This blog is really just a place holder for my notes on using Git, which is probably the most widely used version control system. I swapped over from using CVS a while ago and, while I love Git, it can be a little tricky to get used to.

For my remote repositories I tend to use GitLab, because when I initially develop projects I like to keep them private, and GitLab offers free private accounts.

To start a new project you can either set up a repository on the website of your favourite remote repository (such as GitLab) and use ‘git clone’ to copy the repository to your local machine or you can create a repository locally (by moving to the directory where you store your files and using ‘git init’) and then push the repository (‘git push’) to copy it to the remote repository.

What can be a little confusing is that locally, while there is only one physical copy of your file, there are indexes that track if a file has been ‘added’ to a staging area or ‘committed’ to your local repository. This means that there is a two-step commit… you ‘git add’ to move a file to the staging area and ‘git commit’ to move it to the local repository. A ‘git push’ will move any committed files to your remote repository, where they can be accessed by collaborators.

While I’m sure I don’t always follow this advice, using a coding style guide makes sense and was something I always stressed was essential when working in teams. Even when you are working on your own using a coding style guide generates code that is much easier to maintain.

It turns out that Google publishes their own style guide, I quite like them and so I’ve published the links to them below (just the ones I’m most likely to use). All I was really missing was a style guide for Matlab and Julia so I’ve added a link to Matlab and Julia.org’s own.

Google encourages the use of TODO comments in code, a nice and consistent way of marking areas of code that need improving (TODO(user)).

R style guide

Shell style guide

Python style guide

Matlab style guide

Julia style guide

While biological studies are rapidly generating a clear picture of the key components involved in intracellular pathways they often lack  details of how these components interact.  Understanding these interactions is essential if we want to understand how a cell reacts to its environment. Mathematical modelling is a great to fill in these details but the problem is that high quality mathematical models need to be inferred from high quality data!

Traditionally experimental data that describes subcellular signalling events such as protein phosphorylation comprises few time points and are not quantified. Our recent paper (DOI: 10.1371/journal.pcbi.1004589) details a way of quickly obtaining the necessary high-density quantified data making it easier to infer mathematical models.