This directory contains the visualization code for DRAM. The visualization code is written in Python and uses the Bokeh and Panel libraries. The visualization code is used to generate the figures and dashboards for the DRAM2 gene annotation tool.

The DRAM Visualization Library can be integrated into the DRAM Nextflow pipeline to generate figures in the form of html files. The html files can be viewed in a web browser and can be used to explore the results of the DRAM gene annotation software. This can be incorporated into a larger DRAM Nextflow pipeline or run as a standalone DRAM Nextflow pipeline by by running the nextflow run command:

nextflow run DRAM2.nf --product --annotations <path/to/annotations.tsv> --outdir <path/to/output/directory/>

more options can be found by running:

nextflow run DRAM2.nf --product --help

The DRAM Visualization Library can also be used as a standalone Python package to generate figures and dashboards. First you will need to install the DRAM Visualization Library as a standalone package.

The DRAM Visualization Library can also be used as a standalone Python package to generate figures and dashboards. To install the DRAM Visualization Library stable release, in whatever environment you are using, run:

pip install git+https://github.com/WrightonLabCSU/dram-viz.git

This will install the DRAM Visualization Library and all of its dependencies from the main branch of the GitHub repository.

To install the DRAM Visualization Library as a development package, clone the repository, cd into the repository, create your environment, and run:

pip install -e '.[dev]'

This will install the DRAM Visualization Library and all of its dependencies from the main branch of the GitHub repository, as well as the development dependencies.

We use pre-commit to manage our pre-commit hooks (e.g., linting, formatting that run commit to keep code formatting in check). To install the pre-commit hooks, run:

pre-commit install

now pre-commit will run automatically on git commit, but you can run it manually by running:

pre-commit run --all-files

To generate a figure like the one shown above [TODO: insert figure], run the following command:

python -m dram_viz --annotations <path/to/annotations.tsv> --outdir <path/to/output/directory/>

To launch a dashboard, run the following command:

python -m dram_viz --annotations <path/to/annotations.tsv> --outdir <path/to/output/directory/> --dashboard

This should open your default web browser and display the dashboard. If the dashboard does not open automatically, you can navigate to http://localhost:5006 to view the dashboard.

If you are using the DRAM Visualization Library as a standalone Python package, you can run the dashboard on a remote server and use SSH tunneling to view the dashboard on your local machine. This will allow you to avoid downloading large data files to your local machine. To do this, first launch the dashboard on the remote server by ssh'ing into the server, navigating to the DRAM visualization directory, and running the above dashboard command. Then, on your local machine, run the following command:

ssh -NfL localhost:5006:localhost:5006 <username>@<remote-server>

and navigate to http://localhost:5006 to view the dashboard.

When you are finished viewing the dashboard, you should kill the process on the remote server by hitting Ctrl+C and then locally closing the SSH tunnel by running:

kill $(lsof -ti:5006)