AAAS and the Food and Drug Law Institute (FDLI) are holding three 2-day meetings in Washington DC on personalized medicine, addressing the scientific discoveries, business models, and policy changes that are necessary to develop personalized treatments and diagnostics. All three meetings will be available via a free live webcast where viewers can use response technology to interact with speakers and session chairs.
A tip of the hat to Lifehacker for pointing this out. Ever been nailed by a reviewer or a thesis committee for using too many acronyms without defining them well? There's an easy way built into MS Word to find and highlight all the acronyms in a document. It's a nice check after you're done with a first draft, and you might be surprised by how many you find!
Jason Moore at the previously mentioned Epistasis Blog has begun compiling a list of 100 papers every grad student should read, broken down by discipline. Right now the list is in its infancy, but it's a good start. I'll post here when the list is updated again. 100 Publications Every Graduate Student Should Read UPDATE 2009-05-08: The list has grown substantially since yesterday.
Here's a paper in Nature Reviews Genetics highlighting two recently published methods for mapping regulatory sites. Most currently used procedures rely on ChIP, where you can only examine sites one protein at a time.
Last week I posted a short tutorial on how to merge datasets using R. R is a free and open-source statistical computing software and programming language (get R here). The only downside is a steeper learning curve because the documentation is sparse and often difficult to understand at first.
OpenHelix offers free access to a handful of their tutorials on genomics and bioinformatics resources, including the UCSC Genome Browser, Seattle SNPs, Genome Variation Server, and the VISTA Comparative Genomics tools.
An international team led by Sarah Tishkoff, in collaboration with our own Scott Williams, and former CHGR member Jason Moore, published yesterday in Science the largest, most comprehensive characterization of genetic variation in over 100 different African populations.
A reader asked yesterday how you would merge data from two different files. For example, let's say you have a ped file with genotypes for individuals, and another file that had some other data for some of the individuals in the pedfile, like clinical or environmental covariates.
From a News and Views article in Nature Genetics, following up the recently mentioned sequencing effort for X-linked mental retardation: Nature Genetics: X-cess of variants in XLMRGetting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.
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.
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.