Prioritization of gene diseases candidates by disease-aware evaluation of heterogeneous evidence networks Visit www.glowgenes.org for more information
de la Fuente L, Del Pozo-Valero M, Perea-Romero I, Blanco-Kelly F, Fernández-Caballero L, Cortón M, Ayuso C, Mínguez P. Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks. International Journal of Molecular Sciences. 2023; 24(2):1661. https://doi.org/10.3390/ijms24021661
R (tested with version 3.5.0). R packages: optparse, caret
Python 2.7 or 3.6
Python packages: numpy (tested with version 1.11.0), pandas (tested with version 0.19.0), scipy (tested with version 0.18.1), sklearn (tested with version 0.0), networkx (tested with version 3.0)
Download network files from: Minguez, Pablo (2022): GLOWgenesNets.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.21408393.v1
You could also generate your own networks or selected a subset from theose provided by GLOWgenes
Edit networks_knowledgeCategories.cfg file with your complete directory route to the network files e.g. substitute PATH by home/pablo/GLOWgenesNets in every line, as in: /PATH/coexpressionCOXPRESdbEXT_HGNCnets.txt
usage: GLOWgenes.py [-h] -i INPUT -n NETWORKS -o OUTPUT [-t] [-p] [-f FILTERING] [-en EXPNORM] [-co CUTOFF] [-r RATIO]
python GLOWgenes.py -i diseaseGenes.txt -n networks.cfg -o outputdir -p
Use complete paths to avoid errors
Mandatory parameters:
-i --input INPUT File listing known associated disease genes
-n --networks NETWORKS Evidence network config file. Three tab-separated fields: network path, network name, network category
DEFAULT NETWORK CONFIG FILE IS LOCATED AT TEST FOLDER
-o --output OUTPUT Output directory
-p, --panelapp
Disease-associated genes in PanelApp format
Gene Panels from PanelApp can be download from https://panelapp.genomicsengland.co.uk/panels/.
-t, --timeprinted
Knowledge accumulation approach.
-f FILTERING, --filtering FILTERING List of candidate genes. Edges involving genes not listed here are filtered from networks
-en EXPNORM, --expnorm EXPNORM Expression levels file. Two tab-separated fields: gene name, expression level
-co CUTOFF, --cutoff CUTOFF Maximum seed initialization value when considering gene expression levels. Range 0-1
-r RATIO, --ratio RATIO Training ratio for random training/test splits
Within directory example you have full intructions to test GLOWgenes