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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-9273097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT canessaenrique genomebitsinsightintoomicronanddeltavariantsofcoronaviruspathogen AT tenzelivio genomebitsinsightintoomicronanddeltavariantsofcoronaviruspathogen |