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A proposal for a multivariate quantitative approach to infer karyological relationships among taxa
Abstract. Until now, basic karyological parameters have been used in different ways by researchers to infer karyological relationships among organisms. In the present study, we propose a standardized approach to this aim, integrating six different, not redundant, parameters in a multivariate PCoA an...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Pensoft Publishers
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4296720/ https://www.ncbi.nlm.nih.gov/pubmed/25610547 http://dx.doi.org/10.3897/CompCytogen.v8i4.8564 |
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author | Peruzzi, Lorenzo Altınordu, Fahim |
author_facet | Peruzzi, Lorenzo Altınordu, Fahim |
author_sort | Peruzzi, Lorenzo |
collection | PubMed |
description | Abstract. Until now, basic karyological parameters have been used in different ways by researchers to infer karyological relationships among organisms. In the present study, we propose a standardized approach to this aim, integrating six different, not redundant, parameters in a multivariate PCoA analysis. These parameters are chromosome number, basic chromosome number, total haploid chromosome length, M(CA) (Mean Centromeric Asymmetry), CV(CL) (Coefficient of Variation of Chromosome Length) and CV(CI) (Coefficient of Variation of Centromeric Index). The method is exemplified with the application to several plant taxa, and its significance and limits are discussed in the light of current phylogenetic knowledge of these groups. |
format | Online Article Text |
id | pubmed-4296720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Pensoft Publishers |
record_format | MEDLINE/PubMed |
spelling | pubmed-42967202015-01-21 A proposal for a multivariate quantitative approach to infer karyological relationships among taxa Peruzzi, Lorenzo Altınordu, Fahim Comp Cytogenet Research Articles Abstract. Until now, basic karyological parameters have been used in different ways by researchers to infer karyological relationships among organisms. In the present study, we propose a standardized approach to this aim, integrating six different, not redundant, parameters in a multivariate PCoA analysis. These parameters are chromosome number, basic chromosome number, total haploid chromosome length, M(CA) (Mean Centromeric Asymmetry), CV(CL) (Coefficient of Variation of Chromosome Length) and CV(CI) (Coefficient of Variation of Centromeric Index). The method is exemplified with the application to several plant taxa, and its significance and limits are discussed in the light of current phylogenetic knowledge of these groups. Pensoft Publishers 2014-12-10 /pmc/articles/PMC4296720/ /pubmed/25610547 http://dx.doi.org/10.3897/CompCytogen.v8i4.8564 Text en Lorenzo Peruzzi, Fahim Altınordu http://creativecommons.org/licenses/by/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Articles Peruzzi, Lorenzo Altınordu, Fahim A proposal for a multivariate quantitative approach to infer karyological relationships among taxa |
title | A proposal for a multivariate quantitative approach to infer karyological relationships among taxa |
title_full | A proposal for a multivariate quantitative approach to infer karyological relationships among taxa |
title_fullStr | A proposal for a multivariate quantitative approach to infer karyological relationships among taxa |
title_full_unstemmed | A proposal for a multivariate quantitative approach to infer karyological relationships among taxa |
title_short | A proposal for a multivariate quantitative approach to infer karyological relationships among taxa |
title_sort | proposal for a multivariate quantitative approach to infer karyological relationships among taxa |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4296720/ https://www.ncbi.nlm.nih.gov/pubmed/25610547 http://dx.doi.org/10.3897/CompCytogen.v8i4.8564 |
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