gcCov: Linked open data for global coronavirus studies

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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Application Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9088579/
https://www.ncbi.nlm.nih.gov/pubmed/37731725
http://dx.doi.org/10.1002/mlf2.12008
Descripción
Sumario: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/.

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