Mpox Knowledge Graph

The Mpox KG is built using viral and human proteins reported in different resources. Additionally, the KG represents chemicals tested against Monkeypox and their targets, associated biological processes, molecular functions, diseases and side effects.

KG status

Version 1 stats:

• Number of Nodes: 8235
• Number of Edges: 40422

Version 2 stats (2nd September) :

• Number of Nodes: 9129
• Number of Edges: 44568

Version 3 stats (15th Jan):

• Number of Nodes: 9117
• Number of Edges: 44317

Running KG workflow and visualization

Method 1: Using mybinder (No pre-installations required, recommended for non-programmers)

Thanks to mybinder.org. The resources are hosted publicly facilitating users to instantly access the KG. Please go to: https://mybinder.org/v2/gh/Fraunhofer-ITMP/mpox-kg/HEAD. The loading might take some time. Please be patient. For optimal experience, once the resources are loaded, from the list of headers at the top of the page (File, Edit,..Setting, Help), go to Help -> Launch Classic Notebook. Note: This is also important to get the graph visualized, else it might not work. Once done, you can open the ipynb file and execute the cells.

Method 2: Using local setup (Pre-installations required, intended for researchers with advanced programming skills)

Pre-requisite 1: Installation of python 3 and jupyter notebook (Please skip it if you already have these)

We recommend installing anaconda (https://www.anaconda.com/) which will automatically have python pre-installed whereas jupyter notebook can be installed with a click.

a) Next step would be to create a dedicated environment within anaconda to run the scripts. To do this, open anaconda. You'll be able to see HOME at the left of the screen.

b) Underneath HOME, you'll see Environments. Click it.

c) At the bottom of the page, you'll see 'Create' with a '+' sign. Click it.

d) You can now give a name for you environment, say: mpox. Switch the version of python to 3.10 and click create. It will take few minutes.

e) Once done, you can return to 'Home'. You should see in the middle of screen that 'Applications on' will be 'mpox'. However, please remember that next time you open anaconda, you will have to select 'mpox' again because the environment will be set to 'base (root) by default'.

f) In the list of available applications, find Jupyter Notebook and click install. We will launch it later. That's it!!

Pre-requisite 2: Cloning this github repository with your pc

This step is to fetch everything from this repo to your pc so that you can run the scripts. FYI: The files are not so big and are safe to be stored in your pc. See (https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository) to learn how to clone. Alternatively, you can download a zipped file for this repo and unzip it (Not recommended as each updates have to be downloaded manually).

Running Jupyter Notebook

Once you have fulfilled the pre-requisites you can proceed to generate KG on your own. Make sure you have anaconda open, environment set to 'mpox'. Now you can launch the Jupyter Notebook. Browse to the folder where you have cloned/unzipped this repo previously. Open the graph.ipynb from this folder for understanding step wise process of KG generation and KG statistics. The KG has been exported to formats such as graphml, sif and so on for visualizations in other platforms. For example, the graphml file can be imported to Cytoscape directly. These files are located in 'data\export' folder.

Dissemination and deployment

1. The KG is registered in EMBL-EBI's BioModels (https://www.ebi.ac.uk/biomodels/MODEL2208040001).

2. This work is published in Bioinformatics Advances (https://doi.org/10.1093/bioadv/vbad045)

3. The KG is hosted publicly at the NDEx server: https://doi.org/10.18119/N9SG7D