ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation

• URL: http://arxiv.org/abs/2304.02891v2
• Date: Fri, 7 Apr 2023 11:58:23 GMT
• Title: ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation
• Authors: Sarwan Ali, Prakash Chourasia, Zahra Tayebi, Babatunde Bello, Murray Patterson
• Abstract summary: The amount of sequencing data for SARS-CoV-2 is several orders of magnitude larger than any virus. We propose emphsignatures, a compact vector generation from virome sequencing data that allows effective downstream analysis.
• Score: 0.7874708385247353
• License: http://creativecommons.org/licenses/by/4.0/
• 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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