SNAP predicts effect of mutations on protein function

By Yana Bromberg, Guy Yachdav, Burkhard Rost. 2008

264 citations recorded by the source corpus.

Summary: Many non-synonymous single nucleotide polymor-phisms (nsSNPs) in humans are suspected to impact protein function. Here, we present a publicly available server implementation of the method SNAP (screening for non-acceptable polymorphisms) that predicts the functional effects of single amino acid substitutions. SNAP identifies over 80% of the non-neutral mutations at 77% accuracy and over 76% of the neutral mutations at 80% accuracy at its default threshold. Each prediction is associated with a reliability index that correlates with accuracy and thereby enables experimentalists to zoom into the most promising predictions. Availability: Web-server: http://www.rostlab.org/services/SNAP; downloadable program available upon request. Contact: bromberg@rostlab.org Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants (2011, methodology): “Machine learning-based methods such as PhDSNP [30] and SNAP [18] have shown better results with respect to traditional methods.”
News from the protein mutability landscape. (2013, methodology): “, sorting intolerant from tolerant (SIFT) [22,23] or screening for non-acceptable polymorphisms (SNAP) [24,25]}, others predict the effect with respect to their pathogenicity (e.”
Assessment of computational methods for predicting the effects of missense mutations in human cancers (2013, methodology): “To our knowledge the neural network-based tool SNAP (screening for non-acceptable polymorphisms) [27,28] spans the most comprehensive feature space.”
Dysfunction of lipid sensor GPR120 leads to obesity in both mouse and human (2012, unknown): “…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 (Screening for Non-Acceptable Polymorphisms) software 47 ; and 5/ PMUT web-based program 48 .”
The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics (2013, methodology): “The authors of both SNAP [5,6] and MuD [7] used very nearly balanced training data sets and noted the effect of using imbalanced data sets in their papers.”
The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations (2013, background): “More than a dozen groups have devised meth-ods to analyze the effects a given SAAP will have and in some cases attempt to predict whether the mutation will have a deleterious effect on phenotype [4-15].”
Improving the prediction of disease-related variants using protein three-dimensional structure (2011, methodology): “These algorithms are able to predict the effect of the mutation on protein stability [13,14,19,23-26], protein functional activity [9,21], and insurgence of human pathologies [7,8,10-12,1518,20-22].”