Introduction

Welcome to the tutorials! Here we will learn how to make a computational research project reproducible using several different tools, described in the figure below:

The figure gives an overview of the six available tutorials, a very brief description of their main purpose, and the suggested order to do them. However, each tutorial is made so that it can be completed independently of the other tutorials. It is therefore perfectly possible to choose a different order, or a subset of tutorials that suits your interests. Under the main figure there is a list of a few suggested alternative tutorial orders; you will find the tutorials in the menu to the left.

Before going into the tutorials themselves, we first describe the case study from which the example data comes from, followed by the setup needed to install the tools themselves. These will create quite a lot of files on your computer, some of which will actually take up a bit of storage space too. In order to remove any traces of these after completing the tutorials, please refer to the Take down section.

The case study

We will be running a small bioinformatics project as a case study, and use that to exemplify the different steps of setting up a reproducible research project. To give you some context, the study background and analysis steps are briefly described below.

Background

The data is taken from Osmundson, Dewell, and Darst (2013), who have studied methicillin-resistant Staphylococcus aureus (MRSA). MRSA is resistant to broad spectrum beta-lactam antibiotics and lead to difficult-to-treat infections in humans. Lytic bacteriophages have been suggested as potential therapeutic agents, or as the source of novel antibiotic proteins or peptides. One such protein, gp67, was identified as a transcription-inhibiting transcription factor with an antimicrobial effect. To identify S. aureus genes repressed by gp67, the authors expressed gp67 in S. aureus cells. RNA-seq was then performed on three S. aureus strains:

• RN4220 with pRMC2 with gp67
• RN4220 with empty pRMC2
• NCTC8325-4

Analysis

The graph below shows the different steps of the analysis that are included in this project:

The input files are:

• RNA-seq raw data (FASTQ files) for the three strains
• S. aureus genome sequence (a FASTA file)
• S. aureus genome annotation (a GFF file)

The different steps of the workflow and what they do are as follows:

• get_genome_fasta - Download the genome file.
• index_genome - Index the genome using the Bowtie2 software (required for the alignment step)
• get_SRA_by_accession - Download the RNA-seq raw data for the three strains from the Sequence Read Archive (SRA).
• fastqc - Run quality control on each of the RNA-seq FASTQ files using the FastQC software.
• multiqc - Summarize the quality controls.
• align_to_genome - Align the RNA-seq data from the three strains to the indexed genome using the Bowtie2 software.
• sort_bam - Sort the alignment files by genome coordinate using the Samtools software.
• get_genome_gff3 - Download the genome annotation file.
• generate_count_table - Calculate gene expression by counting aligned reads per gene using the HTSeq-count software.
• generate_rulegraph - Generate the workflow overview figure shown above.
• make_supplementary - Produce the supplementary materials section using data from the quality controls, gene counts and the workflow figure.

Setup for Mac / Linux users

Clone the GitHub repository containing all files you will need for completing the tutorials. First, cd into a directory on your computer (or create one) where it makes sense to download the course directory.

cd /path/to/your/directory
git clone https://github.com/NBISweden/workshop-reproducible-research.git
cd workshop-reproducible-research

Setup for Windows users

There are several different ways to run the course material on a Windows computer. Neither is perhaps optimal, and the material itself has not been adapted specifically for Windows. Nevertheless, in principle everything should be possible to run. A few ways you could setup:

• Use the Linux Bash Shell on Windows 10 (see below) Recommended for the course
• Run the course in a Docker container (see below)
• Run as Linux through a virtual machine (and see the Linux setup above)
• Use the Windows 10 PowerShell, install git and clone the course repository (see the Mac/Linux setup above)

Running in the Linux Bash Shell on Windows 10

This will give you access to a full command-line bash shell based on Linux on your Windows 10 PC. For the difference between the Linux Bash Shell and the PowerShell on Windows 10, see e.g. this article.

Install Bash on Windows 10, following the instructions at e.g. one of these resources:

• Installing the Windows Subsystem and the Linux Bash
• Installing and using Linux Bash on Windows
• Installing Linux Bash on Windows

Open a bash shell Linux terminal and clone the GitHub repository containing all files you will need for completing the tutorials as follows. First, cd into a directory on your computer (or create one) where it makes sense to download the course directory.

cd /path/to/your/directory
git clone https://github.com/NBISweden/workshop-reproducible-research.git
cd workshop-reproducible-research

Using Docker to run the course

Alternatively, you can use Docker to run the course in a Docker container. First, open the Windows 10 PowerShell and cd into a directory on your computer (or create one) where it makes sense to download the course directory. Install Docker by following the instructions in the Docker tutorial. Then run:

cd c:/my_dir
docker run -it -p 8888:8888 -v /c/my_dir:/course/ \
    nbisweden/workshop-reproducible-research:slim

This will start an isolated container running Linux, where the directory c:/my_dir is mounted (i.e. you can access the files in this Windows directory within the Linux container, and files edited or created within the Linux container will appear in this Windows directory). Note that the idea is that you should edit files in the mounted c:/my_dir using an editor in your normal OS, say Notepad in Windows. The terminal in the container is for running stuff, not editing.

You should now be at a terminal in the Docker container. Now clone the GitHub repository containing all the files you will need for the tutorials.

git clone https://github.com/NBISweden/workshop-reproducible-research.git
cd workshop-reproducible-research

Setting up persistent conda environments

Because the root file system of each container is an isolated instance, Conda environments you create during this course will be lost if you exit the container or if it is killed for some reason. This means that you will have to recreate each environment every time you run a new container for the course. To avoid this, you can make sure that Conda uses a subfolder envs/ inside the /course directory for storing environments. If you run containers for the course with some folder on your local machine mounted inside /course that will cause the Conda environments to be available on your local machine even though they are created inside the container. Should a container be stopped for some reason you can simply run a new one and activate Conda environments under /course/envs, saving you the trouble of recreating them.

What you have to do is to, after you've started a container with the docker run command above, first create the envs/ directory:

mkdir /course/envs

Then set the environment variable CONDA_ENVS_PATH to /course/envs:

export CONDA_ENVS_PATH="/course/envs"

Now when you use Conda to create environments inside the Docker container for the course, the environments will be placed in /course/envs which is also present on your local system because you mounted the folder inside the container. Note that you will have to set the CONDA_ENVS_PATH each time you start a new container for this to work.

Don't worry if you feel that this Docker stuff is a little confusing, it will become clearer in the Docker tutorial. However, the priority right now is just to get it running so that you can start working.