  The SARS-CoV-2 coronavirus is the cause of the COVID-19 disease in humans.
Like many coronaviruses, it can adapt to different hosts and evolve into
different lineages. It is well-known that the major SARS-CoV-2 lineages are
characterized by mutations that happen predominantly in the spike protein.
Understanding the spike protein structure and how it can be perturbed is vital
for understanding and determining if a lineage is of concern. These are crucial
to identifying and controlling current outbreaks and preventing future
pandemics. Machine learning (ML) methods are a viable solution to this effort,
given the volume of available sequencing data, much of which is unaligned or
even unassembled. However, such ML methods require fixed-length numerical
feature vectors in Euclidean space to be applicable. Similarly, euclidean space
is not considered the best choice when working with the classification and
clustering tasks for biological sequences. For this purpose, we design a method
that converts the protein (spike) sequences into the sequence similarity
network (SSN). We can then use SSN as an input for the classical algorithms
from the graph mining domain for the typical tasks such as classification and
clustering to understand the data. We show that the proposed alignment-free
method is able to outperform the current SOTA method in terms of clustering
results. Similarly, we are able to achieve higher classification accuracy using
well-known Node2Vec-based embedding compared to other baseline embedding
approaches.
