GLOWgenes

Prioritization of gene diseases candidates by disease-aware evaluation of heterogeneous evidence networks Visit www.glowgenes.org for more information

Citing

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

Requirements

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)

Obtaining network files

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

Editing networks config file

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

Running GLOWgenes

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

Parameters

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

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

Running an example

Within directory example you have full intructions to test GLOWgenes