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Uveitis is an intraocular inflammatory disease which can lead to serious visual impairment. Genetic factors have been shown to be involved in its development. However, few databases have focused on the information of associations between single nucleotide polymorphisms (SNPs) and uveitis. To discover the exact genetic background of uveitis, we developed an SNP database specific for uveitis, "UVEOGENE," which includes 370 genes and 918 SNPs covering 14 uveitis entities and 40 populations from 286 PubMed English-language papers. Stratification analyses by gender, HLA status, and different clinical features were also extracted from the publications. As a result, 371 associations were judged as "statistically significant." These associations were also shared with Global Variome shared Leiden Open Variation Database (LOVD) (https://databases.lovd.nl/shared/genes). Based on these associations, we investigated the genetic relationship among three widely studied uveitis entities including Behcet's disease (BD), Vogt-Koyanagi-Harada (VKH) disease, and acute anterior uveitis (AAU). Furthermore, "UVEOGENE" can be used as a reliable and informative resource to identify similarities as well as differences in the genetic susceptibility among uveitis and other autoimmune diseases. UVEOGENE is freely accessible at http://www.uvogene.com.
Pubmed ID: 30614601 RIS Download
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Archive of aggregated information about sequence variation and its relationship to human health. Provides reports of relationships among human variations and phenotypes along with supporting evidence. Submissions from clinical testing labs, research labs, locus-specific databases, expert panels and professional societies are welcome. Collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about submitter, and other supporting data. Alleles described in submissions are mapped to reference sequences, and reported according to HGVS standard.
View all literature mentionsWeb server to identify statistically enriched pathways, diseases, and GO terms for a set of genes or proteins, using pathway, disease, and GO knowledge from multiple famous databases. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). A standalone command line version is also available
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
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