Literature DB >> 24885522

LincSNP: a database of linking disease-associated SNPs to human large intergenic non-coding RNAs.

Shangwei Ning, Zuxianglan Zhao, Jingrun Ye, Peng Wang, Hui Zhi, Ronghong Li, Tingting Wang, Xia Li1.   

Abstract

BACKGROUND: Genome-wide association studies (GWAS) have successfully identified a large number of single nucleotide polymorphisms (SNPs) that are associated with a wide range of human diseases. However, many of these disease-associated SNPs are located in non-coding regions and have remained largely unexplained. Recent findings indicate that disease-associated SNPs in human large intergenic non-coding RNA (lincRNA) may lead to susceptibility to diseases through their effects on lincRNA expression. There is, therefore, a need to specifically record these SNPs and annotate them as potential candidates for disease. DESCRIPTION: We have built LincSNP, an integrated database, to identify and annotate disease-associated SNPs in human lincRNAs. The current release of LincSNP contains approximately 140,000 disease-associated SNPs (or linkage disequilibrium SNPs), which can be mapped to around 5,000 human lincRNAs, together with their comprehensive functional annotations. The database also contains annotated, experimentally supported SNP-lincRNA-disease associations and disease-associated lincRNAs. It provides flexible search options for data extraction and searches can be performed by disease/phenotype name, SNP ID, lincRNA name and chromosome region. In addition, we provide users with a link to download all the data from LincSNP and have developed a web interface for the submission of novel identified SNP-lincRNA-disease associations.
CONCLUSIONS: The LincSNP database aims to integrate disease-associated SNPs and human lincRNAs, which will be an important resource for the investigation of the functions and mechanisms of lincRNAs in human disease. The database is available at http://bioinfo.hrbmu.edu.cn/LincSNP.

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Year:  2014        PMID: 24885522      PMCID: PMC4038069          DOI: 10.1186/1471-2105-15-152

Source DB:  PubMed          Journal:  BMC Bioinformatics        ISSN: 1471-2105            Impact factor:   3.169


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