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

This is an issue that has bugged me for years, and I've finally found a good solution on osxdaily and Stack Overflow.

Many papers have noted the challenges associated with assigning function to non-coding genetic variation, and since the majority of GWAS hits for common traits are non-coding, resources for providing some mechanism for these associations are desperately needed.