A couple of weeks ago I, with the help of others here at UVA, organized a Software Carpentry bootcamp, instructed by Steve Crouch, Carlos Anderson, and Ben Morris. The day before the course started, Charlottesville was racked by nearly a foot of snow, widespread power outages, and many cancelled incoming flights. Luckily our instructors arrived just in time, and power was (mostly) restored shortly before the boot camp started.

Metagenomics is the study of DNA collected from environmental samples (e.g., seawater, soil, acid mine drainage, the human gut, sputum, pus, etc.). While traditional microbial genomics typically means sequencing a pure cultured isolate, metagenomics involves taking a culture-free environmental sample and sequencing a single gene (e.g. the 16S rRNA gene), multiple marker genes, or shotgun sequencing everything in the sample in order to

Many of you may be familiar with WebGestalt, a wonderful web utility developed by Bing Zhang at Vanderbilt for doing basic gene-set enrichment analyses. Last year, we invited Bing to speak at our annual retreat for the Vanderbilt Graduate Program in Human Genetics, and he did not disappoint! Bing walked us through his new tool called NetGestalt.

If you ever write code for scientific computing (chances are you do if you're here), stop what you're doing and spend 8 minutes reading this open-access paper: Wilson et al. Best Practices for Scientific Computing.

Two of the most common questions at the beginning of an RNA-seq experiments are "how many reads do I need?" and "how many replicates do I need?". This paper describes a web application for designing RNA-seq applications that calculates an appropriate sample size and read depth to satisfy user-defined criteria such as cost, maximum number of reads or replicates attainable, etc.

For 18 years now, computational biologists have convened on the beautiful islands of Hawaii to present and discuss research emerging from new areas of biomedicine. PSB Conference Chairs Teri Klein (@teriklein), Keith Dunker, Russ Altman (@Rbaltman) and Larry Hunter (@ProfLHunter) organize innovative sessions and tutorials that are always interactive and thought-provoking.

It's happened to all of us. You read about a new tool, database, webservice, software, or some interesting and useful data, but when you browse to http://instititution.edu/~home/professorX/lab/data, there's no trace of what you were looking for. THE PROBLEM This isn't an uncommon problem. See the following two articles: The first gives us some alarming statistics.

I've said it before: Twitter makes me a lazy blogger. Lots of stuff came across my radar this week that didn't make it into a full blog post.

Coursera's free Computing for Data Analysis course starts today. It's a four week long course, requiring about 3-5 hours/week. A bit about the course: There are also hundreds of other free courses scheduled for this year. While the Computing for Data Analysis course is more about using R, the Data Analysis course is more about the methods and experimental designs you'll use, with a smaller emphasis on the R language.

Here are links to all of this year's posts (excluding seminar/webinar announcements), with the most visited posts in bold italic . As always, you can follow me on Twitter for more frequent updates. Happy new year!

In case you missed it, a new paper was published in Nature Biotechnology on a method for detecting isoform-level differential expression with RNA-seq Data: Trapnell, Cole, et al. "Differential analysis of gene regulation at transcript resolution with RNA-seq." Nature Biotechnology (2012). THE PROBLEM RNA-seq enables transcript-level resolution of gene expression, but there is no proven methodology for simultaneously accounting for