Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel.

By Abel Gonzalez-Perez, Núria López-Bigas. 2011

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Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “Methods such as PredictSNP [48], Condel [49], and MetaSNP [50] have been explicitly optimized to combine different methods, mostly to annotate dis-ease-variant relationships (as opposed to functional changes).”
A general framework for estimating the relative pathogenicity of human genetic variants (2014, positive): “The lower panel shows violin plots of the median C-scores of potential nonsense (stop-gained) variants for genes that: harbor at least 5 known pathogenic mutations 48 (“disease”); are predicted to be “essential” 23 ; harbor variants associated with complex traits 41 (“GWAS”); harbor at least 2 loss-”
Predicting the Functional Effect of Amino Acid Substitutions and Indels (2012, background): “E-value [10], Condel [11] and several others [12,13].”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, methodology): “7 frequently followed by MutationTaster[13], CADD[14], PROVEAN[15], Mutpred[16] and Condel[17].”
Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data (2015, methodology): “Approaches to rating the potential pathogenicity of variants based on consensus of commonly used prediction algorithms have been shown to have superior calibration and discriminative accuracy when compared with individual predictions [36].”
Assessment of computational methods for predicting the effects of missense mutations in human cancers (2013, methodology): “tions) [2] is a weighted average of the normalized scores from multiple methods.”
Deleterious- and disease-allele prevalence in healthy individuals: insights from current predictions, mutation databases, and population-scale resequencing. (2012, methodology): “The accuracy of the Condel predictions has been estimated to be about 88%–90%.(24)”
The human splicing code reveals new insights into the genetic determinants of disease (2014, background): “However, when we examined 789 and 1757 missense disease SNVs that analysis using Condel (20) indicates minimally and maximally alter protein function, we found that SNVs that minimally alter protein function are on average 5.6 times more likely to disrupt splicing regulation (P=4.5e-14, two-sample t”
The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations (2013, methodology): “…above was used to avoid Nonetheless, our results from training and testing on HumVar mutations that map to structure considerably outperform other well-known individual methods including SIFT and PolyPhen2 as reported by González-Pérez and López-Bigas [18] (Accuracies between 0.690 and 0.771).”
Performance of computational tools in evaluating the functional impact of laboratory-induced amino acid mutations (2012, methodology): “In addition, these accuracies are comparable to those reported for human disease-associated variants (Adzhubei et al., 2010; González-Pérez and López-Bigas, 2011; Kumar et al., 2011).”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, methodology): “Predictionsfromseveralmethodscanbecombined andhasbeendoneinmetapredictorslikeCondel[Gonzalez-Perez andLopez-Bigas,2011],PON-P[Olatubosunetal.,2012],Meta-SNP[Capriottietal.,2013],andPredictSNP[Bendletal.,2014] thathavetrainedMLmethodstoweightheoutputsoftheindivid-ualpredictorsindifferentways.”
A survey of tools for variant analysis of next-generation genome sequencing data (2013, methodology): “NGS–SNP and VEP provide three scores: Condel [130], PolyPhen and SIFT.”