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DRExM³L modelization of Retinitis Pigmentosa

In this work we propose a unique approach that, starting from the set of RP disease affected genes, provides a comprehensive landscape of the molecular mechanisms of the disease along with its druggable space. The method establishes a mechanistic disease map as an actionable environment, and employs an explainable machine learning model, “Drug REpurposing using Mechanistic Models of signal transduction and eXplainable Machine Learning” (DRExM³L), to assess the influence of druggable molecules, like drug target proteins, over the disease environment. Our approach merges information from transcriptomics, pathway graphs, biological/clinical, and drug-target interactions databases, to generate an in-depth view of the disease. The novelty of this workflow lies in the integration of multiple data sources, reinforcing interpretability with biological knowledge while reducing the dimensionality of the datasets.

DRExM³L model overview

Table of Contents

Input Data

The necessary data to reproduce the results of the manuscript can be downloaded from Zenodo. Note that the workflow will try to automatically download the data after installing the dependencies.

  • The Genotype-Tissue Expression (GTEx) RNA-Seq Data Gene read counts:

  • The DrugBank 5.1.8 db :

  • Disambiguations for ambiguous drug action annotations - DrugBank 5.1.8 db :

    • File name: amendments_drugActions_drugbank-v050108.tsv
    • File path: data/raw
  • Public databases downloaded through VarfromPDB R package:

  • The Anatomical Therapeutic Chemical (ATC) Classification table:

  • The Human Phenotype Ontologies database (HPO):

  • The Human Phenotype Ontologies (HPO) annotations linking diseases and phenotypes

  • The Human Phenotype Ontologies (HPO) annotations linking genes and phenotypes

  • HiPathia's list of physiological KEGG signaling pathways

  • HiPathia's list of physiological KEGG signaling pathways with GO/Uniprot functional annotations

  • Main actions by drug (manually curated):

RP Mechanistic Map

RP Mechanistic Map

RP Mechanistic Map here.

Setup and Usage

The project requires a working conda and a GNU/Linux x64 system. If conda is not available, the workflow will try to install the miniconda distribution.

Copy example.env to .env.

  • Set UPDATE=false, the default option, to reproduce the manuscript results.

If using a SLURM-based HPC, run the full analysis with: sbatch run.sbatch

If using bash, the same script could be used in a non-SLURM system.

Updating

We have included a minimal update mechanism to facilitate the reanalysis of the disease:

  • Set UPDATE=true and we will try to update the databases, use the last version of the Hipathia model, etc.
    • To update the Drugbank release, copy the xml file to data/raw and change DRUGBANK_VERSION in .env.
    • To update the GTEx counts, copy the gct file to data/raw and update GTEX_FNAME in .env.
    • Follow the same procedure with the hpo.obo files.
    • RP experts could update the manually curated files.
    • DRExM³L will be automatically updated to the last stable release.
  • Set USE_GPU=N to use the indicated number of GPUs, N=1, 2, ... (0 for CPU).

Authors and contributors

The code of this project is released under the MIT license.

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