Human MutationVolume 30, Issue 8 p. 1237-1244 MethodFree Access Functional annotations improve the predictive score of human disease-related mutations in proteins Remo Calabrese, Remo Calabrese Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalySearch for more papers by this authorEmidio Capriotti, Emidio Capriotti Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalySearch for more papers by this authorPiero Fariselli, Piero Fariselli Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalySearch for more papers by this authorPier Luigi Martelli, Pier Luigi Martelli Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalySearch for more papers by this authorRita Casadio, Corresponding Author Rita Casadio casadio@biocomp.unibo.it Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalyLaboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, ItalySearch for more papers by this author Remo Calabrese, Remo Calabrese Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalySearch for more papers by this authorEmidio Capriotti, Emidio Capriotti Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalySearch for more papers by this authorPiero Fariselli, Piero Fariselli Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalySearch for more papers by this authorPier Luigi Martelli, Pier Luigi Martelli Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalySearch for more papers by this authorRita Casadio, Corresponding Author Rita Casadio casadio@biocomp.unibo.it Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, ItalyLaboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, ItalySearch for more papers by this author First published: 12 May 2009 https://doi.org/10.1002/humu.21047Citations: 406AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Single nucleotide polymorphisms (SNPs) are the simplest and most frequent form of human DNA variation, also valuable as genetic markers of disease susceptibility. The most investigated SNPs are missense mutations resulting in residue substitutions in the protein. Here we propose SNPs&GO, 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. The scoring efficiency of SNPs&GO is as high as 82%, with a Matthews correlation coefficient equal to 0.63 over a wide set of annotated nonsynonymous mutations in proteins, including 16,330 disease-related and 17,432 neutral polymorphisms. 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. Hum Mutat 30:1–8, 2009. © 2009 Wiley-Liss, Inc. Citing Literature Volume30, Issue8August 2009Pages 1237-1244 ReferencesRelatedInformation

Functional annotations improve the predictive score of human disease-related mutations in proteins

Scan to connect with one of our mobile apps

Coinbase Wallet app

Coinbase app

Or try the Coinbase Wallet browser extension