WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation
By Emidio Capriotti, Remo Calabrese, Piero Fariselli, Pier Luigi Martelli, Russ B. Altman, Rita Casadio. BMC Genomics, 2013
357 citations recorded by the source corpus.
SNPs&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. The 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. WS-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.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/cc7495f6d6ac9818f55c1b9c18c11e65b9b1535a
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Citations in the corpus
- Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, background): “SNPs&GO (Capriotti et al , 2013) ranked 2 nd for human and 1 st for yeast proteins, although its predictions were relatively poor for non-eukaryotic (bacterial and viral) proteins.”
- Genetic variations and diseases in UniProtKB/Swiss-Prot: The ins and outs of expert manual curation (2014, background): “Disease-linked variant p.Gln376Pro perturbs protein subcellular location. pathogenicity of missense variants [Thusberg et al., 2011; Capriotti et al., 2013; Shihab et al., 2013; Yates and Sternberg, 2013].”