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Gathering Computational Genomics and Proteomics to Unravel Adaptive Evolution

A recent editorial in PLoS Biology by MacCallum and Hill (2006) pointed out the inappropriateness of studies evaluating signatures of positive selection based solely in single-site analyses. Therefore the rising number of articles claiming positive selection that have been recently published urges t...

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Detalles Bibliográficos
Autores principales: Antunes, Agostinho, Ramos, Maria João
Formato: Texto
Lenguaje:English
Publicado: Libertas Academica 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2684141/
https://www.ncbi.nlm.nih.gov/pubmed/19461985
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author Antunes, Agostinho
Ramos, Maria João
author_facet Antunes, Agostinho
Ramos, Maria João
author_sort Antunes, Agostinho
collection PubMed
description A recent editorial in PLoS Biology by MacCallum and Hill (2006) pointed out the inappropriateness of studies evaluating signatures of positive selection based solely in single-site analyses. Therefore the rising number of articles claiming positive selection that have been recently published urges the question of how to improve the bioinformatics standards for reliably unravel positive selection? Deeper integrative efforts using state-of-the-art methodologies at the gene-level and protein-level are improving positive selection studies. Here we provide some computational guidelines to thoroughly document molecular adaptation.
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spelling pubmed-26841412009-05-19 Gathering Computational Genomics and Proteomics to Unravel Adaptive Evolution Antunes, Agostinho Ramos, Maria João Evol Bioinform Online Perspective A recent editorial in PLoS Biology by MacCallum and Hill (2006) pointed out the inappropriateness of studies evaluating signatures of positive selection based solely in single-site analyses. Therefore the rising number of articles claiming positive selection that have been recently published urges the question of how to improve the bioinformatics standards for reliably unravel positive selection? Deeper integrative efforts using state-of-the-art methodologies at the gene-level and protein-level are improving positive selection studies. Here we provide some computational guidelines to thoroughly document molecular adaptation. Libertas Academica 2007-09-06 /pmc/articles/PMC2684141/ /pubmed/19461985 Text en Copyright © 2007 The authors. http://creativecommons.org/licenses/by/3.0 This article is published under the Creative Commons Attribution By licence. For further information go to: http://creativecommons.org/licenses/by/3.0.
spellingShingle Perspective
Antunes, Agostinho
Ramos, Maria João
Gathering Computational Genomics and Proteomics to Unravel Adaptive Evolution
title Gathering Computational Genomics and Proteomics to Unravel Adaptive Evolution
title_full Gathering Computational Genomics and Proteomics to Unravel Adaptive Evolution
title_fullStr Gathering Computational Genomics and Proteomics to Unravel Adaptive Evolution
title_full_unstemmed Gathering Computational Genomics and Proteomics to Unravel Adaptive Evolution
title_short Gathering Computational Genomics and Proteomics to Unravel Adaptive Evolution
title_sort gathering computational genomics and proteomics to unravel adaptive evolution
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2684141/
https://www.ncbi.nlm.nih.gov/pubmed/19461985
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