VarCards: an integrated genetic and clinical database for coding variants in the human genome. Issue Volume 46:Issue D1(2018) (3rd November 2017)

• Record Type: Journal Article
• Authors: Li, Jinchen; Shi, Leisheng; Zhang, Kun; Zhang, Yi; Hu, Shanshan; Zhao, Tingting; Teng, Huajing; Li, Xianfeng; Jiang, Yi; Ji, Liying; Sun, Zhongsheng
• Is Part Of: Nucleic acids research. Volume 46:Issue D1(2018)
• Page Start: D1039
• Page End: D1048
• Publication Date: 2017-11-03
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkx1039
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: A growing number of genomic tools and databases were developed to facilitate the interpretation of genomic variants, particularly in coding regions. However, these tools are separately available in different online websites or databases, making it challenging for general clinicians, geneticists and biologists to obtain the first-hand information regarding some particular variants and genes of interest. Starting with coding regions and splice sties, we artificially generated all possible single nucleotide variants ( n = 110 154 363) and cataloged all reported insertion and deletions ( n = 1 223 370). We then annotated these variants with respect to functional consequences from more than 60 genomic data sources to develop a database, named VarCards (http://varcards.biols.ac.cn/ ), by which users can conveniently search, browse and annotate the variant- and gene-level implications of given variants, including the following information: (i) functional effects; (ii) functional consequences through different in silico algorithms; (iii) allele frequencies in different populations; (iv) disease- and phenotype-related knowledge; (v) general meaningful gene-level information; and (vi) drug–gene interactions. As a case study, we successfully employed VarCards in interpretation of de novo mutations in autism spectrum disorders. In conclusion, VarCards provides an intuitive interface of necessary information for researchers to prioritize candidate variations and genes.