title: Blind prediction of deleterious amino acid variations with SNPs&GO.
abstract: SNPs&GO is a machine learning method for predicting the association of single amino acid variations (SAVs) to disease, considering protein functional annotation. The method is a binary classifier that implements a support vector machine algorithm to discriminate between disease-related and neutral SAVs. SNPs&GO combines information from protein sequence with functional annotation encoded by gene ontology (GO) terms. Tested in sequence mode on more than 38,000 SAVs from the SwissVar dataset, our method reached 81% overall accuracy and an area under the receiving operating characteristic curve of 0.88 with low false-positive rate. In almost all the editions of the Critical Assessment of Genome Interpretation (CAGI) experiments, SNPs&GO ranked among the most accurate algorithms for predicting the effect of SAVs. In this paper, we summarize the best results obtained by SNPs&GO on disease-related variations of four CAGI challenges relative to the following genes: CHEK2 (CAGI 2010), RAD50 (CAGI 2011), p16-INK (CAGI 2013), and NAGLU (CAGI 2016). Result evaluation provides insights about the accuracy of our algorithm and the relevance of GO terms in annotating the effect of the variants. It also helps to define good practices for the detection of deleterious SAVs.
authors.affiliation: Biocomputing Group, BiGeA / Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna University of Bologna, Bologna, Italy.
authors.forename: Emidio
authors.initials: E
authors.lastname: Capriotti
authors.affiliation: Biocomputing Group, BiGeA / Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna University of Bologna, Bologna, Italy.
authors.forename: Pier Luigi
authors.initials: PL
authors.lastname: Martelli
authors.affiliation: Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Padova, Italy.
authors.forename: Piero
authors.initials: P
authors.lastname: Fariselli
authors.affiliation: Biocomputing Group, BiGeA / Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna University of Bologna, Bologna, Italy.
authors.forename: Rita
authors.initials: R
authors.lastname: Casadio
mesh_terms: D017766:Acid Anhydride Hydrolases; D000465:Algorithms; D019943:Amino Acid Substitution; D064447:Checkpoint Kinase 2 / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D019941:Cyclin-Dependent Kinase Inhibitor p16 / Q000235:genetics*; D045643:DNA Repair Enzymes / Q000235:genetics*; D004268:DNA-Binding Proteins / Q000235:genetics*; D063990:Gene Ontology; D020022:Genetic Predisposition to Disease; D006801:Humans; D058977:Molecular Sequence Annotation; D012372:ROC Curve; D060388:Support Vector Machine; D048809:alpha-N-Acetylgalactosaminidase / Q000235:genetics*
chemical_list: D019941:Cyclin-Dependent Kinase Inhibitor p16; D004268:DNA-Binding Proteins; D064447:Checkpoint Kinase 2; C578634:CHEK2 protein, human; D048809:alpha-N-Acetylgalactosaminidase; D017766:Acid Anhydride Hydrolases; C112158:RAD50 protein, human; D045643:DNA Repair Enzymes
keywords: disease-related variation; gene ontology; genome interpretation; machine learning; protein function; single amino acid variation; variant annotation