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. 2013;14 Suppl 3(Suppl 3):S6.
doi: 10.1186/1471-2164-14-S3-S6. Epub 2013 May 28.

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

Emidio Capriotti  1 , Remo Calabrese, Piero Fariselli, Pier Luigi Martelli, Russ B Altman, Rita Casadio
Affiliations

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

Emidio Capriotti et al. BMC Genomics. 2013.

Abstract

Background: 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.

Results: 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&GO(3d) 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.

Conclusions: 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.

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Figures

Figure 1
Figure 1
Schematic view of SNPs&GO (panel A) and SNPs&GO3d (panel B). From the left to the right, the SNPs&GO and SNPs&GO3d input web pages, the flow chart of the sequence and structure-based methods and two examples of the returned outputs.
Figure 2
Figure 2
Performance of SNP&GO and SNPs&GO3d on the SAP-NEW dataset (DB). In panel A the ROC curves of both methods are shown. In panels B and C the performances of SNP&GO and SNPs&GO3d as a function of the Reliability index (RI) are reported.
See this image and copyright information in PMC

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