whiz with statistics don’t go hand in hand. And for big data projects like most high throughput sequencing experiments, a lack of statistical expertise can hamstring a project just as effectively as a lack of computational skill. So your university’s statistics department is another set of intelligent colleagues you should remember to develop and maintain good relationships with.
He also reminds us that computational analysis isn’t the only talent people can use to get through a PhD without developing the skills necessary to direct a research project of their own as a Principle Investigator later in life. It’s all too easy to fall into the same trap by being the one person in lab who is good at some complicated molecular biology technique. The example he used was chromosome walking… which I’m ashamed to admit I had to look up on wikipedia.