ABSTRACT
Haplotype network is becoming popular due to its increasing use in analyzing genealogical relationships of closely related genomes. We newly proposed McAN, a minimum-cost arborescence based haplotype network con-struction algorithm, by considering mutation spectrum history (mutations in ancestry haplotype should be contained in descendant haplotype), node size (corresponding to sample count for a given node) and sampling time. McAN is two orders of magnitude faster than the state-of-the-art algorithms, making it suitable for analyzation of massive se-quences. Availability: Source code is written in C/C++ and available at https://github.com/Theory-Lun/McAN and https://ngdc.cncb.ac.cn/biocode/tools/BT007301 under the MIT license. The online web service of McAN is available at https://ngdc.cncb.ac.cn/ncov/online/tool/haplotype. SARS-CoV-2 dataset are available at https://ngdc.cncb.ac.cn/ncov/.
ABSTRACT
The COVID-19 pandemic poses a great threat to human society. SARS-CoV-2 is mainly transmitted through social contact; however, it is highly debated whether cold-chain related transmission has occurred and can be identified in the epidemic areas of COVID-19. Here, we provide a new method and distinguish two transmission routes by detecting a lineage-specific reduction of SARS-CoV-2 mutation rate. After analyzing 1,610,125 SARS-CoV-2 genomic sequences, we find that two outbreaks in Xinfadi-Beijing and Auckland are cold-chain related and respectively caused by two mutation-dormant variants. A Dalian outbreak in July 2020 and a Yingkou outbreak ten months later are epidemiologically connected and derived from a cold-chain related variant. Mutation-dormant variants are detected during the spread of spike D614G variant and the Delta Variant of Concern. Cold-chain contaminations repeatedly caused by epidemiologically connected patients are also found and have resulted in infections. Moreover, the COVID-19 outbreak in Wuhan is likely to be cold-chain related. A systematic identification reveals that the frequency of cold-chain related transmission is in the order of magnitude of 0.1-10%. Our results indicate that that cold-chain related transmission is rare but happens globally.
Subject(s)
COVID-19ABSTRACT
COVID-19 has widely spread across the world, and much research is being conducted on the causative virus SARS-CoV-2. To help control the infection, we developed the Coronavirus GenBrowser (CGB) to monitor the pandemic. CGB allows visualization and analysis of the latest viral genomic data. Distributed genome alignments and an evolutionary tree built on the existing subtree are implemented for easy and frequent updates. The tree-based data are compressed at a ratio of 2,760:1, enabling fast access and analysis of SARS-CoV-2 variants. CGB can effectively detect adaptive evolution of specific alleles, such as D614G of the spike protein, in their early stage of spreading. By lineage tracing, the most recent common ancestor, dated in early March 2020, of nine strains collected from six different regions in three continents was found to cause the outbreak in Xinfadi, Beijing, China in June 2020. CGB also revealed that the first COVID-19 outbreak in Washington State was caused by multiple introductions of SARS-CoV-2. To encourage data sharing, CGB credits the person who first discovers any SARS-CoV-2 variant. As CGB is developed with eight different languages, it allows the general public in many regions of the world to easily access pre-analyzed results of more than 132,000 SARS-CoV-2 genomes. CGB is an efficient platform to monitor adaptive evolution and transmission of SARS-CoV-2.
Subject(s)
COVID-19ABSTRACT
On 22 January 2020, the National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), created the 2019 Novel Coronavirus Resource (2019nCoVR), an open-access SARS-CoV-2 information resource. 2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates, which are manually curated with value-added annotations and quality evaluated by our in-house automated pipeline. Of particular note, 2019nCoVR performs systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale. It provides all identified variants and detailed statistics for each virus isolate, and congregates the quality score, functional annotation, and population frequency for each variant. It also generates visualization of the spatiotemporal change for each variant and yields historical viral haplotype network maps for the course of the outbreak from all complete and high-quality genomes. Moreover, 2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on COVID-19 (Coronavirus Disease 2019), including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC. Furthermore, by linking with relevant databases in CNCB-NGDC, 2019nCoVR offers data submission services for raw sequence reads and assembled genomes, and data sharing with National Center for Biotechnology Information. Collectively, all SARS-CoV-2 genome sequences, variants, haplotypes and literature are updated daily to provide timely information, making 2019nCoVR a valuable resource for the global research community. 2019nCoVR is accessible at https://bigd.big.ac.cn/ncov/.