For most use cases, we recommend that you install and use the Conda package manager to install software on GenomeDK. Conda provides access to thousands of software packages and is easy to get started with.
For more advanced use cases, or where there’s a substantial need for reproducibility, we recommend Apptainer, which is also supported on GenomeDK.
You can of course also compile software yourself, but you must provide all of the necessary dependencies (compilers, libraries) for the build, e.g. using Conda.
Conda can install any kind of software. This means that your entire setup can be installed through Conda (if there’s packages for it all). For example, you can create an environment with Rstudio, R, and ggplot2 with a single command.
Conda provides access to thousands of packages used in data science and bioinformatics. These packages can be installed with a single command, so you don’t have to worry about compilers, dependencies, and where to put binaries.
Downloading and installing Conda is very simple, you just download and run the installer:
[fe-open-01]$ wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -O miniforge.sh
[fe-open-01]$ chmod +x miniforge.sh
[fe-open-01]$ bash miniforge.sh -b
[fe-open-01]$ ./miniforge3/bin/conda init bash
That’s it! The last two step makes sure that Conda will be available when you log in, so now is a good time to open a new connection and check that Conda is available.
Now let’s configure Conda to make it super useful.
Conda can install packages from different channels. This is similar to repositories in other package managers. Here we’ll add a few channels that are commonly used in bioinformatics:
[fe-open-01]$ conda config --append channels bioconda
[fe-open-01]$ conda config --append channels genomedk
[fe-open-01]$ conda config --set channel_priority strict
Conda creates a base environment which contains Conda itself. It’s tempting to install packages in base, but that might ruin your Conda installation. You should never install anything in the base environment.
To prevent that you accidentially install something in the base environment, we’ll configure Conda so that it doesn’t activate it when you log in:
[fe-open-01]$ conda config --set auto_activate_base false
Once you have done these steps, you should have a config file in your home folder called .condarc that looks like this:
[fe-open-01]$ cat $HOME/.condarc
channels:
- conda-forge
- bioconda
- genomedk
channel_priority: strict
auto_activate_base: false
You can easily search for Conda packages through the website anaconda.org or using the conda search command:
[fe-open-01]$ conda search samtools
Remember that the Conda package may not be called the exact official name of the software. For example, the Conda package for the software biobambam2 is just called biobambam, so searching for biobambam2 would not return any results.
If you can’t find a suitable Conda package, contact us and we will build a Conda package for you (when possible). Sometimes building a Conda package is not viable and in that case we will build a Singularity/Apptainer image instead.
Here is how the usage might look if we want to create a new environment with the newest version of PySAM:
[fe-open-01]$ conda create -n amazing-project pysam
This gives us a clean environment with just the minimal number of packages necessary to support PySAM. To use the software that was installed in the environment, the environment needs to be activated first:
[fe-open-01]$ conda activate amazing-project
(amazing-project) [fe-open-01]$ python -c 'import pysam; print(pysam.__version__)'
0.6.0
Notice that the prompt changed to show you that you’re now in the amazing-project environment.
You can install further packages in the environment with:
(amazing-project) [fe-open-01]$ conda install r-ggplot2
Since Conda knows about the entire environment you created, it can tell you exactly which packages are used in the environment. This is very useful for collaborating with others, since your collaborators can create an exact copy of your environment with a single command.
To export your environment so that others can recreate it:
(amazing-project) [fe-open-01]$ conda env export > environment.yml
The environment.yml file contains an exact specification of your environment and the packages installed. You can put this in your shared project folder. Others will then be able to recreate your environment by running:
[fe-open-01]$ conda env create -f environment.yml
Apptainer is a container technology for HPC that used to be called “Singularity”. If you’re familiar with Docker, Apptainer will seem familiar and Apptainer can convert most Docker images to its own (SIF) format and run them without issues.
There’s a multitude of repositories for Docker/Apptainer images:
Apptainer is already installed and configured on GenomeDK, and you should be able to pull and run containers without any further setup.
[fe-open-01] apptainer pull docker://biocontainers/blast:2.2.31
This will pull the Docker image for BLAST and convert it to SIF, so it may take a while. In this case, the image will be put in your current working directory as blast_2.2.31.sif.
Be aware that you should pull and convert images once before submitting jobs. That is, never put apptainer pull in a job script.
The images are quite large, so consider putting them in a relevant project folder.
You can now run a command inside the image:
[fe-open-01] apptainer run blast_2.2.31.sif blastp -version
blastp: 2.2.31+
Package: blast 2.2.31, build Apr 23 2016 15:49:47
You can of course do this in job scripts also.
Apptainer supports the use of GPUs in containers, for example:
[fe-open-01] apptainer pull docker://nvcr.io/nvidia/tensorflow:23.08-tf2-py3
Then, on a GPU node (either in an interactive or batch job):
[gn-1001] apptainer run --nv tensorflow_23.08-tf2-py3.sif python3 mnist_classify.py
Note the use of the --nv flag.
If you need to compile a piece of software that is supposed to use GPUs you most likely have to do it in a job on one of the GPU nodes, since headers required for compilation are only located there.
You can get a list of GPU nodes with:
[fe-open-01] sinfo -p gpu -N
NODELIST NODES PARTITION STATE
gn-1001 1 gpu mix
gn-1002 1 gpu alloc
Headers and libraries for compilation are located in /usr/local/cuda/targets/x86_64-linux.
Read more about how to submit jobs for the GPU nodes here.