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
https://hub.docker.com/r/biofold/snps-and-go.
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.
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References
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.
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