Background This tutorial shows an example of RNA-seq data analysis with DESeq2, followed by KEGG pathway analysis using GAGE. Using data from GSE37704, with processed data available on Figshare DOI: 10.6084/m9.figshare.1601975.

I work with gene lists on a nearly daily basis. Lists of genes near ChIP-seq peaks, lists of genes closest to a GWAS hit, lists of differentially expressed genes or transcripts from an RNA-seq experiment, lists of genes involved in certain pathways, etc. And lots of times I’ll need to convert these gene IDs from one identifier to another. There’s no shortage of tools to do this. I use Ensembl Biomart.

I just returned from the Genome Informatics meeting at Cold Spring Harbor. This was, hands down, the best scientific conference I've been to in years. The quality of the talks and posters was excellent, and it was great meeting in person many of the scientists and developers whose tools and software I use on a daily basis.

Per tradition, Russ Altman gave his "Translational Bioinformatics: The Year in Review" presentation at the close of the AMIA Joint Summit on Translational Bioinformatics in San Francisco on March 26th.  This year, papers came from six key areas (and a final Odds and Ends category).  His full slide deck is available here.

Last week I taught a three-hour introduction to R workshop for life scientists at UVA's Health Sciences Library. I broke the workshop into three sections: In the first half hour or so I presented slides giving an overview of R and why R is so awesome. During this session I emphasized reproducible research and gave a demonstration of using knitr + rmarkdown in RStudio to produce a PDF that can easily be recompiled when data updates.

A couple of months ago I posted about how to visualize exome coverage with bedtools and R. But if you're looking to get a basic handle on genome arithmetic, take a look at Aaron Quinlan's bedtools tutorials from the 2013 CSHL course.

Two years ago David Searls published an article in PLoS Comp Bio describing a series of online courses in bioinformatics. Yesterday, the same author published an updated version, "A New Online Computational Biology Curriculum," (PLoS Comput Biol 10(6): e1003662.

I've been asked a few times how to make a so-called volcano plot from gene expression results. A volcano plot typically plots some measure of effect on the x-axis (typically the fold change) and the statistical significance on the y-axis (typically the -log10 of the p-value). Genes that are highly dysregulated are farther to the left and right sides, while highly significant changes appear higher on the plot.

Three years ago I wrote a blog post on how to create manhattan plots in R. After hundreds of comments pointing out bugs and other issues, I've finally cleaned up this code and turned it into an R package.

For a few years now, my EvoSTAR colleague, Bill Langdon, has been exploring the degree to which Mycoplasma bacteria have contaminated experimental systems and even "infected" online databases with the contents of their genomes.