In my previous post about Q10 a commenter suggested a software called "The Journal" by davidRM for productively keeping track of experiments, datasets, projects, etc. I've never tried this software before, but about a year ago I ditched my pen and paper lab notebook for an electronic lab notebook in the form of a blog using Blogger, the same platform I use to write Getting Genetics Done.

If you caught Soumya Raychaudhuri's seminar last week you heard a lot about the tool he developed at the broad called GRAIL - Gene Relationships Across Implicated Loci. You've got GWAS results and now you want to prioritize SNPs to follow up in replication or functional studies.

Soumya Raychaudhuri is the lead author on the GRAIL paper in Nature Genetics. GRAIL (Gene Relationships Across Implicated Loci) is software available from the Broad Institute and looks like an interesting way to prioritize SNPs for followup. I'll cover it here in the future, but in the meantime, check out this seminar this Wednesday.

Do you ever find yourself switching back and forth between your work computer, your laptop, and your home computer?  This happens to me all the time when I'm writing.  Rather than carry all your files on a USB stick and risk losing it or corrupting your data, give Dropbox a try.  It's dead simple, and works for PC, Mac, and Linux too.

R is a great tool with lots of resources for genetics, genome-wide association studies, and many other biological applications.  We've covered several places to find help in R in the past, but if you're still apprehensive about diving into R's command-line interface, fear not.  The R commander is a graphical user interface (GUI) for R that works under Windows, Linux, and Mac.

I wish I would have discovered this long ago.  Loading data into R or MySQL requires you to specify the full path to the file.  If you do this on a Windows machine there are two annoyances.  First, if you save something to your desktop the path to your desktop is really long.  Second, windows by default uses backslashes "\" in the file path, while R or other software requires forward slashes "/".

I've linked to UCLA's stat computing resources once before on a previous post about choosing the right analysis for the questions your asking and the data types you have.

When you need to focus and get some serious writing done, it may be a good idea to ditch your word processor and go with plain text instead. Save all your formatting and spell-checking to do later in one step. I just tried this out on a review article I needed to write and found it much easier to concentrate without all of MS Word's squiggly underlining, autocorrecting, autoformatting, and fancy toolbar buttons begging to be clicked.

What's your power to detect a recessive effect with an odds ratio of 1.2 for a disease with 4.2% prevalence using 1200 cases and 2900 controls? What if the allele is rare? Is it worth it, in terms of power gain, to genotype 1000 more individuals? How small of an effect can you detect with 80% power using the data you have? These questions and others can be answered by power and sample size calculations.