- SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants. [PMID: 22075996]
Greet De Baets, Joost Van Durme, Joke Reumers, Sebastian Maurer-Stroh, Peter Vanhee, Joaquin Dopazo, Joost Schymkowitz, Frederic Rousseau
Nucleic acids research 2012:40(Database issue)
65 Citations (Google Scholar as of 2016-01-22)
Abstract: Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.
- SNPeffect v2.0: a new step in investigating the molecular phenotypic effects of human non-synonymous SNPs. [PMID: 16809394]
Joke Reumers, Sebastian Maurer-Stroh, Joost Schymkowitz, Frederic Rousseau
Bioinformatics (Oxford, England) 2006:22(17)
83 Citations (Google Scholar as of 2016-01-22)
Abstract: Single nucleotide polymorphisms (SNPs) constitute the most fundamental type of genetic variation in human populations. About 75 000 of these reported variations cause an amino acid change in the translated protein. An important goal in genomic research is to understand how this variability affects protein function, and whether or not particular SNPs are associated to disease susceptibility. Accordingly, the SNPeffect database uses sequence- and structure-based bioinformatics tools to predict the effect of non-synonymous SNPs on the molecular phenotype of proteins. SNPeffect analyses the effect of SNPs on three categories of functional properties: (1) structural and thermodynamic properties affecting protein dynamics and stability (2) the integrity of functional and binding sites and (3) changes in posttranslational processing and cellular localization of proteins. The search interface of the database can be used to search specifically for polymorphisms that are predicted to cause a change in one of these properties. Now based on the Ensembl human databases, the SNPeffect database has been remodeled to better fit an automatically updatable structure. The current edition holds the molecular phenotype of 74 567 nsSNPs in 23 426 proteins. SNPeffect can be accessed through http://snpeffect.vib.be.
- SNPeffect: a database mapping molecular phenotypic effects of human non-synonymous coding SNPs. [PMID: 15608254]
Joke Reumers, Joost Schymkowitz, Jesper Ferkinghoff-Borg, Francois Stricher, Luis Serrano, Frederic Rousseau
Nucleic acids research 2005:33(Database issue)
113 Citations (Google Scholar as of 2016-01-20)
Abstract: Single nucleotide polymorphisms (SNPs) are an increasingly important tool for genetic and biomedical research. However, the accumulated sequence information on allelic variation is not matched by an understanding of the effect of SNPs on the functional attributes or 'molecular phenotype' of a protein. Towards this aim we developed SNPeffect, an online resource of human non-synonymous coding SNPs (nsSNPs) mapping phenotypic effects of allelic variation in human genes. SNPeffect contains 31 659 nsSNPs from 12 480 human proteins. The current release of SNPeffect incorporates data on protein stability, integrity of functional sites, protein phosphorylation and glycosylation, subcellular localization, protein turnover rates, protein aggregation, amyloidosis and chaperone interaction. The SNP entries are accessible through both a search and browse interface and are linked to most major biological databases. The data can be displayed as detailed descriptions of individual SNPs or as an overview of all SNPs for a given protein. SNPeffect will be regularly updated and can be accessed at http://snpeffect.vib.be/.