2011
DOI: 10.1016/j.ajhg.2011.03.004
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Improving the Assessment of the Outcome of Nonsynonymous SNVs with a Consensus Deleteriousness Score, Condel

Abstract: Several large ongoing initiatives that profit from next-generation sequencing technologies have driven--and in coming years will continue to drive--the emergence of long catalogs of missense single-nucleotide variants (SNVs) in the human genome. As a consequence, researchers have developed various methods and their related computational tools to classify these missense SNVs as probably deleterious or probably neutral polymorphisms. The outputs produced by each of these computational tools are of different natu… Show more

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Cited by 719 publications
(625 citation statements)
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“…[29][30][31] The Consensus Deleteriousness software, which combines information derived from SIFT, Polyphen-2, and Mutation Assessor, was used to predict the functionality of nonsynonymous mutations. 32 In addition, we calculated the Combined Annotation Dependent Depletion score (http:// cadd.gs.washington.edu/score), which is another predictor for the pathogenicity of an amino acid change and includes the previous prediction models. 33 …”
Section: Review Materials and Methodsmentioning
confidence: 99%
“…[29][30][31] The Consensus Deleteriousness software, which combines information derived from SIFT, Polyphen-2, and Mutation Assessor, was used to predict the functionality of nonsynonymous mutations. 32 In addition, we calculated the Combined Annotation Dependent Depletion score (http:// cadd.gs.washington.edu/score), which is another predictor for the pathogenicity of an amino acid change and includes the previous prediction models. 33 …”
Section: Review Materials and Methodsmentioning
confidence: 99%
“…I-Mutant2.0 28 was used as a predictor of protein stability changes upon variations. Computational prediction of disease-related variants was performed with the consensus tools CONDEL 29 (combines Logre, MAPP, Massessor, Pph2 and SIFT), Meta-SNP 30 (combines PANTHER, PhD-SNP, SIFT and SNAP), PredictSNP 31 (combines MAPP, PhD-SNP, Pph1, Pph2, SIFT and SNAP) and PON-P2. 32 …”
Section: Bioinformatics Tools For Sequence Variant Interpretationmentioning
confidence: 99%
“…To distinguish variants from local polymorphisms between 200 and 248 anonymized in‐house normal control blood samples were sequenced over the positions of interest. Variant were interpreted for their deleteriousness by using SIFT (Sorting Intolerant From Tolerant), PolyPhen‐2, (Polymorphism Phenotyping version 2), Mutation Taster, Condel (CONsensus DELeteriousness score of missense SNVs), and PON‐P (Pathogenic‐or‐Not–Pipeline) (Sunyaev et al, 2001; Kumar et al, 2009; Schwarz et al, 2010; Gonzalez‐Perez and Lopez‐Bigas, 2011; Olatubosun et al, 2012). Splice‐site prediction of variants was performed with SpliceSiteFinder‐like, MaxEntScan, NNSPLICE, GeneSplicer and Human Splicefinder (Reese et al, 1997; Zhang, 1998; Pertea et al, 2001; Fairbrother et al, 2002; Cartegni et al, 2003; Yeo and Burge, 2004; Desmet et al, 2009; Houdayer et al, 2012).…”
Section: Methodsmentioning
confidence: 99%