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Uncovering Signals from the Coronavirus Genome

A signal analysis of the complete genome sequenced for coronavirus variants of concern—B.1.1.7 (Alpha), B.1.135 (Beta) and P1 (Gamma)—and coronavirus variants of interest—B.1.429–B.1.427 (Epsilon) and B.1.525 (Eta)—is presented using open GISAID data. We deal with a certain new type of finite altern...

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Detalles Bibliográficos
Autor principal: Canessa, Enrique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303286/
https://www.ncbi.nlm.nih.gov/pubmed/34202172
http://dx.doi.org/10.3390/genes12070973
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author Canessa, Enrique
author_facet Canessa, Enrique
author_sort Canessa, Enrique
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description A signal analysis of the complete genome sequenced for coronavirus variants of concern—B.1.1.7 (Alpha), B.1.135 (Beta) and P1 (Gamma)—and coronavirus variants of interest—B.1.429–B.1.427 (Epsilon) and B.1.525 (Eta)—is presented using open GISAID data. We deal with a certain new type of finite alternating sum series having independently distributed terms associated with binary [Formula: see text] indicators for the nucleotide bases. Our method provides additional information to conventional similarity comparisons via alignment methods and Fourier Power Spectrum approaches. It leads to uncover distinctive patterns regarding the intrinsic data organization of complete genomics sequences according to its progression along the nucleotide bases position. The present new method could be useful for the bioinformatics surveillance and dynamics of coronavirus genome variants.
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spelling pubmed-83032862021-07-25 Uncovering Signals from the Coronavirus Genome Canessa, Enrique Genes (Basel) Article A signal analysis of the complete genome sequenced for coronavirus variants of concern—B.1.1.7 (Alpha), B.1.135 (Beta) and P1 (Gamma)—and coronavirus variants of interest—B.1.429–B.1.427 (Epsilon) and B.1.525 (Eta)—is presented using open GISAID data. We deal with a certain new type of finite alternating sum series having independently distributed terms associated with binary [Formula: see text] indicators for the nucleotide bases. Our method provides additional information to conventional similarity comparisons via alignment methods and Fourier Power Spectrum approaches. It leads to uncover distinctive patterns regarding the intrinsic data organization of complete genomics sequences according to its progression along the nucleotide bases position. The present new method could be useful for the bioinformatics surveillance and dynamics of coronavirus genome variants. MDPI 2021-06-25 /pmc/articles/PMC8303286/ /pubmed/34202172 http://dx.doi.org/10.3390/genes12070973 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Canessa, Enrique
Uncovering Signals from the Coronavirus Genome
title Uncovering Signals from the Coronavirus Genome
title_full Uncovering Signals from the Coronavirus Genome
title_fullStr Uncovering Signals from the Coronavirus Genome
title_full_unstemmed Uncovering Signals from the Coronavirus Genome
title_short Uncovering Signals from the Coronavirus Genome
title_sort uncovering signals from the coronavirus genome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303286/
https://www.ncbi.nlm.nih.gov/pubmed/34202172
http://dx.doi.org/10.3390/genes12070973
work_keys_str_mv AT canessaenrique uncoveringsignalsfromthecoronavirusgenome