There's a new kid on the block for RNA-seq alignment. Dobin, Alexander, et al. "STAR: ultrafast universal RNA-seq aligner." Bioinformatics (2012). Aligning RNA-seq data is challenging because reads can overlap splice junctions. Many other RNA-seq alignment algorithms (e.g. Tophat) are built on top of DNA sequence aligners.

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.

Recently published in Nucleic Acids Research: F. Zambelli, G. M. Prazzoli, G. Pesole, G. Pavesi, Cscan: finding common regulators of a set of genes by using a collection of genome-wide ChIP-seq datasets., Nucleic acids research 40 , W510–5 (2012). Cscan web interface screenshot This paper presents a methodology and software implementation that allows users to discover a set of transcription factors or epigenetic modifications that

The 20th annual ISMB meeting was held over the last week in Long Beach, CA. It was an incredible meeting with lots of interesting and relevant talks, and lots of folks were tweeting the conference, usually with at least a few people in each concurrent session. I wrote the code below that uses the twitteR package to pull all the tweets about the meeting under the #ISMB hashtag. You can download that raw data here.

Thanks to the excellent work of Lucia Hindorff and colleagues at NHGRI, the GWAS catalog provides a great reference for the cumulative results of GWAS for various phenotypes.  Anyone familiar with GWAS also likely knows about dbGaP – the NCBI repository for genotype-phenotype relationships – and the wealth of data it contains.

The ENCODE project continues to generate massive numbers of data points on how genes are regulated.  This data will be of incredible use for understanding the role of genetic variation, both for altering low-level cellular phenotypes (like gene expression or splicing), but also for complex disease phenotypes.  While it is all deposited into the UCSC browser, ENCODE data is not always the easiest to access or manipulate.

A few folks have asked me how I get my news and stay on top of what's going on in my field, so I thought I'd share my strategy.

I'm a huge supporter of the Free and Open Source Software movement. I've written more about R than anything else on this blog, all the code I post here is free and open-source, and a while back I invited you to steal this blog under a cc-by-sa license. Every now and then, however, something comes along that just might be worth paying for.

A few weeks ago I showed you how to convert gene IDs with BioMart. Yesterday I hosted a workshop on the Ensembl Genome Browser, given by Dr. Bert Overduin from EBI-EMBL. He gave several examples of very useful tasks that you can do very quickly and easily using BioMart. One, in particular, is something that I'm doing for a client in the core right now.