NORDic 1.0.6

Network Oriented Repurposing of Drugs (NORDic): network identification and master regulator detection

Network Oriented Repurposing of Drugs (NORDic) package

(c) Clémence Réda, 2022.

Citation

If you use NORDic in published research, please cite the following preliminary work:

Réda, C., & Delahaye-Duriez, A. (2022). Prioritization of Candidate Genes Through Boolean Networks. In International Conference on Computational Methods in Systems Biology (pp. 89-121). Springer, Cham.

Due to the presence of copyrighted databases, the license for this code is Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Installation

pip install NORDic

Using the "refractory epilepsy" application

Import the initial states from Mirza et al., 2017 and the M30 genes from Delahaye-Duriez et al., 2016

conda activate NORDic_env
python3 download_Refractory_Epilepsy_Data.py
conda deactivate

Building a Boolean network

You need to register to the LINCS L1000 database and the DisGeNet database and write up the corresponding credentials and API keys to files

from NORDic.NORDic_NI.functions import network_identification
solution = network_identification(file_folder, taxon_id, path_to_genes, ...)

The final network solution is written to <file_folder>solution.bnet.

Detection of master regulators

Using the filename in .bnet "network_name", the size k of the set of master regulators and the set of initial states states

from NORDic.NORDic_PMR.functions import greedy
S, spreads = greedy(network_name, k, states, ...)

The result file is named application_regulators.csv.

Network analysis with Cytoscape

Network analyses are performed with Cytoscape 3.8.0. You need to download the module CytoCtrlAnalyser (version 1.0.0). Then run

from NORDic.NORDic_NI.functions import solution2cytoscape
solution2cytoscape(solution, file_folder+"solution_minimal_cytoscape")

which will create a style file (in .xml) and a network file readable by Cytoscape (in .sif).

Pull requests, issues, suggestions?

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