# ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation

arXiv: https://arxiv.org/abs/2304.02891 · HTML: https://mlsanity.com/paper/2304.02891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 60 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences](https://mlsanity.com/paper/2109.05019/) (2021)
- [A k-mer Based Approach for SARS-CoV-2 Variant Identification](https://mlsanity.com/paper/2108.03465/) (2021)
- [PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences](https://mlsanity.com/paper/2201.02273/) (2022)
- [Efficient Approximate Kernel Based Spike Sequence Classification](https://mlsanity.com/paper/2209.04952/) (2022)
- [Effective and scalable clustering of SARS-CoV-2 sequences](https://mlsanity.com/paper/2108.08143/) (2021)
- [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://mlsanity.com/paper/2110.09622/) (2021)
- [Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data](https://mlsanity.com/paper/2211.08267/) (2022)
- [Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification](https://mlsanity.com/paper/2207.08898/) (2022)
- [Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences](https://mlsanity.com/paper/2211.09263/) (2022)
- [Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning](https://mlsanity.com/paper/2302.08688/) (2023)
- [Exploring The Potential Of GANs In Biological Sequence Analysis](https://mlsanity.com/paper/2303.02421/) (2023)
- [Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model](https://mlsanity.com/paper/2211.10546/) (2022)
- [Lower density selection schemes via small universal hitting sets with short remaining path length](https://mlsanity.com/paper/2001.06550/) (2020)
- [Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location](https://mlsanity.com/paper/2110.00809/) (2021)
- [KMC 2: Fast and resource-frugal k-mer counting](https://mlsanity.com/paper/1407.1507/) (2014)

## Built on by (most cited first, 1 of 1)

- [Virus2Vec: Viral Sequence Classification Using Machine Learning](https://mlsanity.com/paper/2304.12328/) (2023)

## Abstract (arXiv)

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 ViralVectors, a compact feature vector generation from virome sequencing data that allows effective downstream analysis. Such generation is based on 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.