Sign in
BioinformaticsGenomics Proteomics & Bioinformatics
Peer-reviewed journal article

RCoV19: A One-stop Hub for SARS-CoV-2 Genome Data Integration, Variants Monitoring, and Risk Pre-warning

Cuiping LiiD, Lina MaiD, Dong ZouiD, Rongqin ZhangiD, Xue BaiiD, Lun LiiD, Gangao WuiD, Tianhao HuangiD, Wei ZhaoiD, Enhui JiniD, Yīmíng BàoiD, Shuhui SongiD

Abstract

The Resource for Coronavirus 2019 (RCoV19, https://ngdc.cncb.ac.cn/ncov/) 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.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISSong '20Rakha '20Fernandes '20Desai '21Rahimian '23Wittig '21
70% semantic matchGenomics
Shuhui Song, Lina Ma, Dong Zou+27 more·Genomics Proteomics & Bioinformatics·Dec 1, 2020· 112 citations