# Bioinformatics Zen

## How I develop

// Fri July 24 2015

I've been in the bioinformatics field for almost 10 years, originally coming from a molecular biology degree background, I deciding to move into computing after struggling to find a job doing lab work. This post is a general outline of how I now develop since then.

## Programming languages

I began coding around 10 years ago during my bioinformatics master's degree when I was was writing Java using Eclipse on Debian. I've never been too keen on Java because of the verbosity of classes and getter/setters and so forth.

During my PhD I switched to learn Ruby. This was certainly a revelation after several years writing Java. The ability to dynamically define methods on classes or dynamically look up missing methods combined with a simple syntax felt very liberating. I used Ruby for several years and it's still my choice when I want a scripting language. I wrote a Ruby program to help finish and submit genome sequences which I used during my postdoc. I'm still proud of this because I used all modern software development practices - though afterwards I did question the value of good scientific software.

These days I've also learnt Python, Haskell, Clojure and Erlang. I try use which ever language seems the most appropriate for the task given their different strengths and weaknesses. This however tends to be mostly bash for speed of development or Python for collaborative projects, this is ironic as I quite dislike python. I think this dislike comes from already gotten used to the advantages of functional languages or lisps. In Python I miss the strong generalised algebraic data typing of Haskell, or elegant macros such as -> in Clojure, which are both aspects of what makes these languages a pleasure to use. I don't think you can really avoid python when working in bioinformatics, but the libraries functools, pymonad and itertools can help to make it more functional.

I also use R regularly for modelling and statistics. I used to hate R, however the libraries ggplot2, dplyr, tidyr, readr, stringr and magrittr make it feel like working in a different, more usable language. The amazes me because almost all were created by one person - Hadley Wickham.

## Development environment

My development workflow is centred around using my ~/.dotfiles running on Mac OSX. I use vim as my editor, tmux for a window manager and zsh as the shell. I love using vim and I think I wouldn't have had such a enjoyable career so far if I hadn't picked it up, and then subsequently spent the time tuning and tweaking it. A good habit I've gotten into is to consciously notice pain points when I'm coding and try to create a shell or editor shortcut to address it. I first created my dotfiles repository 7 years ago and it has over 1,000 commits on it since then. I've since found Tim Pope's vim sensible, and cleared out many of my vim settings in favour of these sensible defaults.

I try to automate everything as much as possible. I hate doing manual tasks more than a couple of times. Being forced, such as by infrastructure or tooling, is something that I find the most annoying. As an example of automation, I have a script called gitme which clones a git repository, switches to the last committed git branch, splits and arranges tmux windows, bootstraps the project as I describe below, and finally starts an autotesting script. This script allows me to pull and immediately start working on a cloned project.

## Focus

Trying to focus and avoid distractions is very important to me. I feel that an unbroken period of 3-4 hours is very valuable for working on projects. I try to make the most of these periods and what I've felt has worked for me:

• The self control app is excellent. This allows you to block a list of websites for a set period of time, and there's no way to unblock them until the time is up. I use this to block email, twitter and hacker news. Twitter and email are terrible distractions to getting anything done.

• Getting up very early. I built nucleotid.es by getting up at 6am every morning for about 6 months. An average of 2-3 hours of time everyday allowed me to create the prototype site to prove that this could be useful. These hours in the morning feel like my most productive and usually there's no one else awake to interrupt you.

## My computer is a cache of what I'm working on

I am very strongly in favour of developer parity. This is the idea that my development, staging and production environments should all be identical. For example if something works when I test it on my laptop and then fails when I test it on the server then my environments are not identical. I want developer parity so that I can spend my time improving code rather than debugging differences between environments.

To this end, I treat my laptop as a cache of what I'm working on. I have a launchctl script that deletes and then recreates the directory ~/cache every time I turn my laptop on. Any projects I git clone into this directory will be purged at the end of the day. This therefore forces me to push feature branches and tidy up my projects before I finish for the day.

As a result of this all my development projects now contain all their own setup and configuration. Each project includes a command to be able to bootstrap all the resources required to start work on it. Examples of this are using virtualenv to set up third party libraries, or fetching a set of S3 resources for a website.

Most importantly this bootstrap command is the same for developing on my computer, deploying on a server, or running on the continuous integration server. This means that if it works on my computer it should work everywhere. If it doesn't then I get email immediately for the CI server that something has has been broken.

## Every project I work on has the same development CLI

I structure all my projects the same way where they all have the same named scripts that perform similar actions. These are located in the directory ./script and are as follows:

• bootstrap: all initial setup required the first time after cloning a project. This might be downloading data from S3 or setting up third-party libraries with bundler or cabal. After running this script I should be able to run or build this project.

• test: run unit tests on the code. Pretty straight forward as you expect. These are all the tests I run to help me develop, i.e. catching individual breaking changes to the code as I write them. This should a return a non-zero exit code if the tests fail.

• feature: run features tests, usually in cucumber, to test the user facing interface of the project. This should catch any changes that break the stack as a whole. Increasingly as I create micro services using docker, I'll rebuild the Docker image then run the feature tests against a container of this image. This insures and breaking changes to the Dockerfile, libraries or code are caught as early as possible.

• build: build whatever this project is about: compile a binary, a docker image or the set of webpages.

• autotest: start a script that watches for any file changes to the code then reruns the test script. This is extremely useful for seeing breaking changes immediately, as you're coding. Tools like inotify make this straight forward to set up.

• autofeature: the same as autotest but runs feature instead. Again this is very handy for catching breaking changes quickly as long my features will run in a few seconds. Even when they don't, I use cucumber tags to subset the quick features, or run only the feature file relevant to what I'm working on.

The advantage of this is that I know that every project had the same entry points. Even if I haven't worked on something in years, I know I can clone it, then bootstrap it and start the autotest script.