BioStar is a site for asking, answering and discussing bioinformatics questions and issues. We are members of the community and find it very useful. Often questions and answers arise at BioStar that are germane to our readers (end users of genomics resources). Every Thursday we will be highlighting one of those items or discussions here in this thread. You can ask questions in this thread, or you can always join in at BioStar.
This week’s question generated a lot of chatter. It’s so timely for me, too, because I was just wrestling with the educational issues on this topic for that NSF proposal that came out a week back. People being trained at the new interface of bench biology and computational biology need some guidance on dealing with big data and software tools that are often brand new–but they don’t see the “textbook”. And that’s kind of the point I made in my proposal. That we have to teach these students in new ways because this field changes so fast, and there just is no damn textbook.
But definitely have a look at the question and suggestions. I thought it was very interesting as a thought exercise, and also the resources that people did find useful.
I came from a wet lab background and now doing a Ph.D. in genomics. Our lab is pretty decent in publication and my advisor often says the training we received in handling large amount of data is very desirable out there, etc. I can sense the need for this type of training is strong but I don’t feel I’m getting the best or most effective training in how to handle data or being a good bioinformatics even though our program is ranked pretty high in US.
Just looking at my peers, I felt there is a great level of variation in the level of technical skills students have in the genomics filed. I struggle to find a “textbook” on the best practice type of thing. How did you learn it or suggest to do so in an efficient, systematic way, for students with biology, non-computer science background?
Go. Read. I thought it was a wise question, but that there isn’t one good answer in this rapidly developing field yet. And this field has so many branches–medical applications like cancer genomics, variations across individuals (not just human data sets), metagenomics with huge amounts of different species data, and many other paths.