We present a novel framework enhancing the prediction of whether novel lineage poses the threat of eventually dominating the viral population. The framework is based purely on genomic sequence data, without requiring prior established biological analysis. Its building blocks are sets of coevolving sites in the alignment (motifs), identified via coevolutionary signals. The collection of such motifs forms a relational structure over the polymorphic sites. Motifs are constructed using distances quantifying the coevolutionary coupling of pairs and manifest as coevolving clusters of sites. We present an approach to genomic surveillance based on this notion of relational structure. Our system will issue an alert regarding a lineage, based on its contribution to drastic changes in the relational structure. We then conduct a comprehensive retrospective analysis of the COVID-19 pandemic based on SARS-CoV-2 genomic sequence data in GISAID from October 2020 to September 2022, across 21 lineages and 27 countries with weekly resolution. We investigate the performance of this surveillance system in terms of its accuracy, timeliness, and robustness. Lastly, we study how well each lineage is classified by such a system.

中文翻译：

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SARS-CoV-2 进化中的基序

我们提出了一个新的框架，增强了对新谱系是否构成最终控制病毒种群的威胁的预测。该框架纯粹基于基因组序列数据，不需要事先建立的生物学分析。它的构建模块是对齐（基序）中的一组共同进化位点，通过共同进化信号识别。这些基序的集合形成了多态性位点上的关系结构。基序是使用量化配对的共同进化耦合的距离来构建的，并表现为共同进化的位点簇。我们提出了一种基于关系结构概念的基因组监测方法。我们的系统将根据谱系对关系结构剧烈变化的贡献发出有关谱系的警报。然后，我们根据 2020 年 10 月至 2022 年 9 月 GISAID 中的 SARS-CoV-2 基因组序列数据，对 21 个谱系和 27 个国家的 COVID-19 大流行进行了全面的回顾性分析，每周进行一次分析。我们调查该监控系统的准确性、及时性和稳健性方面的性能。最后，我们研究了这样一个系统对每个谱系的分类效果。