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

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Bioinformatics Research and Applications (ISBRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13064))

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Abstract

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 (\(1\%\) 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).

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Notes

  1. 1.

    https://www.gisaid.org/.

  2. 2.

    https://github.com/sarwanpasha/covid_variant_classification.

  3. 3.

    https://www.gisaid.org/.

  4. 4.

    https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-info.html.

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Author information

Authors and Affiliations

  1. Georgia State University, Atlanta, GA, USA

    Sarwan Ali, Bikram Sahoo, Alexander Zelikovskiy & Murray Patterson

  2. Lahore University of Management Sciences, Lahore, Pakistan

    Naimat Ullah & Imdadullah Khan

Authors
  1. Sarwan Ali
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  2. Bikram Sahoo
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  3. Naimat Ullah
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  4. Alexander Zelikovskiy
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  5. Murray Patterson
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  6. Imdadullah Khan
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Corresponding authors

Correspondence to Murray Patterson or Imdadullah Khan .

Editor information

Editors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Yanjie Wei

  2. Central South University, Changsha, China

    Min Li

  3. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  4. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

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Ali, S., Sahoo, B., Ullah, N., Zelikovskiy, A., Patterson, M., Khan, I. (2021). A k-mer Based Approach for SARS-CoV-2 Variant Identification. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_14

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  • DOI: https://doi.org/10.1007/978-3-030-91415-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91414-1

  • Online ISBN: 978-3-030-91415-8

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