Detail výsledku
PredictSNP 2.0
Musil Miloš, Ing., Ph.D., UIFS (FIT)
Štourač Jan, FIT (FIT)
Zendulka Jaroslav, doc. Ing., CSc., UIFS (FIT)
Damborský Jiří, prof. Mgr., Dr., UMEL (FEKT)
Brezovský Jan, FIT (FIT)
Thistool estimates the deleteriousness of single-nucleotide mutations inthe context of the development of Mendelian diseases. The predictionsare based on the results of existing tools: CADD, DANN, FATHMM,FunSeq2 and GWAVA. To achieve the highest possible accuracy,developed consensual functions based on category optimal decisionthresholds differ according to the category of variants. Thesegeneral categories are recognized: (i) regulatory, (ii) splicing,(iii) synonymous, (iv) missense and (v)nonsense variants. Theevaluation on large datasets revealed a marked benefit of thisapproach while the web interface provides easily interpretableresults for all individual tools and our consensual predictiontogether with the links to the relevant databases and on-lineservices.
SNP effect; deleteriousness prediction; SNP prediction; mutation analysis; Mendelian diseases
Nástroj je dostupný na internetové adrese:http://loschmidt.chemi.muni.cz/predictsnp2/Uživatelský manuál je dostupný na internetové adrese:http://loschmidt.chemi.muni.cz/predictsnp2/docs/userguide.pdf
Pro informace o licenčních podmínkách prosím kontaktujte: Mgr. Michaela Kavková, Výzkumné centrum informačních technologií, Fakulta informačních technologií VUT v Brně, Božetěchova 2, 612 66 Brno, 541 141 470.