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SNPs&GO
Predicting disease-related SNPs using GO terms
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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 server
implementation of SNPs&GO hosted by a server of the
Structural
Bioinformatics Unit at the University of Balearic Islands (UIB).
SNPs&GO is an accurate method that, starting from a protein sequence,
can predict whether a mutation is disease related or not by exploiting the 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.
SNPs&GO server
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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.
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