GcCov: Linked open data for global coronavirus studies. Issue 1 (16th March 2022)

• Record Type: Journal Article
• Authors: Shi, Wenyu; Fan, Guomei; Shen, Zhihong; Hu, Chuan; Ma, Juncai; Zhou, Yuanchun; Meng, Zhen; Hu, Songnian; Bi, Yuhai; Wang, Liang; Yu, Haiying; Lin, Siru; Sun, Xiuqiang; Zhang, Xinjiao; Liu, Dongmei; Sun, Qinlan; Wu, Linhuan
• Is Part Of: MLife. Volume 1:Issue 1(2022)
• Page Start: 92
• Page End: 95
• Publication Date: 2022-03-16
• Subjects: Microbiology -- Periodicals; Microbiology; Periodicals; 579; 579
• DOI: 10.1002/mlf2.12008
• Languages: English
• ISSNs: 2770-100X

Abstract

Impact statement: We present a method of mapping data from publicly available genomics and publication resources to the Resource Description Framework (RDF) and implement a server to publish linked open data (LOD). As one of the largest and most comprehensive semantic databases about coronaviruses, the resulted gcCov database demonstrates the capability of using data in the LOD framework to promote correlations between genotypes and phenotypes. These correlations will be helpful for future research on fundamental viral mechanisms and drug and vaccine designs. These LOD with 62, 168, 127 semantic triplets and their visualizations are freely accessible through gcCov at https://nmdc.cn/gccov/ .