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Clustering SARS-CoV-2 Variants from Raw High-Throughput Sequencing Reads Data

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Computational Advances in Bio and Medical Sciences (ICCABS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13254))

Abstract

The massive amount of genomic data appearing over the past two years for SARS-CoV-2 has challenged traditional methods for studying the dynamics of the COVID-19 pandemic. As a result, new methods, such as the Pangolin tool, have appeared which can scale to the millions of samples of SARS-CoV-2 currently available. Such a tool is tailored to take assembled, aligned and curated full-length sequences, such as those provided by GISAID, as input. As high-throughput sequencing technologies continue to advance, such assembly, alignment and curation may become a bottleneck, creating a need for methods which can process raw sequencing reads directly.

In this paper, we propose several alignment-free embedding approaches, which can generate a fixed-length feature vector representation directly from the raw sequencing reads, without the need for assembly. Moreover, because such an embedding is a numerical representation, it can be passed to already highly optimized clustering methods such as k-means. We show that the clusterings we obtain with the proposed embeddings are more suited to this setting than the Pangolin tool, based on several internal clustering evaluation metrics. Moreover, we show that a disproportionate number of positions in the spike region of the SARS-CoV-2 genome are informing such clusterings (in terms of information gain), which is consistent with current biological knowledge of SARS-CoV-2.

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Notes

  1. 1.

    https://www.ncbi.nlm.nih.gov/sars-cov-2/.

  2. 2.

    https://covid-19.ensembl.org/index.html.

  3. 3.

    https://github.com/murraypatterson/ncbi-sra-runs-pipeline.

  4. 4.

    https://drive.google.com/drive/folders/1i4uRrnkjkwUA93EOl8YORBBLb7yIFIm1?usp=sharing.

  5. 5.

    https://github.com/murraypatterson/ncbi-sra-runs-pipeline.

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Authors and Affiliations

  1. Georgia State University, Atlanta, GA, 30303, USA

    Prakash Chourasia, Sarwan Ali & Murray Patterson

  2. University of Milano-Bicocca, Milan, Italy

    Simone Ciccolella & Gianluca Della Vedova

Authors
  1. Prakash Chourasia
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  2. Sarwan Ali
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  3. Simone Ciccolella
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  4. Gianluca Della Vedova
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  5. Murray Patterson
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Corresponding author

Correspondence to Murray Patterson .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Mukul S. Bansal

  2. University of Connecticut, Storrs, CT, USA

    Ion Măndoiu

  3. University of Connecticut Health Center, Farmington, CT, USA

    Marmar Moussa

  4. Georgia State University, Atlanta, GA, USA

    Murray Patterson

  5. University of Connecticut, Storrs, CT, USA

    Sanguthevar Rajasekaran

  6. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  7. Georgia State University, Atlanta, GA, USA

    Alexander Zelikovsky

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Chourasia, P., Ali, S., Ciccolella, S., Della Vedova, G., Patterson, M. (2022). Clustering SARS-CoV-2 Variants from Raw High-Throughput Sequencing Reads Data. In: Bansal, M.S., et al. Computational Advances in Bio and Medical Sciences. ICCABS 2021. Lecture Notes in Computer Science(), vol 13254. Springer, Cham. https://doi.org/10.1007/978-3-031-17531-2_11

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  • DOI: https://doi.org/10.1007/978-3-031-17531-2_11

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