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Installing the Workflow for Another Project

These instructions are for if you've already run the tucca-rna-seq workflow but want to start a new project or analysis without affecting your previous work, which is crucial for maintaining reproducibility.

1. Login to the Tufts HPC​

SSH into the Tufts HPC through the command line at a UNIX-like terminal on your laptop or desktop or you can access the cluster through Tufts' OnDemand service. You will need a Tufts Cluster Account to be able to log in to the Tufts HPC.

2. Get off of a login node​

Once logged into the Tufts HPC run the following to get off a login node and onto a compute node so that you can start performing computations.

After login you are on a Login Node of the cluster (login-prod-0x) and in your Home Directory (~ or /cluster/home/your_utln). Your terminal should look like this:

$ [your_utln@login-prod-02 ~]

To get off a login node run:

srun -p interactive --pty bash

This will allocate a compute node with bash shell, 1 CPU core (default), 4 hours (interactive partition default and maxmimum value), and 2GB of CPU memory (default). If desired, modify with number of CPU cores -n , memory --mem= , an alternative partition -p , and/or runtime --time= .

Depending on the current usage of the Tufts HPC, you may need to wait in a queue to be allocated enough resources. This could take some time depending on the current usage of the cluster.

3. Clone the repository​

Go to the desired directory (aka folder) on your file system on the Tufts HPC. Then clone (aka copy) the repository and move into the respective directory with:

git clone https://github.com/benjibromberg/tucca-rna-seq.git
tip

For Kaplan Lab users, it is recommended to clone the repository into your personal directory in the kaplanlab workspace. After cloning the repo, you can change the name of the cloned directory to suit your project. Refer to the Tufts HPC resources linked above if this does not make sense.

4. Dependencies installation​

For improved reproducibility and reusability of the workflow, each individual step of the workflow runs either in its own Conda virtual environment. As a consequence, running this workflow has very few individual dependencies.

You will need to add the bioconda channel to your base conda environment which loads everytime you load a conda manager like miniforge. If this is problematic for some reason, you will need to load another conda environment that includes the conda-forge, bioconda, and default channels before loading the snakemake conda environment which will require manually modifying the run.sh runner script.

To add the bioconda channel to your base conda environment run:

module load miniforge/24.7.1-py312
conda activate base
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict
conda config --show channels

The result of the final command should match the following:

channels:
  - conda-forge
  - bioconda
  - defaults

Then run the following to create the snakemake conda environment:

conda env create -f tucca-rna-seq/install/snakemake.yaml