A web server for predicting disease associated variations
from protein sequence and structure.

SNPs&GO is an algorithm developed in the Laboratory of Biocomputing at the University of Bologna directed by Prof. Rita Casadio. This page links to a web application of SNPs&GO hosted by a server of the BioFolD Unit. SNPs&GO predictor is also made avalible through a docker container at the link

SNPs&GO is an accurate method that, starting from a protein sequence, can predict whether a variation is disease related or not by exploiting the corresponding protein functional annotation. SNPs&GO collects in unique framework information derived from protein sequence,evolutionary information, and function as encoded in the Gene Ontology terms, and outperforms other available predictive methods.

Recently, we developed an extension of SNPs&GO including information extracted from the protein 3D structure (SNPs&GO3d). Although tested on a smaller set of mutations, SNPs&GO3d results in a better accuracy with respect to the sequence-based method.

SNPs&GO            SNPs&GO3D


Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. (2009). Functional annotations improve the predictive score of human disease-related mutations in proteins. Human Mutation. 30; 1237-1244. 

Capriotti E, Altman RB. (2011). Improving the prediction of disease-related variants using protein three-dimensional structure. BMC Bioinformatics. 12 (Suppl 4); S3. 

Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R (2013). WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics. Suppl 3:S6.