This repository was developed as an educational tutorials/workshops for the Applied Bioinformatics course (3030NSC/7104ESC) offered at Griffith University (see course details). The tutorials cover basic bioinformatics skills taught in the course, particularly those based on command-line tools and R (for example retrieval of information and sequences from NCBI databases, multiple sequence alignment and phylogeny inference, differential gene expression analysis and visualisation in R using DESeq2, etc.).
The teaching material for the workshops were developed by Dr. Alex Cristion ([email protected]) and Dr. Ido Bar ([email protected]) and prepared as a Binder repository.

The tutorial is designed to be run live and interactively in class either locally on the students’ computers or using Binder through Jupyter and RStudio servers (see instructions below).

The Jupyter Notebook is an open source web application that you can use to create and share documents that contain live code (supporting over 40 computing languages, including Python, R, Julia, Linux Bash and many more), equations, visualizations, and text. Jupyter Notebook is maintained by the people at Project Jupyter. You can learn more about Jupyter Notebooks in the official documentation and in Jupyter Notebook: An Introduction by Mark Driscoll

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

RStudio is a set of integrated tools designed to help you be more productive with R. It includes a console, syntax-highlighting editor that supports direct code execution, and a variety of robust tools for plotting, viewing history, debugging and managing your workspace. It requires R to be installed prior to be able to send commands to the interpreter.

If we want to keep things simple (for this course) or we would like to use R on shared computers, where we can’t install software, we can run R and Rstudio through a web client that is hosted on a remote server.
We will use the Binder service, which is free, easy to use and can be launched from a single GitHub repository (more about this in the workshop).

Using Binder is as simple as clicking on the Binder badge - and choosing the appropriate server (Jupyter, RStudio or Terminal).
Alternatively, you can navigate to the Binder homepage and enter the URL of this tutorial GitHub repository https://github.com/IdoBar/3030NSC-workshops-binder.git and click on the launch button (see screenshot in Figure 1 below).

Figure 1: Binder launch screenshot.

Now be patient while the environment is loading…
You should now see in your web browser the Binder interface, where you can choose the server to work with (Jupyter, Terminal or RStudio) and you can start working in “The Cloud”!

We can upload and download files to the Binder environment using the file explorer on the left of the interface (use the button to upload files and right click on a file/folder and select “Download” to download it, see screenshot in Figure 2 below).

Figure 2: Binder main screen and file access screenshot.

If we are using RStudio, we can download any output files (summary tables and figures) by using the files tab in RStudio (bottom right pane).
Select the files/folders that you would like to download and click on More Export… (see screenshot in Figure 3 below) to save the file on your computer.

Figure 3: Download files from Binder/RStudio screenshot.

For more details and instructions how to setup a similar repository, please visit From Zero to Binder in R!