Interuniversity Institute of Bioinformatics in Brussels (IB)²
DIDA 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. Feel free to contact us at dida@ibsquare.be
DIDA – publication and citation
DIDA is published in the Nucleic Acids Research Database issue 2016 and has been selected as a NAR 2016 Breaktrough paper. The manuscript can be accessed here.
Please cite: 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. Nucl. Acids Res., 10.1093/nar/gkv1068.
Predictors developed with DIDA
The data from DIDA was used in our research group to train machine learning predictors aiming to predict and understand the cause of digenic diseases: VarCoPP , which predicts the pathogenicity of digenic variant combinations and the Digenic Effect predictor, which differentiates between True Digenic, Monogenic plus modifier and Dual Molecular diagnosis cases. The latter was improved in a later work. These predictors can be cited as:
- Digenic effect predictor:
- 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. DOI: https://doi.org/10.1093/nar/gkx557
- Versbraegen N. and 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. DOI: https://doi.org/10.1016/j.artmed.2019.06.006
- VarCoPP: Papadimitriou S., Gazzo A., Versbraegen N., Nachtegael C., Aerts J., Moreau Y., Van Dooren S., Nowé A., Smits G., Lenaerts T. Predicting disease-causing variant combinations. Proceedings of the National Academy of Sciences. May 2019. DOI: https://doi.org/10.1073/pnas.1815601116
Other predictors were developed with the DIDA data across the world :
- OligoPVP, Boudellioua I. et al., Scientific Reports (2018). DOI : https://doi.org/10.1038/s41598-018-32876-3
- DiGePred, Mukherjee S. et al., The American Journal of Human Genetics (2021). DOI : https://doi.org/10.1016/j.ajhg.2021.08.010
This work was supported by
the ARC project entitled ”Deciphering Oligo- and Polygenic Genetic Architecture in Brain Developmental Disorders”,
Wetenschappelijk Fonds Willy Gepts – UMCOR (Vrije Universiteit Brussel, UZ Brussel),
Brussels Institute for Research and Innovation (Innoviris) through the project BridgeIris and
the Interuniversity Institute of Bioinformatics in Brussels (IB)².
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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