nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms.
Nonsynonymous single nucleotide polymorphisms (nsSNPs) are prevalent in genomes and are closely associated with inherited diseases. To facilitate identifying disease-associated nsSNPs from a large number of neutral nsSNPs, it is important to develop computational tools to predict the nsSNP's phenotypic effect (disease-associated versus neutral). nsSNPAnalyzer, a web-based software developed for this purpose, extracts structural and evolutionary information from a query nsSNP and uses a machine learning method called Random Forest to predict the nsSNP's phenotypic effect. nsSNPAnalyzer server is available at http://snpanalyzer.utmem.edu/.
Bao
Lei
L
Department of Molecular Sciences, Center of Genomics and Bioinformatics, University of Tennessee Health Science Center, 858 Madison Avenue, Memphis, TN 38163, USA.
Zhou
Mi
M
Cui
Yan
Y
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D020022:Genetic Predisposition to Disease; D006801:Humans; D020407:Internet; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011487:Protein Conformation; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D014584:User-Computer Interface