I saw a demonstration of this tool at the workshop on network analysis I announced last week. Genes2Networks draws from a large background network consisting of several experimentally verified mammalian protein interaction databases. It will take a list of genes you provide as seed genes and identify all interacting genes that fall on paths through the background network between them.

Amidst the fallout of an academic discussion over the worth of GWA studies followed by several gloomy and scathing articles in the popular press, came this paper in Nature Genetics. In summary, the investigators sequenced all the coding DNA on the X-chromosome in families affected with an evidently X-linked mental retardation phenotype.

Save yourself a few keystrokes the next time you visit GGD. Going to gettinggeneticsdone.com will now automatically redirect you to the blogspot address. If you've bookmarked the site or subscribed to the site's feed with RSS, everything will still work.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Avi Ma'ayan, Ph.D., Assistant Professor in the Department of Pharmacology and Systems Therapeutics of Mount Sinai School of Medicine, will present "Network Analysis in Systems Biology", at 10:00 am on Wednesday, April 22 in Room 206 PRB.

Here are four interesting and provocative articles in New England Journal of Medicine: David Goldstein: Common Genetic Variation and Human Traits Joel Hirschhorn: Genomewide Association Studies — Illuminating Biologic Pathways Peter Kraft and David Hunter: Genetic Risk Prediction — Are We There Yet? John Hardy &

I briefly mentioned "time" in the previously posted Linux command line cheat sheet, but I can't overstate its utility especially for ACCRE/Vampire users. The time command does exactly what it sounds like: it times exactly how long it takes to run anything at the command line. And it couldn't be easier to use. Just type the word "time" preceding what you would normally type in at the command line.

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.

If you're interested in gene-gene and gene-environment interaction (and who wouldn't be?), then you should check out the Epistasis Blog. Our friend and colleague Jason Moore at Dartmouth Medical School has maintained compgen.blogspot.com since 2005 writing about epistasis, computational genetics, and related topics. I've personally stumbled upon several interesting papers mentioned here that I may have otherwise missed.

Will recently posted a set of flowcharts made by Marylyn, Jason, and Tricia, to help choose which statistical analysis to use for nearly any situation. UCLA has a very similar chart with links to Stata code, examples, and annotated output for every method they mention. Also, check out their Stata help homepage and Stata learning modules. They're excellent resources for learning how to use Stata for data analysis!

This abstract in BMC Bioinformatics was presented in our Computational Genetics Journal Club a few weeks back: "Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases." As described by the authors at CDC, Gene Prospector is a bioinformatics tool designed to sort, rank, and display information about genes in relation to human diseases, risk factors and other phenotypes.

For the second installment in our Noteworthy Blog series, take a look at Chun Li's biostatistics course blog. Several years ago, CHGR faculty member Chun Li taught a class in biostatistics, maintaining this blog over the duration of the course.