2010
DOI: 10.1093/bioinformatics/btq028
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Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar

Abstract: Summary: The SwissVar portal provides access to a comprehensive collection of single amino acid polymorphisms and diseases in the UniProtKB/Swiss-Prot database via a unique search engine. In particular, it gives direct access to the newly improved Swiss-Prot variant pages. The key strength of this portal is that it provides a possibility to query for similar diseases, as well as the underlying protein products and the molecular details of each variant. In the context of the recently proposed molecular view on … Show more

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Cited by 146 publications
(151 citation statements)
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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%
“…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%
“…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.…”
mentioning
confidence: 99%
“…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%