Prediction of deleterious human alleles.

By Shamil R. Sunyaev, V. Ramensky, I. Koch, Warren C. Lathe, Alexey S. Kondrashov, Peer Bork. 2001

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Citations in the corpus

Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2 (2013, positive): “As we demonstrated earlier (Sunyaev et al., 2001; Boyko et al., 2008), these predictions correlate with the effect of natural selection, demonstrated by an excess of rare alleles among alleles that are predicted to be functional.”
Accounting for human polymorphisms predicted to affect protein function. (2002, background): “…might appear incorrect based on the lack of association with diabetes, but carriers of this nsSNP have higher cholesterol levels and increased apolipoprotein B concentrations, thus it has been proposed to increase the risk of coronary artery disease (Vohl et al. 2000; Lacquemant et al. 2000).”
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations (2014, methodology): “Finally, PPH-1 employs an expert set of empirical rules for the classification [32].”
Testing for an Unusual Distribution of Rare Variants (2011, methodology): “For example, for POLYPHEN the larger the position-specific score the more likely the substitution is deleterious and applying such weights to each variant in C-alpha might further enhance power.”
Assessing the Evolutionary Impact of Amino Acid Mutations in the Human Genome (2008, methodology): “Briefly, PolyPhen will classify a SNP as benign, possibly damaging, or probably damaging based on how conserved the site is across a multi-species alignment, functional annotation, and upon the structural consequences of the SNP [26,27].”
Characterising and Predicting Haploinsufficiency in the Human Genome (2010, background): “We contend that the prediction power of popular methods for predicting the functional impact of nonsynonymous substitutions from structural information and evolutionary conservation, such as SIFT [25] and POLYPHEN [26], is limited by an inability to discern from cross-species alignments whether puri”
Dysfunction of lipid sensor GPR120 leads to obesity in both mouse and human (2012, methodology): “…acid substitutions on the structure and function of GPR120, we used several softwares: 1/ PolyPhen (Polymorphism Phenotyping) web-based program 43,44 ; 2/ PANTHER (Protein ANalysis THrough Evolutionary Relationships) 45 ; 3/ SIFT (Sorting Intolerant From Tolerant) algorithm 46 ; 4/ SNAP…”
Human non-synonymous SNPs: server and survey. (2002, background): “The rule is based on the analysis of the ability of various structural parameters and pro®le scores to discriminate between disease mutations and substitutions between human proteins and closely related mammalian orthologues (7).”
Evaluation of structural and evolutionary contributions to deleterious mutation prediction. (2002, methodology): “for predicting intolerant mutations.(2) These methods integrate the substitution and site mutability information into a single score, allow for the calculation of consistent scores when few homologs are available, and model the tolerance of residues that belong to the same biochemical class as those”
SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies (2009, methodology): “We used two in silico classification programs, Polyphen (8) and SNPs3D (9), to predict the effect of an amino acid substitution on the structure and function of a human protein, and then classified nsSNPs as possibly or probably damaging or benign.”
The amino-acid mutational spectrum of human genetic disease (2003, background): “We note that the mutation rate we estimate (a fraction of newly created deleterious mutations) is different from the fraction of existing SNPs with deleterious effects on protein function (estimated previously [1,2,23]).”
Functional annotations improve the predictive score of human disease‐related mutations in proteins (2009, background): “…missense SNPs (mSNPs) predictions from mutagenesis data [Chasman and Adams, 2001; Krishnan and Westhead, 2003] and from the generation of neutral data starting from pseudomutations between orthologous proteins [Ferrer-Costa et al., 2002, 2004, 2005; Sunyaev et al., 2001; Yue and Moult, 2005, 2006].”