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

ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation