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Clustering Based Identification of SARS-CoV-2 Subtypes

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Computational Advances in Bio and Medical Sciences (ICCABS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12686))

Abstract

With the availability of more than half a million SARS-CoV-2 sequences and counting, many approaches have recently appeared which aim to leverage this information towards understanding the genomic diversity and dynamics of this virus. Early approaches involved building transmission networks or phylogenetic trees, the latter for which scalability becomes more of an issue with each day, due to its high computational complexity.

In this work, we propose an alternative approach based on clustering sequences to identify novel subtypes of SARS-CoV-2 using methods designed for haplotyping intra-host viral populations. We assess this approach using cluster entropy, a notion which very naturally captures the underlying process of viral mutation—the first time entropy was used in this context. Using our approach, we were able to identify the well-known B.1.1.7 subtype from the sequences of the EMBL-EBI (UK) database, and also show that the associated cluster is consistent with a measure of fitness. This demonstrates that our approach as a viable and scalable alternative to unveiling trends in the spread of SARS-CoV-2.

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Acknowledgments

AM, SK, BS, RH and AZ were partially supported from NSF Grant 16119110, and NIH grant 1R01EB025022-01. FM and PS were partially supported from NIH grant 1R01EB025022-01. AM, BS and SK were partially supported by a GSU Molecular Basis of Disease Fellowship. MP was supported by a GSU startup grant.

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Authors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Andrew Melnyk, Fatemeh Mohebbi, Sergey Knyazev, Bikram Sahoo, Roya Hosseini, Pavel Skums, Alex Zelikovsky & Murray Patterson

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  1. Andrew Melnyk
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  2. Fatemeh Mohebbi
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  3. Sergey Knyazev
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  4. Bikram Sahoo
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  5. Roya Hosseini
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  6. Pavel Skums
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  7. Alex Zelikovsky
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  8. Murray Patterson
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Corresponding authors

Correspondence to Alex Zelikovsky or Murray Patterson .

Editor information

Editors and Affiliations

  1. The University of Texas at San Antonio, San Antonio, TX, USA

    Sumit Kumar Jha

  2. University of Connecticut, Storrs, CT, USA

    Ion Măndoiu

  3. University of Connecticut, Storrs Mansfield, CT, USA

    Sanguthevar Rajasekaran

  4. Department of Computer Science, Georgia State University, Roswell, GA, USA

    Pavel Skums

  5. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Alex Zelikovsky

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Melnyk, A. et al. (2021). Clustering Based Identification of SARS-CoV-2 Subtypes. In: Jha, S.K., Măndoiu, I., Rajasekaran, S., Skums, P., Zelikovsky, A. (eds) Computational Advances in Bio and Medical Sciences. ICCABS 2020. Lecture Notes in Computer Science(), vol 12686. Springer, Cham. https://doi.org/10.1007/978-3-030-79290-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-79290-9_11

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  • Print ISBN: 978-3-030-79289-3

  • Online ISBN: 978-3-030-79290-9

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