WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation

Abstract

BackgroundSNPs&GO is a method for the prediction of deleterious Single Amino acid Polymorphisms (SAPs) using protein functional annotation. In this work, we present the web server implementation of SNPs&GO (WS-SNPs&GO). The server is based on Support Vector Machines (SVM) and for a given protein, its input comprises: the sequence and/or its three-dimensional structure (when available), a set of target variations and its functional Gene Ontology (GO) terms. The output of the server provides, for each protein variation, the probabilities to be associated to human diseases.ResultsThe server consists of two main components, including updated versions of the sequence-based SNPs&GO (recently scored as one of the best algorithms for predicting deleterious SAPs) and of the structure-based SNPs&GO3d programs. Sequence and structure based algorithms are extensively tested on a large set of annotated variations extracted from the SwissVar database. Selecting a balanced dataset with more than 38,000 SAPs, the sequence-based approach achieves 81% overall accuracy, 0.61 correlation coefficient and an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve of 0.88. For the subset of ~6,600 variations mapped on protein structures available at the Protein Data Bank (PDB), the structure-based method scores with 84% overall accuracy, 0.68 correlation coefficient, and 0.91 AUC. When tested on a new blind set of variations, the results of the server are 79% and 83% overall accuracy for the sequence-based and structure-based inputs, respectively.ConclusionsWS-SNPs&GO is a valuable tool that includes in a unique framework information derived from protein sequence, structure, evolutionary profile, and protein function. WS-SNPs&GO is freely available at http://snps.biofold.org/snps-and-go.

Genetic Epidemiology of Glucose-6-Phosphate Dehydrogenase Deficiency in the Arab World

“…The pathogenicity of the 23 missense mutations among the Arab population was assessed using five in silico prediction tools, namely, SNAP237 (https://www.rostlab.org/services/snap/), SIFT38 (http://sift.jcvi.org), PolyPhen-239 (http://genetics.bwh.harvard.edu/pph2/), SNPs&Go40 (http://snps-and-go.biocomp.unibo.it/snps-and-go/), and Mutationassessor41 (http://mutationassessor.org/). …”

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

“…Several recent articles have demonstrated this principle particularly well. For example, for SNPs&GO (Capriotti et al 2013b) the same learning procedure was followed with and without the additional Gene Ontology information (Ashburner et al 2000) to quantitate the improvement in prediction accuracy from this one additional data source. SNAP2 (Hecht et al 2015) reported results for multiple different machine-learning methods using the same set of features.…”

Merosin-deficient congenital muscular dystrophy: A novel homozygous mutation in the laminin-2 gene

“…However, he has a greater language and cognitive delay than usual, with only four words (all names) in his vocabulary at present. Table 1 In silico analyses of the LAMA2 gene * mutation Protein prediction programs PolyPhen-2 ** [6] Mutation taster [7] Mutation assessor [8] (functional impact) I-MUTANT 3.0 [9] PMut [10] MutPred [11] SNPs&GO [12] SIFT [13] Probably damaging The protein prediction programmes were queried using the above references, with the LAMA2 gene mutation formally described as c.7862G>T and p.Gly2621Val. ** Scores relate to predictions based on HumDiv and HumVar models.…”