nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms. 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/.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Qin, Wenli; Li, Yizhou; Li, Juan; Yu, Lezheng; Wu, Di; Jing, Runyu; Pu, Xuemei; Guo, Yanzhi; Li, Menglong: Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes (2012)
- Li, Yizhou; Wen, Zhining; Xiao, Jiamin; Yin, Hui; Yu, Lezheng; Yang, Li; Li, Menglong: Predicting disease-associated substitution of a single amino acid by analyzing residue interactions (2011) ioport
- Masso, Majid; Vaisman, Iosif I.: Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms (2010)
- Bromberg, Yana; Rost, Burkhard: Correlating protein function and stability through the analysis of single amino acid substitutions (2009) ioport
- Bao, Lei; Zhou, Mi; Cui, Yan: Nssnpanalyzer: Identifying disease-associated nonsynonymous single nucleotide polymorphisms. (2005) ioport