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GenomeBits insight into omicron and delta variants of coronavirus pathogen

We apply the new GenomeBits method to uncover underlying genomic features of omicron and delta coronavirus variants. This is a statistical algorithm whose salient feature is to map the nucleotide bases into a finite alternating (±) sum series of distributed terms of binary (0,1) indicators. We show...

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Detalles Bibliográficos
Autores principales: Canessa, Enrique, Tenze, Livio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273097/
https://www.ncbi.nlm.nih.gov/pubmed/35816483
http://dx.doi.org/10.1371/journal.pone.0271039
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author Canessa, Enrique
Tenze, Livio
author_facet Canessa, Enrique
Tenze, Livio
author_sort Canessa, Enrique
collection PubMed
description We apply the new GenomeBits method to uncover underlying genomic features of omicron and delta coronavirus variants. This is a statistical algorithm whose salient feature is to map the nucleotide bases into a finite alternating (±) sum series of distributed terms of binary (0,1) indicators. We show how by this method, distinctive signals can be uncovered out of the intrinsic data organization of amino acid progressions along their base positions. Results reveal a sort of ‘ordered’ (or constant) to ‘disordered’ (or peaked) transition around the coronavirus S-spike protein region. Together with our previous results for past variants of coronavirus: Alpha, Beta, Gamma, Epsilon and Eta, we conclude that the mapping into GenomeBits strands of omicron and delta variants can help to characterize mutant pathogens.
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spelling pubmed-92730972022-07-12 GenomeBits insight into omicron and delta variants of coronavirus pathogen Canessa, Enrique Tenze, Livio PLoS One Research Article We apply the new GenomeBits method to uncover underlying genomic features of omicron and delta coronavirus variants. This is a statistical algorithm whose salient feature is to map the nucleotide bases into a finite alternating (±) sum series of distributed terms of binary (0,1) indicators. We show how by this method, distinctive signals can be uncovered out of the intrinsic data organization of amino acid progressions along their base positions. Results reveal a sort of ‘ordered’ (or constant) to ‘disordered’ (or peaked) transition around the coronavirus S-spike protein region. Together with our previous results for past variants of coronavirus: Alpha, Beta, Gamma, Epsilon and Eta, we conclude that the mapping into GenomeBits strands of omicron and delta variants can help to characterize mutant pathogens. Public Library of Science 2022-07-11 /pmc/articles/PMC9273097/ /pubmed/35816483 http://dx.doi.org/10.1371/journal.pone.0271039 Text en © 2022 Canessa, Tenze https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Canessa, Enrique
Tenze, Livio
GenomeBits insight into omicron and delta variants of coronavirus pathogen
title GenomeBits insight into omicron and delta variants of coronavirus pathogen
title_full GenomeBits insight into omicron and delta variants of coronavirus pathogen
title_fullStr GenomeBits insight into omicron and delta variants of coronavirus pathogen
title_full_unstemmed GenomeBits insight into omicron and delta variants of coronavirus pathogen
title_short GenomeBits insight into omicron and delta variants of coronavirus pathogen
title_sort genomebits insight into omicron and delta variants of coronavirus pathogen
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273097/
https://www.ncbi.nlm.nih.gov/pubmed/35816483
http://dx.doi.org/10.1371/journal.pone.0271039
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