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arXiv · 2304.02891

ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation

Sarwan Ali·Prakash Chourasia·Zahra Tayebi·Babatunde Bello·Murray Patterson
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Abstract

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.

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Sarwan Ali, Prakash Chourasia, Zahra Tayebi, Babatunde Bello, Murray Patterson. 2023-04-06. ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation. https://arxiv.org/abs/2304.02891

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