Cargando…
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...
| Autores principales: | , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
John Wiley and Sons Inc.
2022
|
| Materias: | |
| 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 |
| _version_ | 1784704355823255552 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Shi, Wenyu |
| collection | PubMed |
| description | 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/. |
| format | Online Article Text |
| id | pubmed-9088579 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2022 |
| publisher | John Wiley and Sons Inc. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-90885792022-05-10 gcCov: Linked open data for global coronavirus studies 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 mLife Application Note 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/. John Wiley and Sons Inc. 2022-03-16 2022-03 /pmc/articles/PMC9088579/ /pubmed/37731725 http://dx.doi.org/10.1002/mlf2.12008 Text en © 2022 The Authors. mLife published by John Wiley & Sons Australia, Ltd. on behalf of Institute of Microbiology, Chinese Academy of Sciences. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| spellingShingle | Application Note 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 gcCov: Linked open data for global coronavirus studies |
| title | gcCov: Linked open data for global coronavirus studies |
| title_full | gcCov: Linked open data for global coronavirus studies |
| title_fullStr | gcCov: Linked open data for global coronavirus studies |
| title_full_unstemmed | gcCov: Linked open data for global coronavirus studies |
| title_short | gcCov: Linked open data for global coronavirus studies |
| title_sort | gccov: linked open data for global coronavirus studies |
| topic | Application Note |
| url | 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 |
| work_keys_str_mv | AT shiwenyu gccovlinkedopendataforglobalcoronavirusstudies AT fanguomei gccovlinkedopendataforglobalcoronavirusstudies AT shenzhihong gccovlinkedopendataforglobalcoronavirusstudies AT huchuan gccovlinkedopendataforglobalcoronavirusstudies AT majuncai gccovlinkedopendataforglobalcoronavirusstudies AT zhouyuanchun gccovlinkedopendataforglobalcoronavirusstudies AT mengzhen gccovlinkedopendataforglobalcoronavirusstudies AT husongnian gccovlinkedopendataforglobalcoronavirusstudies AT biyuhai gccovlinkedopendataforglobalcoronavirusstudies AT wangliang gccovlinkedopendataforglobalcoronavirusstudies AT yuhaiying gccovlinkedopendataforglobalcoronavirusstudies AT linsiru gccovlinkedopendataforglobalcoronavirusstudies AT sunxiuqiang gccovlinkedopendataforglobalcoronavirusstudies AT zhangxinjiao gccovlinkedopendataforglobalcoronavirusstudies AT liudongmei gccovlinkedopendataforglobalcoronavirusstudies AT sunqinlan gccovlinkedopendataforglobalcoronavirusstudies AT wulinhuan gccovlinkedopendataforglobalcoronavirusstudies |