The IBsquare Toolbox for Oligogenic Analysis comprises three tools that are meant to assist researchers and doctors alike in the identification of genetic diseases. These three tools are closely intertwined as the data contained in the DIDA database was used to train the VarCopp predictor, which in turn is part of the pipeline for ORVAL.
IBsquare Toolbox for Oligogenic Analysis
DIDA: DIgenic Diseases DAtabase is a novel database that provides for the first time detailed information on genes and associated genetic variants involved in digenic diseases, the simplest form of oligogenic inheritance.
VarCoPP: Variant Combination Pathogenicity Predictor is a machine-learning method that predicts the potential pathogenicity of any bi-locus variant combination (i.e. a combination of two to four variant alleles between two genes). It has been trained on digenic disease data present in the Digenic Diseases Database (DIDA) and variant data derived from control individuals of the 1000 Genomes Project (1KGP). VarCoPP consists of an ensemble of 500 individual Random Forest predictors that predict whether a variant combination is disease-causing (i.e. candidate or probably pathogenic) or neutral (i.e. probably neutral).
ORVAL: Oligogenic Resource for Variant AnaLysis is a platform for the prediction and exploration of candidate disease-causing oligogenic variant combinations.
If you are using one of the above tools for your analysis, you can cite the tool and the predictive methods as follows:
- Gazzo,A.M., Daneels,D., Cilia,E., Bonduelle,M., Abramowicz,M., Van Dooren,S., Smits,G. and Lenaerts,T. (2015) DIDA: A curated and annotated digenic diseases database. Nucleic Acids Research. 44(D1), D900-D907.
- Gazzo A., Raimondi D., Daneels D., Moreau Y., Smits G., Van Dooren S., Lenaerts T. (2017) Understanding mutational effects in digenic diseases. Nucleic Acids Research. 45(15):e140.
- Versbraegen N., Fouché A., Nachtegael C., Papadimitriou S., Gazzo A., Smits G., Lenaerts T. (2019) Using game theory and decision decomposition to effectively discern and characterise bi-locus diseases. Artificial Intelligence in Medicine. 99:101690.
- Papadimitriou S., Gazzo A., Versbraegen N., Nachtegael C., Aerts J., Moreau Y., Van Dooren S., Nowé A., Smits G., Lenaerts T. (2019) Predicting disease-causing variant combinations. Proceedings of the National Academy of Sciences. 116 (24), 11878-11887.
- Renaux A., Papadimitriou S., Versbraegen N., Nachtegael C., Boutry S., Nowé A., Smits G., Lenaerts T. (2019) ORVAL: A novel platform for the prediction and exploration of disease-causing oligogenic variant combinations. Nucleic Acids Research. 47(W1):W93-W98.
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