nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms.

Lei Bao; Mi Zhou; Yan Cui

doi:10.1093/nar/gki372

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nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms.

Lei Bao·Mi Zhou·Yan Cui

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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/.

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Algorithms Artificial Intelligence Computational Biology Genetic Predisposition to Disease Humans Internet Phenotype Polymorphism, Single Nucleotide Protein Conformation Sequence Alignment Sequence Analysis, Protein Software User-Computer Interface Journal Article Research Support, Non-U.S. Gov't

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Lei Bao, Mi Zhou, Yan Cui. 2005-07-01. nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms.. https://doi.org/10.1093/nar%2Fgki372

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