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Nucleic Acids Research 2005 33(Web Server Issue):W480-W482; doi:10.1093/nar/gki372
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© The Author 2005. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oupjournals.org

Article

nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms

Lei Bao, Mi Zhou and Yan Cui*

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

*To whom correspondence should be addressed. Tel: +1 901 448 3240; Fax: +1 901 4487360; Email: ycui2{at}utmem.edu

Received January 21, 2005. Revised February 16, 2005. Accepted March 7, 2005.

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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