The amount of sequencing data for SARS-CoV-2 is several orders of magnitude larger than any virus. This will continue to grow geometrically for SARS-CoV-2, and other viruses, as many countries heavily finance genomic surveillance efforts. Hence, we need methods for processing large amounts of sequence data to allow for effective yet timely decision-making. Such data will come from heterogeneous sources: aligned, unaligned, or even unassembled raw nucleotide or amino acid sequencing reads pertaining to the whole genome or regions (e.g., spike) of interest. In this work, we propose \emph{ViralVectors}, a compact feature vector generation from virome sequencing data that allows effective downstream analysis. Such generation is based on \emph{minimizers}, a type of lightweight "signature" of a sequence, used traditionally in assembly and read mapping -- to our knowledge, the first use minimizers in this way. We validate our approach on different types of sequencing data: (a) 2.5M SARS-CoV-2 spike sequences (to show scalability); (b) 3K Coronaviridae spike sequences (to show robustness to more genomic variability); and (c) 4K raw WGS reads sets taken from nasal-swab PCR tests (to show the ability to process unassembled reads). Our results show that ViralVectors outperforms current benchmarks in most classification and clustering tasks.

翻译：新冠病毒（SARS-CoV-2）的测序数据量比任何其他病毒高出数个数量级。随着多国大力资助基因组监测工作，这类数据规模将持续呈几何级增长——不仅限于SARS-CoV-2，其他病毒亦是如此。因此，我们需要能够处理海量序列数据的方法，以实现高效且及时的决策。这些数据将来自异质来源：包括全基因组或特定区域（如刺突蛋白）的对齐、未对齐甚至未组装的原始核苷酸或氨基酸测序读段。本文提出了一种称为ViralVectors的紧凑特征向量生成方法，可从病毒组测序数据中提取特征以实现高效下游分析。该生成方法基于minimizers——一种用于序列的轻量级"签名"技术，传统上用于序列组装和读段映射——据我们所知，这是首次以这种方式应用minimizers。我们在三类测序数据上验证了该方法：(a) 250万条SARS-CoV-2刺突蛋白序列（展示可扩展性）；(b) 3000条冠状病毒科刺突蛋白序列（展示对更高基因组变异性的鲁棒性）；(c) 4000组来自鼻拭子PCR检测的原始全基因组测序读段（展示处理未组装读段的能力）。结果表明，在大多数分类和聚类任务中，ViralVectors的性能优于当前基准方法。