nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms

Lei Bao; Mi Zhou; Yan Cui

doi:10.1093/nar/gki372

nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms

Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W480-2. doi: 10.1093/nar/gki372.

Authors

Lei Bao¹, Mi Zhou, Yan Cui

Affiliation

¹ Department of Molecular Sciences, Center of Genomics and Bioinformatics, University of Tennessee Health Science Center, 858 Madison Avenue, Memphis, TN 38163, USA.

Abstract

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/.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artificial Intelligence
Computational Biology
Genetic Predisposition to Disease*
Humans
Internet
Phenotype
Polymorphism, Single Nucleotide*
Protein Conformation
Sequence Alignment
Sequence Analysis, Protein / methods*
Software*
User-Computer Interface