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.

I've seen this question asked and partially answered all around the web. As with anything related to Perl, I'm sure there is more than one way to do it. Here's how I do it with Perl 5.10.1 on CentOS 6.4. First, install local::lib with bootstrapping method as described here.

Jeff Leek, biostats professor at Johns Hopkins and instructor of the Coursera Data Analysis course, recently posted on Simly Statistics this list of awesome things other people accomplished in 2013 in genomics, statistics, and data science. At risk of sounding too meta , I'll say that this list itself is one of the awesome things that was put together in 2013.

Boston-based startup Curoverse has announced $1.5 million in funding to develop and support the open-source Arvados platform for cloud-based bioinformatics & genomics data analysis. The Arvados platform was developed in George Church's lab by scientists and engineers led by Alexander Wait Zaranek, now scientific director at Curoverse.

Obi Griffith over at Biostar put together this excellent cheat sheet for dealing with one-based and zero-based genomic coordinate systems. The cheat sheet visually explains the difference between zero and one-based coordinate systems, as well as how to indicate a position, SNP, range, or indel using both coordinate systems.

One of the most powerful tools you can learn to use in genomics research is a relational database system, such as MySQL.  These systems are fairly easy to setup and use, and provide users the ability to organize and manipulate data and statistical results with simple commands.  As a graduate student (during the height of GWAS), this single skill quickly turned me into an “expert”.