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.

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.

About a year ago I wrote a post about producing scatterplot matrices in R. These are handy for quickly getting a sense of the correlations that exist in your data. Recently someone asked me to pull out some relevant statistics (correlation coefficient and p-value) into tabular format to publish beside a scatterplot matrix.

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.

I saw this plot in the supplement of a recent paper comparing microarray results to RNA-seq results. Nothing earth-shattering in the paper - you've probably seen a similar comparison many times before - but I liked how they solved the overplotting problem using heat-colored contour lines to indicate density. I asked how to reproduce this figure using R on Stack Exchange, and my question was quickly answered by Christophe Lalanne.

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.