nsSNPAnalyzer：识别与疾病相关的非同义单核苷酸多态性
nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms.
作者信息
Bao Lei, Zhou Mi, Cui Yan
机构信息
Department of Molecular Sciences, Center of Genomics and Bioinformatics, University of Tennessee Health Science Center, 858 Madison Avenue, Memphis, TN 38163, USA.
出版信息
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W480-2. doi: 10.1093/nar/gki372.
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/.
摘要
非同义单核苷酸多态性（nsSNPs）在基因组中普遍存在，并且与遗传性疾病密切相关。为了便于从大量中性nsSNPs中识别与疾病相关的nsSNPs，开发计算工具来预测nsSNP的表型效应（疾病相关与中性）非常重要。nsSNPAnalyzer是为此目的开发的基于网络的软件，它从查询的nsSNP中提取结构和进化信息，并使用一种称为随机森林的机器学习方法来预测nsSNP的表型效应。nsSNPAnalyzer服务器可在http://snpanalyzer.utmem.edu/获取。
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