Automated inference of molecular mechanisms of disease from amino acid substitutions

By Biao Li, Vidhya Gomathi Krishnan, Matthew E. Mort, Fuxiao Xin, Kishore K. Kamati, David N. Cooper, Sean D. Mooney, Predrag Radivojac. 2009

811 citations recorded by the source corpus.

Subfields: Cancer Research, Genetics, Molecular Biology

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Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “Some methods focus exclusively on the human genome [12,13] and some aspire to identify disease-causing variants [14-16].”
SIFT web server: predicting effects of amino acid substitutions on proteins (2012, methodology): “At least two methods, MutPred (26) and nsSNPAnalyzer (27) incorporate the SIFT algorithm into their prediction pipelines, while BSIFT (28) uses the SIFT algorithm to include predictions for activating mutations.”
The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine (2013, neutral): “HGMD has also been used by a number of different groups to aid the development of post-NGS variant interpretation algorithms including MutPred (Li et al. 2009), PROVEAN (Choi et al. 2012), CAROL (Lopes et al. 2012), CRAVAT (Douville et al. 2013), NEST (Carter et al. 2013) and FATHMM (Shihab et al.…”
Improving the prediction of disease-related variants using protein three-dimensional structure (2011, methodology): “several methods have been developed to predict the impact of a given SAP [7-26].”
A new disease-specific machine learning approach for the prediction of cancer-causing missense variants (2011, methodology): “Only MutPred [25] is the first attempt of algorithm able to provide information about the disease mechanism.”
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].”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, background): “Thebestper-formingmethods,SNPs&GO[Calabreseetal.,2009]andMutPred [Lietal.,2009],hadMCCvaluescloseto0.”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, background): “We found that Poly-phen [11] and SIFT [12] are cited most frequently, followed by MutationTaster [13], CADD [14], PROVEAN [15], Mutpred [16], and Condel [17].”
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models (2012, methodology): “First, the VariBench database was used to benchmark our method against nine alternative computational prediction algorithms [Adzhubei et al., 2010; Bao et al., 2005; Bromberg and Rost, 2007; Calabrese et al., 2009; Capriotti et al., 2006; Li et al., 2009; Mort et al., 2010; Ng and Henikoff, 2001; Ra”
Performance of mutation pathogenicity prediction methods on missense variants (2011, methodology): “…Henikoff, 2001]), or a combination of protein structural and/or functional parameters and multiple sequence alignment derived information (MutPred [Li et al., 2009], nsSNPAnalyzer [Bao et al., 2005], PolyPhen [Ramensky et al., 2002], PolyPhen2 [Adzhubei et al., 2010], SNAP [Bromberg and Rost,…”