Changes to laboratory protocols need to be kept track of. Documenting who made a change and why is important to ensure reproducibility of results. It also empowers people who think of improvements to a protocol to communicate those changes to the other users.

This post began as a simple question: “How do I make this script I just wrote accessible on the command line?” The answer took us on a journey. Along the way, we learned a lot about what exactly the “command line” is, and how it’s useful for humans doing research and engineering in biology.

Computation has become a central part of biology. These days, if you have biological questions, you are more than likely going to use computers, sequencing, and laboratory automation to answer them.

As someone who starting computing as a teenager in 2000 with the “public beta” of Mac OS X, my first shell was Bash. Seventeen years later, I still use Bash every day.

Computation has become a central part of biology. These days, if you have biological questions, you are more than likely going to use computers and laboratory automation to answer them.

It’s well known that the Unix shell allows for some amazing feats of brevity. But did you know it was possible to perform diffs on designed protein structures, transcribe DNA to RNA, reverse complement DNA sequences, and perform other computational biology feats from your command line? What follows is some fun with Unix in computational biology.

The use of generators is an intermediate programming topic that comes up again and again in biological codebases. In short, a generator lets you store some code to run later. In biological codebases, generators are excellent for times when you would like to go through all the elements in some collection and operate on all of them, and then collect the result of all the operations into another list.

As a biologist who learned how to code to solve my problems and answer biological questions, here’s some advice to those like me: don’t learn outdated technology.

Rosetta’s sampling methodology allows searching protein sequence functional space (which is extremely rugged). To effectively use Rosetta, high performance computing (cluster computing) is necessary to carry out many sampling trajectories in parallel.