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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...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2007
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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. |
format | Text |
id | pubmed-2684141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | Libertas Academica |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT antunesagostinho gatheringcomputationalgenomicsandproteomicstounraveladaptiveevolution AT ramosmariajoao gatheringcomputationalgenomicsandproteomicstounraveladaptiveevolution |