A Novel Coronavirus Genome Identified in a Cluster of Pneumonia Cases - Wuhan, China 2019-2020
Wenjie Tan1, Xiang Zhao1, Xuejun Ma1, Wenling Wang1, Peihua Niu1, Wenbo Xu1, George F. Gao1, Guizhen Wu2, Guizhen Wu1 •
01 Jan 2020-Vol. 2, Iss: 4, pp 61-62
About: The article was published on 2020-01-01 and is currently open access. It has received 558 citations till now. The article focuses on the topics: Coronavirus.
Citations
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Chaolin Huang1, Yeming Wang2, Xingwang Li3, Lili Ren4, Jianping Zhao5, Yi Hu5, Li Zhang1, Guohui Fan2, Jiuyang Xu6, Xiaoying Gu2, Zhenshun Cheng7, Ting Yu1, Jia'an Xia1, Yuan Wei1, Wenjuan Wu1, Xuelei Xie1, Wen Yin5, Li Hui2, Min Liu2, Yan Xiao4, Hong Gao4, Li Guo4, Jungang Xie5, Guang-Fa Wang8, Rongmeng Jiang3, Zhancheng Gao8, Qi Jin4, Jianwei Wang4, Bin Cao2 •
TL;DR: The epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of patients with laboratory-confirmed 2019-nCoV infection in Wuhan, China, were reported.
36,578 citations
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Na Zhu1, Dingyu Zhang, Wenling Wang1, Xingwang Li2, Bo Yang1, Jingdong Song1, Xiang Zhao1, Baoying Huang1, Weifeng Shi, Roujian Lu1, Peihua Niu1, Faxian Zhan1, Xuejun Ma1, Dayan Wang1, Wenbo Xu1, Wenbo Xu3, Guizhen Wu1, George F. Gao, Wenjie Tan1 •
TL;DR: Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily, which is the seventh member of the family of coronaviruses that infect humans.
Abstract: In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.).
21,455 citations
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Qun Li1, Xuhua Guan1, Peng Wu2, Xiaoye Wang1, Lei Zhou1, Yeqing Tong1, Ruiqi Ren1, Kathy Leung2, Eric H. Y. Lau2, Jessica Y. Wong2, Xuesen Xing1, Nijuan Xiang1, Yang Wu1, Chao Li1, Chen Qi1, Dan Li1, Tian Liu1, Jing Zhao1, Man Liu1, Wenxiao Tu1, Chuding Chen1, Lianmei Jin1, Rui Yang1, Qi Wang1, Suhua Zhou1, Rui Wang1, Hui Liu1, Yingbo Luo1, Yuan Liu1, Ge Shao1, Huan Li1, Zhongfa Tao1, Yang Yang3, Yang Yang4, Zhiqiang Deng5, Boxi Liu5, Zhitao Ma5, Yanping Zhang1, Guoqing Shi1, Tommy Tsan-Yuk Lam2, Joseph T. Wu2, George F. Gao6, George F. Gao1, Benjamin J. Cowling2, Bo Yang5, Gabriel M. Leung2, Zijian Feng1 •
TL;DR: There is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019 and considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere.
Abstract: Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the...
13,101 citations
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Roujian Lu1, Xiang Zhao1, Juan Li2, Peihua Niu1, Bo Yang3, Honglong Wu, Wenling Wang1, Hao Song4, Baoying Huang1, Na Zhu1, Yuhai Bi4, Xuejun Ma1, Faxian Zhan3, Liang Wang4, Tao Hu2, Hong Zhou2, Zhenhong Hu, Weimin Zhou1, Li Zhao1, Jing Chen5, Yao Meng1, Ji Wang1, Yang Lin, Jianying Yuan, Zhihao Xie, Jinmin Ma, William J. Liu1, Dayan Wang1, Wenbo Xu1, Edward C. Holmes6, George F. Gao1, George F. Gao4, Guizhen Wu1, Weijun Chen, Weifeng Shi2, Wenjie Tan1, Wenjie Tan4 •
TL;DR: The phylogenetic analysis suggests that bats might be the original host of this virus, an animal sold at the seafood market in Wuhan might represent an intermediate host facilitating the emergence of the virus in humans.
9,474 citations
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TL;DR: Results of PCR and viral RNA testing for SARS-CoV-2 in bronchoalveolar fluid, sputum, feces, blood, and urine specimens from patients with COVID-19 infection in China are described to identify possible means of non-respiratory transmission.
Abstract: This study describes results of PCR and viral RNA testing for SARS-CoV-2 in bronchoalveolar fluid, sputum, feces, blood, and urine specimens from patients with COVID-19 infection in China to identify possible means of non-respiratory transmission.
4,242 citations
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References
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TL;DR: This version of MAFFT has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update.
Abstract: We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
27,771 citations
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TL;DR: This work has used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches.
Abstract: The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/. (Algorithm; computer simulations; maximum likelihood; phylogeny; rbcL; RDPII project.) The size of homologous sequence data sets has in- creased dramatically in recent years, and many of these data sets now involve several hundreds of taxa. More- over, current probabilistic sequence evolution models (Swofford et al., 1996 ; Page and Holmes, 1998 ), notably those including rate variation among sites (Uzzell and Corbin, 1971 ; Jin and Nei, 1990 ; Yang, 1996 ), require an increasing number of calculations. Therefore, the speed of phylogeny reconstruction methods is becoming a sig- nificant requirement and good compromises between speed and accuracy must be found. The maximum likelihood (ML) approach is especially accurate for building molecular phylogenies. Felsenstein (1981) brought this framework to nucleotide-based phy- logenetic inference, and it was later also applied to amino acid sequences (Kishino et al., 1990). Several vari- ants were proposed, most notably the Bayesian meth- ods (Rannala and Yang 1996; and see below), and the discrete Fourier analysis of Hendy et al. (1994), for ex- ample. Numerous computer studies (Huelsenbeck and Hillis, 1993; Kuhner and Felsenstein, 1994; Huelsenbeck, 1995; Rosenberg and Kumar, 2001; Ranwez and Gascuel, 2002) have shown that ML programs can recover the cor- rect tree from simulated data sets more frequently than other methods can. Another important advantage of the ML approach is the ability to compare different trees and evolutionary models within a statistical framework (see Whelan et al., 2001, for a review). However, like all optimality criterion-based phylogenetic reconstruction approaches, ML is hampered by computational difficul- ties, making it impossible to obtain the optimal tree with certainty from even moderate data sets (Swofford et al., 1996). Therefore, all practical methods rely on heuristics that obtain near-optimal trees in reasonable computing time. Moreover, the computation problem is especially difficult with ML, because the tree likelihood not only depends on the tree topology but also on numerical pa- rameters, including branch lengths. Even computing the optimal values of these parameters on a single tree is not an easy task, particularly because of possible local optima (Chor et al., 2000). The usual heuristic method, implemented in the pop- ular PHYLIP (Felsenstein, 1993 ) and PAUP ∗ (Swofford, 1999 ) packages, is based on hill climbing. It combines stepwise insertion of taxa in a growing tree and topolog- ical rearrangement. For each possible insertion position and rearrangement, the branch lengths of the resulting tree are optimized and the tree likelihood is computed. When the rearrangement improves the current tree or when the position insertion is the best among all pos- sible positions, the corresponding tree becomes the new current tree. Simple rearrangements are used during tree growing, namely "nearest neighbor interchanges" (see below), while more intense rearrangements can be used once all taxa have been inserted. The procedure stops when no rearrangement improves the current best tree. Despite significant decreases in computing times, no- tably in fastDNAml (Olsen et al., 1994 ), this heuristic becomes impracticable with several hundreds of taxa. This is mainly due to the two-level strategy, which sepa- rates branch lengths and tree topology optimization. In- deed, most calculations are done to optimize the branch lengths and evaluate the likelihood of trees that are finally rejected. New methods have thus been proposed. Strimmer and von Haeseler (1996) and others have assembled four- taxon (quartet) trees inferred by ML, in order to recon- struct a complete tree. However, the results of this ap- proach have not been very satisfactory to date (Ranwez and Gascuel, 2001 ). Ota and Li (2000, 2001) described
16,261 citations
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TL;DR: 100 years after the infamous “Spanish flu” pandemic, the 2017–2018 flu season has been severe, with numerous infections worldwide.
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