Human non-synonymous SNPs: server and survey.

By V. Ramensky, Peer Bork, Shamil R. Sunyaev. Nucleic Acids Research, 2002

2,455 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/601d2883fd6928766dffd01c9d1ab63821328425

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Citations in the corpus

ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data (2010, methodology): “One of the mutations was predicted as ‘benign’ by PolyPhen in the Ng et al. study and would have been missed, had Ng et al. utilized PolyPhen predictions in their ﬁltering procedure (3).”
Pooled association tests for rare variants in exon-resequencing studies. (2010, methodology): “Functional predictions for each missense variant were calculated with PolyPhen-2.(17,18) PolyPhen-2 scores were used for computing Bayesian posterior probabilities, and weights were applied as described above.”
FASTSNP: an always up-to-date and extendable service for SNP function analysis and prioritization (2006, background): “In this pipeline, an agent will query PolyPhen (21) to obtain its prediction on whether the SNP will alter an amino acid in a protein and result in structural changes (damaged or benign) in the protein.”
The impact of next-generation sequencing on genomics (2011, methodology): “Polymorphism Phenotyping (PolyPhen) and updated PolyPhen-2 are tools which predict the possible impact of an amino acid substitution on the structure and function of a specific protein using straightforward comparative physical methods (Sunyaev et al., 2001;Ramensky et al., 2002).”
Leber congenital amaurosis: genes, proteins and disease mechanisms. (2008, methodology): “Besides checking SNP databases and testing control individuals for a new variant to determine the allele frequency in the normal population, several tools exist that can be helpful to predict pathogenicity, including the Blosum 62 substitution matrix (Henikoff and Henikoff, 1992), PolyPhen (Ramensky”
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations (2014, methodology): “Therearemany computational tools for prediction of the effects of amino acid substitution on protein function, e.g., MutPred [10], nsSNPAnalyzer [11], PolyPhen-1 (PPH-1) [12], PolyPhen-2 (PPH-2) [13], SNAP [14], MAPP [15], PANTHER [16], PhD-SNP [17], SIFT [18] and SNPs&GO [19].”
F-SNP: computationally predicted functional SNPs for disease association studies (2007, methodology): “Protein coding PolyPhen (6) http://genetics.”