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.

A few weeks ago the 2014 AMIA Translational Bioinformatics Meeting (TBI) was held in beautiful San Francisco.  This meeting is full of great science that spans the divide between molecular and clinical research, but a true highlight of this meeting is the closing keynote, traditionally given by Russ Altman.

I recently found this little gem of a web app that analyzes the clarity of your writing. Hemingway highlights long, complex, and hard to read sentences. It also highlights complex words where a simple one would do, and highlights adverbs, suggesting you use a stronger verb instead. It highlights passive voice (bad!), and tells you the minimum reading grade level necessary to understand your writing.

I'm calling variants from exome sequencing data and I need to evaluate the efficiency of the capture and the coverage along the target regions. This sounds like a great use case for bedtools, your swiss-army knife for genomic arithmetic and interval manipulation.

Software Carpentry is an international collaboration backed by Mozilla and the Sloan Foundation comprising a team of volunteers that teach computational competence and basic programming skills to scientists.

Last month I told you about Coursera's specializations in data science, systems biology, and computing. Today I was reading Jeff Leek's blog post defending p-values and found a link to HarvardX's Data Analysis for Genomics course, taught by Rafael Irizarry and Mike Love. Here's the course description: If you've ever wanted to get started with data analysis in genomics and you'd learn R along the way, this looks like a great place to start.

At the moment, the world is obsessed with “Big Data” yet it sometimes seems that people who use this phrase don’t have a good grasp of its meaning.  Like most good buzz-words, “Big Data” sparks the idea of something grand and complicated, while sounding ordinary enough that listeners feel like they have an intuitive understanding of the concept.

This is one of those things I picked up years ago while in graduate school that I just assumed everyone else already knew about. GNU screen is a great utility built-in to most Linux installations for remote session management. Typing 'screen' at the command line enters a new screen session.

I first mentioned Coursera about a year ago, when I hired a new analyst in my core. This new hire came in as a very competent Python programmer with a molecular biology and microbial ecology background, but with very little experience in statistics.