The COVID-19 pandemic has highlighted the critical role of genomic surveillance for guiding policy and control. Timeliness is key, but sequence alignment and phylogeny slow most surveillance techniques. Millions of SARS-CoV-2 genomes have been assembled. Phylogenetic methods are ill equipped to handle this sheer scale. We introduce a pangenomic measure that examines the information diversity of a k-mer library drawn from a country's complete set of clinical, pooled, or wastewater sequence. Quantifying diversity is central to ecology. Hill numbers, or the effective number of species in a sample, provide a simple metric for comparing species diversity across environments. The more diverse the sample, the higher the Hill number. We adopt this ecological approach and consider each k-mer an individual and each genome a transect in the pangenome of the species. Structured in this way, Hill numbers summarize the temporal trajectory of pandemic variants, collapsing each day's assemblies into genome equivalents. For pooled or wastewater sequence, we instead compare days using survey sequence divorced from individual infections. Across data from the UK, USA, and South Africa, we trace the ascendance of new variants of concern as they emerge in local populations well before these variants are named and added to phylogenetic databases. Using data from San Diego wastewater, we monitor these same population changes from raw, unassembled sequence. This history of emerging variants senses all available data as it is sequenced, intimating variant sweeps to dominance or declines to extinction at the leading edge of the COVID-19 pandemic.

中文翻译：

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使用临床、混合或废水序列作为种群变化传感器进行快速 SARS-CoV-2 监测


COVID-19 大流行凸显了基因组监测在指导政策和控制方面的关键作用。及时性是关键，但序列比对和系统发育会减慢大多数监测技术的速度。已经组装了数百万个 SARS-CoV-2 基因组。系统发育方法无法处理如此庞大的规模。我们引入了一种泛基因组学测量方法，用于检查从一个国家/地区的全套临床、混合或废水序列中提取的 k-mer 文库的信息多样性。量化多样性是生态学的核心。山数或样本中物种的有效数量为比较不同环境中的物种多样性提供了一个简单的指标。样本越多样化，Hill 数越高。我们采用这种生态学方法，并将每个 k-mer 视为一个个体，每个基因组是该物种泛基因组中的一个横断面。以这种方式构建，希尔数字总结了大流行变异的时间轨迹，将每天的集合分解成基因组等价物。对于混合或废水序列，我们改用与个体感染分离的调查序列来比较天数。在来自英国、美国和南非的数据中，我们追踪了令人担忧的新变异的优势，因为它们早在当地种群中出现，早在这些变异被命名并添加到系统发育数据库之前。使用来自圣地亚哥废水的数据，我们从原始的、未组装的序列中监测这些相同的种群变化。这种新出现的变体的历史在测序时感知到所有可用数据，暗示在 COVID-19 大流行的前沿，变体席卷到主导地位或下降到灭绝。