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A Novel Network Representation of SARS-CoV-2 Sequencing Data

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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

The unprecedented level of genome sequencing during the SARS-CoV-2 pandemic brought about the challenge of processing this genomic data. However, the state-of-the-art phylogenetic methods were mostly designed for analyzing data that are significantly sparser and require extensive subsampling of strains. We present \((\varepsilon ,\tau )\)-MSN, a novel tool that reconstructs a viral genetic relatedness network based on genetic distances, that can process hundreds of thousands of sequences in under several hours. We applied \((\varepsilon ,\tau )\)-MSN to the global COVID-19 outbreak data and were able to build a genetic network on more than 100,000 SARS-CoV-2 sequences. We show that \((\varepsilon ,\tau )\)-MSN can accurately detect transmission events and build a genetic network with significantly higher assortativity with respect to continent and country attributes of SARS-CoV-2 samples. The source code for this software suite is available at https://github.com/Sergey-Knyazev/eMST.

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Acknowledgement

DN, SK, and AZ were partially supported by NSF grants 1564899 and 16119110 and by NIH grant 1R01EB025022-01. PS was partially supported by NIH grant 1R01EB025022-01 and NSF grant 2047828. SK was partially supported by the GSU Molecular Basis of Disease Fellowship. SM was partially supported by NSF grant 2041984.

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Authors and Affiliations

  1. Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA

    Sergey Knyazev

  2. Department of Computer Science, Georgia State University, Atlanta, USA

    Daniel Novikov, Roya Hosseini, Pavel Skums, Ellsworth Campbell & Alex Zelikovsky

  3. Department of Mathematics and Statistics, Georgia State University, Atlanta, USA

    Mark Grinshpon

  4. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Harman Singh

  5. Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, USA

    Ram Ayyala & Serghei Mangul

  6. Department of Computer Science, University of California Los Angeles, Los Angeles, USA

    Varuni Sarwal

  7. Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland

    Pelin Icer Baykal

  8. SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Pelin Icer Baykal

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  1. Sergey Knyazev
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  2. Daniel Novikov
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Corresponding author

Correspondence to Alex Zelikovsky .

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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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Knyazev, S. et al. (2021). A Novel Network Representation of SARS-CoV-2 Sequencing Data. 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_15

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  • DOI: https://doi.org/10.1007/978-3-030-91415-8_15

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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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