ID | 116298 |
タイトル別表記 | SARS-CoV-2 genome clusters analyzed by Deep Learning
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著者 |
Miyake, Jun
Osaka University
Sato, Takaaki
Osaka University
Baba, Shunsuke
Osaka University
Nakamura, Hayao
Osaka University
Niioka, Hirohiko
Osaka University
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キーワード | Autoencoder
Deep Learning
SARS-CoV-2
Genome
Mutation
Classification
Cluster
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資料タイプ |
プレプリント
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抄録 | We report on a method for analyzing the variant of coronavirus genes using autoencoder. Since coronaviruses have mutated rapidly and generated a large number of genotypes, an appropriate method for understanding the entire population is required. The method using autoencoder meets this requirement and is suitable for understanding how and when the variants emarge and disappear. For the over 30,000 SARS-CoV-2 ORF1ab gene sequences sampled globally from December 2019 to February 2021, we were able to represent a summary of their characteristics in a 3D plot and show the expansion, decline, and transformation of the virus types over time and by region. Based on ORF1ab genes, the SARS-CoV-2 viruses were classified into five major types (A, B, C, D, and E in the order of appearance): the virus type that originated in China at the end of 2019 (type A) practically disappeared in June 2020; two virus types (types B and C) have emerged in the United States and Europe since February 2020, and type B has become a global phenomenon. Type C is only prevalent in the U.S. and is suspected to be associated with high mortality, but this type also disappeared at the end of June. Type D is only found in Australia. Currently, the epidemic is dominated by types B and E.
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発行日 | 2021-03-16
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備考 | This article is a preprint and has not been certified by peer review.
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権利情報 | The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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言語 |
eng
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部局 |
生物資源系
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