Genome variants which re-occur independently across evolutionary lineages are key molecular signatures of adaptation. Inferring the dynamics of such genetic changes from pandemic-scale genomic datasets is now possible, which opens up unprecedented insight into evolutionary processes. However, existing approaches depend on the construction of accurate phylogenetic trees, which remains challenging at scale. Here we present EVOtRec, an organism-agnostic, fast and scalable Topological Data Analysis approach that enables the inference of convergently evolving genomic variants over time directly from topological patterns in the dataset, without requiring the construction of a phylogenetic tree. Using data from both simulations and published experiments, we show that EVOtRec can robustly identify variants under positive selection and performs orders of magnitude faster than state-of-the-art phylogeny-based approaches, with comparable results. We apply EVOtRec to three large viral genome datasets: SARS-CoV-2, influenza virus A subtype H5N1 and HIV-1. We identify key convergent genome variants and demonstrate how EVOtRec facilitates the real-time tracking of high fitness variants in large datasets with millions of genomes, including effects modulated by varying genomic backgrounds. We envision our Topological Data Analysis approach as a new framework for efficient comparative genomics.

翻译：在进化谱系中独立重复出现的基因组变异是适应性进化的关键分子标志。如今，从疫情规模的基因组数据集中推断此类遗传变化的动态已成为可能，这为理解进化过程提供了前所未有的视角。然而，现有方法依赖于构建准确的系统发育树，这在规模较大时仍具挑战性。本文提出EVOtRec，一种与生物体无关、快速且可扩展的拓扑数据分析方法，能够直接从数据集中的拓扑模式推断随时间发生的趋同演化基因组变异，无需构建系统发育树。通过使用模拟数据和已发表的实验数据，我们证明EVOtRec能够稳健地识别正选择下的变异，其运行速度比基于系统发育的最新方法快数个数量级，且结果具有可比性。我们将EVOtRec应用于三个大型病毒基因组数据集：SARS-CoV-2、甲型流感病毒H5N1亚型和HIV-1。我们识别出关键的趋同基因组变异，并展示了EVOtRec如何促进在包含数百万个基因组的大型数据集中实时追踪高适应性变异，包括受不同基因组背景调控的效应。我们期望这种拓扑数据分析方法能够成为高效比较基因组学的新框架。