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Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond
As both a computational and an experimental endeavor, ancestral sequence reconstruction remains a timely and important technique. Modern approaches to conduct ancestral sequence reconstruction for proteins are built upon a conceptual framework from journal founder Emile Zuckerkandl. On top of this,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828096/ https://www.ncbi.nlm.nih.gov/pubmed/33486547 http://dx.doi.org/10.1007/s00239-021-09993-1 |
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author | Selberg, Avery G. A. Gaucher, Eric A. Liberles, David A. |
author_facet | Selberg, Avery G. A. Gaucher, Eric A. Liberles, David A. |
author_sort | Selberg, Avery G. A. |
collection | PubMed |
description | As both a computational and an experimental endeavor, ancestral sequence reconstruction remains a timely and important technique. Modern approaches to conduct ancestral sequence reconstruction for proteins are built upon a conceptual framework from journal founder Emile Zuckerkandl. On top of this, work on maximum likelihood phylogenetics published in Journal of Molecular Evolution in 1996 was one of the first approaches for generating maximum likelihood ancestral sequences of proteins. From its computational history, future model development needs as well as potential applications in areas as diverse as computational systems biology, molecular community ecology, infectious disease therapeutics and other biomedical applications, and biotechnology are discussed. From its past in this journal, there is a bright future for ancestral sequence reconstruction in the field of evolutionary biology. |
format | Online Article Text |
id | pubmed-7828096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-78280962021-01-25 Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond Selberg, Avery G. A. Gaucher, Eric A. Liberles, David A. J Mol Evol Commentary As both a computational and an experimental endeavor, ancestral sequence reconstruction remains a timely and important technique. Modern approaches to conduct ancestral sequence reconstruction for proteins are built upon a conceptual framework from journal founder Emile Zuckerkandl. On top of this, work on maximum likelihood phylogenetics published in Journal of Molecular Evolution in 1996 was one of the first approaches for generating maximum likelihood ancestral sequences of proteins. From its computational history, future model development needs as well as potential applications in areas as diverse as computational systems biology, molecular community ecology, infectious disease therapeutics and other biomedical applications, and biotechnology are discussed. From its past in this journal, there is a bright future for ancestral sequence reconstruction in the field of evolutionary biology. Springer US 2021-01-24 2021 /pmc/articles/PMC7828096/ /pubmed/33486547 http://dx.doi.org/10.1007/s00239-021-09993-1 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Commentary Selberg, Avery G. A. Gaucher, Eric A. Liberles, David A. Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond |
title | Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond |
title_full | Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond |
title_fullStr | Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond |
title_full_unstemmed | Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond |
title_short | Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond |
title_sort | ancestral sequence reconstruction: from chemical paleogenetics to maximum likelihood algorithms and beyond |
topic | Commentary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828096/ https://www.ncbi.nlm.nih.gov/pubmed/33486547 http://dx.doi.org/10.1007/s00239-021-09993-1 |
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