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RCoV19: A One-stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-warning
- Source :
- Genomics, Proteomics & Bioinformatics, Vol 21, Iss 5, Pp 1066-1079 (2023)
- Publication Year :
- 2023
- Publisher :
- Oxford University Press, 2023.
-
Abstract
- The Resource for Coronavirus 2019 (RCoV19) is an open-access information resource dedicated to providing valuable data on the genomes, mutations, and variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this updated implementation of RCoV19, we have made significant improvements and advancements over the previous version. Firstly, we have implemented a highly refined genome data curation model. This model now features an automated integration pipeline and optimized curation rules, enabling efficient daily updates of data in RCoV19. Secondly, we have developed a global and regional lineage evolution monitoring platform, alongside an outbreak risk pre-warning system. These additions provide a comprehensive understanding of SARS-CoV-2 evolution and transmission patterns, enabling better preparedness and response strategies. Thirdly, we have developed a powerful interactive mutation spectrum comparison module. This module allows users to compare and analyze mutation patterns, assisting in the detection of potential new lineages. Furthermore, we have incorporated a comprehensive knowledgebase on mutation effects. This knowledgebase serves as a valuable resource for retrieving information on the functional implications of specific mutations. In summary, RCoV19 serves as a vital scientific resource, providing access to valuable data, relevant information, and technical support in the global fight against COVID-19. The complete contents of RCoV19 are available to the public at https://ngdc.cncb.ac.cn/ncov/.
Details
- Language :
- English
- ISSN :
- 16720229
- Volume :
- 21
- Issue :
- 5
- Database :
- Directory of Open Access Journals
- Journal :
- Genomics, Proteomics & Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f184c259b9794e1fbebf334324d108e6
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.gpb.2023.10.004