Like your .bashrc, .vimrc, or many other dotfiles you may have in your home directory, your .Rprofile is sourced every time you start an R session. On Mac and Linux, this file is usually located in ~/.Rprofile. On Windows it's buried somewhere in the R program files. Over the years I've grown and pruned my .Rprofile to set various options and define various "utility" functions I use frequently at the interactive prompt.

Automatically Archiving Twitter Results Ever since Twitter gamed its own API and killed off great services like IFTTT triggers, I've been looking for a way to automatically archive tweets containing certain search terms of interest to me. Twitter's built-in search is limited, and I wanted to archive interesting tweets for future reference and to start playing around with some basic text / trend analysis.

I frequently get asked to recommend workshops or online learning resources for bioinformatics, genomics, statistics, and programming. I compiled a list of both online learning resources and in-person workshops (preferentially highlighting those where workshop materials are freely available online): List of Bioinformatics Workshops and Training Resources I hope to keep the page above as up-to-date as possible.

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.

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

If you need to catch up on all those years you spent not learning how to code (you need to know how to code), here are a few resources to help you quickly learn R and Python, and have a little fun doing it. First, the free online Coursera course Computing for Data Analysis just started.

Update (Dec 18, 2012): Please see this related post I wrote about differential isoform expression analysis with Cuffdiff 2. DESeq and edgeR are two methods and R packages for analyzing quantitative readouts (in the form of counts) from high-throughput experiments such as RNA-seq or ChIP-seq.