Emerging SARS-CoV-2 Diversity Revealed by Rapid Whole-Genome Sequence Typing.
Emerging SARS-CoV-2 Diversity Revealed by Rapid Whole-Genome Sequence Typing.
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DOI：
10.1093/gbe/evab197
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发表时间：
2021-09-01
影响因子：
3.3
通讯作者：
Planet PJ
中科院分区：
文献类型：
--
作者：
Moustafa AM;Planet PJ
SARS-CoV-2病毒基因型的离散分类可以识别新出现的毒株，并检测地理传播、病毒多样性和传播事件。我们开发了一个工具（基于gnu的病毒鉴定[GNUVID]），该工具集成了全基因组多位点序列分型和基于监督的机器学习随机森林分类器。我们使用GNUVID对GISAID提供的所有高质量基因组进行序列类型（ST）配置。STs聚类成克隆复合物（CCs），然后用于训练机器学习分类器。我们使用该工具检测潜在的引进和出口事件，并估计美国16个州不同地点和时间的有效病毒多样性。GNUVID是一种高度可扩展的病毒基因型分类工具（https://github.com/ahmedmagds/GNUVID），可以以与系统发育一致的方式快速分类数十万个基因组。我们的ST/CC基因分型分析揭示了不同州的ST/CC患病率和多样性的动态变化，在分析的时间段内，每个州平均有20.6个假定的引进和7.5个出口。我们介绍了有效多样性指标（希尔数）的使用，可用于估计干预措施（例如，旅行限制、疫苗接种、口罩规定）对传播病毒变异的影响。我们的分类工具揭示了多个输入和输出事件，以及不同州SARS-CoV-2基因型的扩展和替换浪潮。GNUVID分类有助于测量生态多样性，并且，通过系统的基因组采样，它可以用于跟踪循环病毒多样性和识别新兴克隆和热点。
Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. We developed a tool (GNU-based Virus IDentification [GNUVID]) that integrates whole-genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to all high-quality genomes available from GISAID. STs were clustered into clonal complexes (CCs) and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events and to estimate effective viral diversity across locations and over time in 16 US states. GNUVID is a highly scalable tool for viral genotype classification (https://github.com/ahmedmagds/GNUVID) that can quickly classify hundreds of thousands of genomes in a way that is consistent with phylogeny. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states, an average of 20.6 putative introductions and 7.5 exportations for each state over the time period analyzed. We introduce the use of effective diversity metrics (Hill numbers) that can be used to estimate the impact of interventions (e.g., travel restrictions, vaccine uptake, mask mandates) on the variation in circulating viruses. Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. GNUVID classification lends itself to measures of ecological diversity, and, with systematic genomic sampling, it could be used to track circulating viral diversity and identify emerging clones and hotspots.
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影响因子：
10.7
作者：
Dellicour S;Durkin K;Hong SL;Vanmechelen B;Martí-Carreras J;Gill MS;Meex C;Bontems S;André E;Gilbert M;Walker C;Maio N;Faria NR;Hadfield J;Hayette MP;Bours V;Wawina-Bokalanga T;Artesi M;Baele G;Maes P
通讯作者：
Maes P
影响因子：
5.8
作者：
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通讯作者：
de Oliveira, Tulio
影响因子：
56.9
作者：
Deng, Xianding;Gu, Wei;Chiu, Charles Y.
通讯作者：
Chiu, Charles Y.
影响因子：
3.2
作者：
Feil, EJ;Li, BC;Spratt, BG
通讯作者：
Spratt, BG
影响因子：
6.7
作者：
Dellicour S;Hong SL;Vrancken B;Chaillon A;Gill MS;Maurano MT;Ramaswami S;Zappile P;Marier C;Harkins GW;Baele G;Duerr R;Heguy A
通讯作者：
Heguy A