A k-mer Based Approach for SARS-CoV-2 Variant Identification

• Published in: Bioinformatics Research and Applications Vol. 13064; pp. 153 - 164
• Main Authors: Ali, Sarwan, Sahoo, Bikram, Ullah, Naimat, Zelikovskiy, Alexander, Patterson, Murray, Khan, Imdadullah
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 9783030914141; 3030914143
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-91415-8_14

With the rapid spread of the novel coronavirus (COVID-19) across the globe and its continuous mutation, it is of pivotal importance to design a system to identify different known (and unknown) variants of SARS-CoV-2. Identifying particular variants helps to understand and model their spread patterns, design effective mitigation strategies, and prevent future outbreaks. It also plays a crucial role in studying the efficacy of known vaccines against each variant, and modeling the likelihood of breakthrough infections. It is well known that the spike protein contains most of the information/variation pertaining to coronavirus variants. In this paper, we use spike sequences to classify different variants of the human SARS-CoV-2. We show that preserving order information of the amino acids helps the underlying classifiers to achieve better performance. We also show that we can train our model to outperform the baseline algorithms using only a small number of training samples (1% $$1\%$$ of the data). Finally, we show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA’s Centers for Disease Control and Prevention (CDC).