Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar

Mottaz, Anaïs; David, Fabrice; Veuthey, Anne‐Lise; Yip, Yum Lina

doi:10.1093/bioinformatics/btq028

Cited by 143 publications

(151 citation statements)

References 10 publications

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“…The benchmarks generally fall into two classes: those that distinguish presumed disease-causing variants from variants that have been observed but not associated with disease, and those that distinguish between mutations with and without effect in an experimental assay. In the first class, presumed disease-causing variants have been obtained from comprehensive databases, most commonly Swiss-Prot (Mottaz et al 2010), but also OMIM (Hamosh et al 2005) and HGMD (Stenson et al 2003) or from locus-specific databases [LSDBs, typically focusing on only a single human gene, are reviewed in (Greenblatt et al 2008)] such as the IARC TP53 database (Olivier et al 2002) and the BIC database on BRCA1 and BRCA2 (Goldgar et al 2004). Nondisease-associated variants have been obtained in diverse ways: from substitutions between closely related orthologs (Ramensky et al 2002), from non-disease-associated human variants reported in Swiss-Prot (Boeckmann et al 2003), or from either all or common (e.g., a minor allele frequency .1% in at least one population) human NSV alleles in a public resource like dbSNP (Sherry et al 2001).…”

Section: Assessment Of Nsv Impact Predictionsmentioning

confidence: 99%

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

Tang¹,

Thomas

2016

Genetics

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As personal genome sequencing becomes a reality, understanding the effects of genetic variants on phenotype-particularly the impact of germline variants on disease risk and the impact of somatic variants on cancer development and treatment-continues to increase in importance. Because of their clear potential for affecting phenotype, nonsynonymous genetic variants (variants that cause a change in the amino acid sequence of a protein encoded by a gene) have long been the target of efforts to predict the effects of genetic variation. Whole-genome sequencing is identifying large numbers of nonsynonymous variants in each genome, intensifying the need for computational methods that accurately predict which of these are likely to impact disease phenotypes. This review focuses on nonsynonymous variant prediction with two aims in mind: (1) to review the prioritization methods that have been developed to date and the principles on which they are based and (2) to discuss the challenges to further improving these methods.

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Section: Assessment Of Nsv Impact Predictionsmentioning

confidence: 99%

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

Tang¹,

Thomas

2016

Genetics

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“…predictSNPSelected, and SwissVarSelected, which have been manually curated to minimize possible overlaps and proposed to serve as a benchmarking set (26). The latter three, indicated by the suffix "Selected", are subsets of VariBench (12), predictSNP (13), and SwissVar (14), respectively, obtained upon clearing entries already represented in the former two most populated datasets (i.e., HumVar and ExoVar). Such preliminary filtering has been performed to allow for a fair comparison of the performances of pathogenicity predictors and to remove "training bias"-that is, any bias that might originate from partial overlap between the corresponding training and testing datasets.…”

Section: Significancementioning

confidence: 99%

“…Such investigations greatly benefited from the creation of publicly available databases of mutations found in humans and computational tools developed for pathogenicity prediction (2,(11)(12)(13)(14). Sequence conservation/evolution analyses using machine learning methods is a common approach in those tools.…”

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confidence: 99%

Structural Dynamics is a Determinant of the Functional Significance of Missense Variants

Ponzoni

Bahar

2018

Biophysical Journal

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Accurate evaluation of the effect of point mutations on protein function is essential to assessing the genesis and prognosis of many inherited diseases and cancer types. Currently, a wealth of computational tools has been developed for pathogenicity prediction. Two major types of data are used to this aim: sequence conservation/ evolution and structural properties. Here, we demonstrate in a systematic way that another determinant of the functional impact of missense variants is the protein's structural dynamics. Measurable improvement is shown in pathogenicity prediction by taking into consideration the dynamical context and implications of the mutation. Our study suggests that the class of dynamics descriptors introduced here may be used in conjunction with existing features to not only increase the prediction accuracy of the impact of variants on biological function, but also gain insight into the physical basis of the effect of missense variants.structural dynamics | missense variants | elastic network models | machine learning

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“…UniProtKB accession numbers provide unique identifiers for gene products, allowing direct look-up of the disease-association data from the selected SNP databases: MSV3d (Luu et al, 2012) and SwissVar (Mottaz et al, 2010). A list of 20,277 reviewed human proteins (representing the gene products of 19,700 genes) was compiled from UniProtKB (2012_06 release, accessed 1 November 2013).…”

Section: Data Sourcesmentioning

confidence: 99%

AACDS: A database for personal genome interpretation

Preeprem

Gibson

2014

EMBnet j.

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Incorporation of diverse data sources adds value to genomic studies, especially for annotation and categorisation of personal genome variants. The database for Association-Adjusted Consensus Deleterious Scheme (AACDS) and its Web application deliver a novel approach to assess genetic variations based on their putative functionality. The database is built upon integrated knowledge of variant data, with the aim of relating clinical phenotypes to predictions of variant deleteriousness. The simple but inter-related queries classify each variant into an 8-level category. The categories can be ranked, enabling straightforward interpretation of relative likelihood of functionality. The ranking thus facilitates improved efficiency in prioritising further detailed evaluation of key variants within a personal genome. The AACDS database covers more than 68 million mis-sense variants in approximately 18,000 human genes. Given a list of genetic variants, the retrieval of the AACDS category, along with known clinical data can be performed through an intuitive search platform.Availability: The AACDS Web application is publicly available at http://cig.gatech.edu/tools.

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Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar

Cited by 143 publications

References 10 publications

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

Structural Dynamics is a Determinant of the Functional Significance of Missense Variants

AACDS: A database for personal genome interpretation

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