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Two possibilities:
A lot of widely-used programs have been installed globally (BLAST, tophat, bowtie, samtools, …)
You are encouraged to install the software that you need into your own home directory (or possibly into a shared directory for the project you are working on). This gives you control of the versions of the software you are using, and when upgrades are made. This is good for reproducibility of results. You can use Conda to install specific versions of Python or R, and associated packages into your own home directory. Conda can also install many bioinformatics programs (e.g. STAR, bwa etc.) through the bioconda channel. You can also look at using a (Singularity) container to encapsulate a project or software pipeline - see Using Singularity.
You can install miniconda by downloading the installation script here: https://docs.conda.io/en/latest/miniconda.html.
apt-get won’t do it.
Possibilities:
Since your home directories are available on all nodes, any program you install locally will automatically be available to you on the compute nodes. (This is not always true with apt-get installations, as they sometimes go into /usr/bin which is not shared across nodes.)
This can be quite simple, or a nightmare of dependencies.
cd source_dir make
./configure -prefix=$HOME make install
apt-get source package-name tar -zxf package-name.tar.gz cd package-dir ./configure -prefix=$HOME make install
You can install conda in your own home directory and then use it to install a specific version of python or R (for instance).
For instance, to install R 3.6.1 (currently):
conda create -n r-test r-base r-tidyverse r-rlang ... conda activate r-test
Using a Singularity container allows you to have complete control over your environment and what is installed, and allows you to share that environment with others, but does require a little more effort than other installation techniques.
See the page on Using Singularity.