Daniele Raimondi1, Andrea M Gazzo2, Marianne Rooman3, Tom Lenaerts4, Wim F Vranken5. 1. Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium Machine Learning Group, Université Libre De Bruxelles, Brussels 1050, Belgium Structural Biology Brussels, Vrije Universiteit Brussel, Brussels 1050, Belgium Structural Biology Research Centre, VIB, Brussels 1050, Belgium. 2. Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium Machine Learning Group, Université Libre De Bruxelles, Brussels 1050, Belgium. 3. Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium 3BIO-BioInfo Group, Université Libre De Bruxelles, Brussels 1050, Belgium. 4. Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium Machine Learning Group, Université Libre De Bruxelles, Brussels 1050, Belgium Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels 1050, Belgium. 5. Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium Structural Biology Brussels, Vrije Universiteit Brussel, Brussels 1050, Belgium Structural Biology Research Centre, VIB, Brussels 1050, Belgium.
Abstract
MOTIVATION: There are now many predictors capable of identifying the likely phenotypic effects of single nucleotide variants (SNVs) or short in-frame Insertions or Deletions (INDELs) on the increasing amount of genome sequence data. Most of these predictors focus on SNVs and use a combination of features related to sequence conservation, biophysical, and/or structural properties to link the observed variant to either neutral or disease phenotype. Despite notable successes, the mapping between genetic variants and their phenotypic effects is riddled with levels of complexity that are not yet fully understood and that are often not taken into account in the predictions, despite their promise of significantly improving the prediction of deleterious mutants. RESULTS: We present DEOGEN, a novel variant effect predictor that can handle both missense SNVs and in-frame INDELs. By integrating information from different biological scales and mimicking the complex mixture of effects that lead from the variant to the phenotype, we obtain significant improvements in the variant-effect prediction results. Next to the typical variant-oriented features based on the evolutionary conservation of the mutated positions, we added a collection of protein-oriented features that are based on functional aspects of the gene affected. We cross-validated DEOGEN on 36 825 polymorphisms, 20 821 deleterious SNVs, and 1038 INDELs from SwissProt. The multilevel contextualization of each (variant, protein) pair in DEOGEN provides a 10% improvement of MCC with respect to current state-of-the-art tools. AVAILABILITY AND IMPLEMENTATION: The software and the data presented here is publicly available at http://ibsquare.be/deogen CONTACT: : wvranken@vub.ac.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: There are now many predictors capable of identifying the likely phenotypic effects of single nucleotide variants (SNVs) or short in-frame Insertions or Deletions (INDELs) on the increasing amount of genome sequence data. Most of these predictors focus on SNVs and use a combination of features related to sequence conservation, biophysical, and/or structural properties to link the observed variant to either neutral or disease phenotype. Despite notable successes, the mapping between genetic variants and their phenotypic effects is riddled with levels of complexity that are not yet fully understood and that are often not taken into account in the predictions, despite their promise of significantly improving the prediction of deleterious mutants. RESULTS: We present DEOGEN, a novel variant effect predictor that can handle both missense SNVs and in-frame INDELs. By integrating information from different biological scales and mimicking the complex mixture of effects that lead from the variant to the phenotype, we obtain significant improvements in the variant-effect prediction results. Next to the typical variant-oriented features based on the evolutionary conservation of the mutated positions, we added a collection of protein-oriented features that are based on functional aspects of the gene affected. We cross-validated DEOGEN on 36 825 polymorphisms, 20 821 deleterious SNVs, and 1038 INDELs from SwissProt. The multilevel contextualization of each (variant, protein) pair in DEOGEN provides a 10% improvement of MCC with respect to current state-of-the-art tools. AVAILABILITY AND IMPLEMENTATION: The software and the data presented here is publicly available at http://ibsquare.be/deogen CONTACT: : wvranken@vub.ac.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Sofia Papadimitriou; Andrea Gazzo; Nassim Versbraegen; Charlotte Nachtegael; Jan Aerts; Yves Moreau; Sonia Van Dooren; Ann Nowé; Guillaume Smits; Tom Lenaerts Journal: Proc Natl Acad Sci U S A Date: 2019-05-24 Impact factor: 11.205