nsSNPAnalyzer

Identifying disease-associated nonsynonymous single nucleotide polymorphisms

Lei Bao, Mi Zhou, Yan Cui

Research output: Contribution to journalArticle

122 Citations (Scopus)

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/.

Original languageEnglish (US)
JournalNucleic acids research
Volume33
Issue numberSUPPL. 2
DOIs
StatePublished - Jul 1 2005

Fingerprint

Single Nucleotide Polymorphism
Software
Genome
Machine Learning
Forests

All Science Journal Classification (ASJC) codes

  • Genetics

Cite this

nsSNPAnalyzer : Identifying disease-associated nonsynonymous single nucleotide polymorphisms. / Bao, Lei; Zhou, Mi; Cui, Yan.

In: Nucleic acids research, Vol. 33, No. SUPPL. 2, 01.07.2005.

Research output: Contribution to journalArticle

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