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Global Virology III: Virology in the 21st Century pp 485–513Cite as

Futuristic Methods in Virus Genome Evolution Using the Third-Generation DNA Sequencing and Artificial Neural Networks

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Abstract

The Third-Generation in DNA sequencing has emerged in the last few years, using new technologies that allow the production of long-read sequences. Applications of Third-Generation sequencing enable real-time data production, changing the research paradigms in environmental, and facilitating medical sampling in virology. To take full advantage of the large-scale data generated from long-read sequencing, an innovation in downstream data analysis is necessary. Here, we discuss futuristic methods using machine learning approaches to analyze big genetic data. We discuss the future of twenty-first-century virology by presenting advanced approaches for virus studies using real-time data production and on-site data analysis with Third-Generation Sequencing and machine learning methods. We first introduce the basic concepts in conventional statistical models and methods in virology, building gradually into the necessity of innovating downstream data analysis to meet the advances in sequencing technologies. We argue that artificial neural networks can innovate downstream data analysis, as they can learn from big datasets without model assumptions nor feature specifications, as opposed to current data analysis in bioinformatics. Furthermore, we discuss how futuristic methods using artificial neural networks, combined with long-read sequences can revolutionize virus studies, using specific examples in supervised and unsupervised settings.

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Notes

  1. 1.

    Ebola virus disease. (2019.02.01). Retrieved from https://www.who.int/news-room/fact-sheets/detail/ebola-virus-disease

  2. 2.

    Zika virus. (2019.02.01). Retrieved from https://www.who.int/news-room/fact-sheets/detail/zika-virus

  3. 3.

    Influenza Research Database. (2019.01.30). Retrieved from https://www.fludb.org/brc/dataSummary.spg?decorator=influenza

  4. 4.

    HiSeq X™ Series of Sequencing Systems. (2016.02.10). Retrieved from http://www.illumina.com/content/dam/illumina-marketing/documents/products/datasheets/datasheet-hiseq-x-ten.pdf

  5. 5.

    BioRxiv: the preprint server for biology. (2019.02.07). Retrieved from https://www.biorxiv.org/

  6. 6.

    Movidius (2019.02.13). Retrieved from https://www.movidius.com/

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Acknowledgements

We thank Sunil Kumar Dogga, Ana K. Pitol, Hyun Jeong Shim for helpful discussions.

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Authors and Affiliations

  1. Department of Earth and Planetary Sciences, University of California, Berkeley, Berkeley, CA, USA

    Hyunjin Shim

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  1. Hyunjin Shim
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Editors and Affiliations

  1. Department of Internal Medicine, University of South Florida, Tampa, FL, USA

    Paul Shapshak

  2. Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

    Seetharaman Balaji

  3. Biomedical Informatics 17, Irulan Sandy Annex, Pondicherry, Pondicherry, India

    Pandjassarame Kangueane

  4. Oral Biology and Medicine, CHS 63-090, UCLA School of Dentistry Oral Biology and Medicine, CHS 63-090, Los Angeles, CA, USA

    Francesco Chiappelli

  5. Department of Internal Medicine, University of South Florida, Tampa, FL, USA

    Charurut Somboonwit

  6. Department of Internal Medicine, University of South Florida, Tampa, FL, USA

    Lynette J. Menezes

  7. Department of Internal Medicine, University of South Florida, Tampa, FL, USA

    John T. Sinnott

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Shim, H. (2019). Futuristic Methods in Virus Genome Evolution Using the Third-Generation DNA Sequencing and Artificial Neural Networks. In: Shapshak, P., et al. Global Virology III: Virology in the 21st Century. Springer, Cham. https://doi.org/10.1007/978-3-030-29022-1_17

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