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The massive amount of genomic data appearing for SARS-CoV-2 since thebeginning of the COVID-19 pandemic has challenged traditional methods forstudying its dynamics. As a result, new methods such as Pangolin, which canscale to the millions of samples of SARS-CoV-2 currently available, haveappeared. Such a tool is tailored to take as input assembled, aligned andcurated full-length sequences, such as those found in the GISAID database. Ashigh-throughput sequencing technologies continue to advance, such assembly,alignment and curation may become a bottleneck, creating a need for methodswhich can process raw sequencing reads directly.  In this paper, we propose Reads2Vec, an alignment-free embedding approachthat can generate a fixed-length feature vector representation directly fromthe raw sequencing reads without requiring assembly. Furthermore, since such anembedding is a numerical representation, it may be applied to highly optimizedclassification and clustering algorithms. Experiments on simulated data showthat our proposed embedding obtains better classification results and betterclustering properties contrary to existing alignment-free baselines. In a studyon real data, we show that alignment-free embeddings have better clusteringproperties than the Pangolin tool and that the spike region of the SARS-CoV-2genome heavily informs the alignment-free clusterings, which is consistent withcurrent biological knowledge of SARS-CoV-2.

Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data