README

ODAMNet is a Python package to study molecular relationship between environmental factors (called chemicals here) and rare diseases.

The ODAMNet documentation is available in ReadTheDocs.

This tool was created within the framework of the EJRP-RD project.

If you use ODAMNet, please cite our publication:

Térézol M, Baudot A, Ozisik O. ODAMNet: A Python package to identify molecular relationships between chemicals and rare diseases using overlap, active module and random walk approaches. SoftwareX. 2024;26:101701. doi: 10.1016/j.softx.2024.101701.

Installation

From PyPI

ODAMNet is available in Python package. You can easily install it using pip.

$ python3 -m pip install odamnet

From Conda

ODAMNet is also available in bioconda using conda.

$ conda install odamnet

From Github

Clone the repository from GitHub

$ git clone https://github.com/MOohTus/ODAMNet.git

Then, install it

$ python3 -m pip install -e ODAMNet/

If it’s not working, try to update pip using pip install pip –upgrade

Usage

Three different approaches are available:

Overlap analysis
Active Modules Identification (AMI, using DOMINO)
Random Walk with Restart (RWR, using multiXrank)

$ odamnet [overlap|domino|multixrank|networkCreation|networkDownloading] [ARGS]

Examples

Three approaches are implemented to study relationships between genes targeted by chemicals (retrieved automatically from the Comparative Toxicogenomics Database (CTD)) and rare diseases (retrieved automatically from WikiPathways).

Overlap analysis

The first approach computes the overlap between chemical target genes and rare disease pathways. It is looking for direct associations, i.e. chemical target genes that are part of rare disease pathways.

Give your chemicals list into --chemicalsFile input.

$ odamnet overlap --chemicalsFile FILENAME

Active Module Identification (AMI)

The Active Module Identification is performed using DOMINO tool.

DOMINO defines target genes as active genes to search for active modules using a biological network (e.g. protein-protein interaction network, PPI). Then, an overlap analysis is performed between identified active modules and rare disease pathways.

Give your chemicals list and your biological network into --chemicalsFile and --networkFile respectively.

$ odamnet domino --chemicalsFile FILENAME --networkFile FILENAME

Random Walk with Restart (RWR)

The Random Walk with Restart is performed using multiXrank Python package. This approach mesures the proximity of every node (e.g. genes and diseases) to the target genes within a multilayer network. The multilayer network is composed of genes networks and rare disease pathway network. Diseases and genes are linked using a bipartite.

Give your chemicals list into --chemicalsFile input.

MultiXrank needs a configuration file (--configPath), networks directory (--networksPath), the target genes file (--seedsFile) and a name to write the result into network file (--sifFileName).

$ odamnet multixrank --chemicalsFile FILENAME --configPath PATH --networksPath PATH --seedsFile FILENAME --sifFileName FILENAME

You can have more details about the configuration file in the documentation page.

Other functions

Network and bipartite creation

For the RWR, you should need to create a rare disease pathways network to integrate disease information into the multilayer. ODAMNet creates a disconnected network (no connection between disease nodes) and its corresponding bipartite that connects diseases with genes that are involved in.

Give a path to save generated disease network and disease-gene bipartite using --networksPath and --bipartitePath respectively.

$ odamnet networkCreation --networksPath PATH --bipartitePath PATH

Rare disease pathways are retrieved automatically from WikiPathways.

Network downloading

ODAMNet allows you to download automatically biological networks from NDEx using the network ID (--netUUID). You can choose the network name file with --networkFile.

$ odamnet networkDownloading --netUUID TEXT --networkFile FILENAME