nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms

被引:171
作者
Bao, L [1 ]
Zhou, M [1 ]
Cui, Y [1 ]
机构
[1] Univ Tennessee, Ctr Hlth Sci, Ctr Genom & Bioinformat, Dept Mol Sci, Memphis, TN 38163 USA
关键词
D O I
10.1093/nar/gki372
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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/.
引用
收藏
页码:W480 / W482
页数:3
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