First, if you haven't taken a look at the comments on my previous post on this paper, go take a look. Thanks to everyone for sharing your thoughts and pointing out some of my own oversight regarding this paper. There was one issue in particular that deserves more attention than just another comment thread.

Check out this paper in PNAS and the corresponding synopsis in the New York Times. The authors take a unique approach to finding genes likely to be associated with human traits using orthologous phenotypes in model organisms, or phenologs. The idea is simple. The authors have a database of ~2000 disease associated genes in humans.

Here's a very good 3-page essay on how modern scientific policy and culture (e.g. short-term funding, unstable job security, publish-or-perish mindset) is adversely affecting young scientists, causing lots of bright minds to abandon academia in search of other careers (via @WileyScience). BioEssays: How today's scientific culture affects young scientists Anyone care to comment?

The authors here invited ACM KDD Innovation Award and IEEE ICDM Research Contributions Award winners to each nominate up to 10 best-known algorithms in data mining, including the algorithm name, justification for nomination, and a representative publication reference. The list was voted on by other IEEE and ACM award winners to narrow this down to a top 10 list.

The recent settlement between Arizona State University and the Havasupai Indian tribe is calling attention to (and perhaps challenging) the ideas of informed consent. While I'm sure there are arguments to be made supporting both sides of this case, regardless of your position this is an excellent reminder that there are people's lives behind the alleles in our spreadsheets and PED files.

Update 2010-04-21 : I forgot to post the link last time. That would have been helpful. Here you go: Vanderbilt Biostatistics: Statistical Problems to Document and to Avoid ..... At the Regression Modeling Strategies course I attended a few weeks ago, Frank Harrell pointed out the checklist on the biostatistics department's website of statistical problems to document and avoid.

This looks like a must-read for anyone starting out in computational biology without extensive experience at the command line.  The 135-page document linked at the bottom of the Google Group page looks like an excellent primer with lots of examples that could probably be completed in a day or two, and provides a great start for working in a Linux/Unix environment and programming with Perl.

Update Thursday, April 29, 2010: See further commentary at a newer post here. Just finished reading Jon McClellan and Mary-Claire King's Genetic Heterogeneity in Human Disease essay in Cell. It's definitely one of the most forthright and compelling essays I've read on the subject of the inadequacy of GWAS for identifying genes that cause complex human disease. The essay starts with an evolutionary perspective.

Check out this review essay in Cell: Genetic Heterogeneity in Human Disease, by Jon McClellan and Mary-Claire King. (King's lab, incidentally, was the group who discovered via linkage analysis that the gene for early-onset breast and ovarian cancer on chromosome 17q21, nearly 5 years before Myriad Genetics filed for patent protection on the BRCA1/2 genes). Anyhow, looks like a great review on genetic heterogeneity and GWAS. Thanks @JVJAI.

This month's cancer biostatistics workshop on overfitting will be given by Fei Ye and Zhiguo (Alex) Zhao, both in the Department of Biostatistics and the Cancer Biostatistics Center. This looks like a good one, especially after attending Frank Harrell's regression modeling strategies course a few weeks ago. See the link below for the full 2010 series.

Our Program in Computation Genomics Journal Club is starting again, now the 3rd Friday of each month. The next meeting is this Friday, April 16, at 3pm in the CHGR conference room. As usual, please bring in any articles you've found recently and give a brief overview of why you thought it was interesting.