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A Computational Framework for Evaluating the Efficiency of Arabidopsis Accessions in Response to Nitrogen Stress Reveals Important Metabolic Mechanisms
High-throughput phenotyping technologies in combination with genetic variability for the plant model species Arabidopsis thaliana (Arabidopsis) offer an excellent experimental platform to reveal the effects of different gene combinations on phenotypes. These developments have been coupled with compu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Research Foundation
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3457008/ https://www.ncbi.nlm.nih.gov/pubmed/23056002 http://dx.doi.org/10.3389/fpls.2012.00217 |
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author | Kleessen, Sabrina Fernie, Alisdair R. Nikoloski, Zoran |
author_facet | Kleessen, Sabrina Fernie, Alisdair R. Nikoloski, Zoran |
author_sort | Kleessen, Sabrina |
collection | PubMed |
description | High-throughput phenotyping technologies in combination with genetic variability for the plant model species Arabidopsis thaliana (Arabidopsis) offer an excellent experimental platform to reveal the effects of different gene combinations on phenotypes. These developments have been coupled with computational approaches to extract information not only from the multidimensional data, capturing various levels of biochemical organization, but also from various morphological and growth-related traits. Nevertheless, the existing methods usually focus on data aggregation which may neglect accession-specific effects. Here we argue that revealing the molecular mechanisms governing a desired set of output traits can be performed by ranking of accessions based on their efficiencies relative to all other analyzed accessions. To this end, we propose a framework for evaluating accessions via their relative efficiencies which establish a relationship between multidimensional system’s inputs and outputs from different environmental conditions. The framework combines data envelopment analysis (DEA) with a novel valency index characterizing the difference in congruence between the efficiency rankings of accessions under various conditions. We illustrate the advantages of the proposed approach for analyzing genetic variability on a publicly available data set comprising quantitative data on metabolic and morphological traits for 23 Arabidopsis accessions under three conditions of nitrogen availability. In addition, we extend the proposed framework to identify the set of traits displaying the highest influence on ranking based on the relative efficiencies of the considered accessions. As an outlook, we discuss how the proposed framework can be combined with well-established statistical techniques to further dissect the relationship between natural variability and metabolism. |
format | Online Article Text |
id | pubmed-3457008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Frontiers Research Foundation |
record_format | MEDLINE/PubMed |
spelling | pubmed-34570082012-10-09 A Computational Framework for Evaluating the Efficiency of Arabidopsis Accessions in Response to Nitrogen Stress Reveals Important Metabolic Mechanisms Kleessen, Sabrina Fernie, Alisdair R. Nikoloski, Zoran Front Plant Sci Plant Science High-throughput phenotyping technologies in combination with genetic variability for the plant model species Arabidopsis thaliana (Arabidopsis) offer an excellent experimental platform to reveal the effects of different gene combinations on phenotypes. These developments have been coupled with computational approaches to extract information not only from the multidimensional data, capturing various levels of biochemical organization, but also from various morphological and growth-related traits. Nevertheless, the existing methods usually focus on data aggregation which may neglect accession-specific effects. Here we argue that revealing the molecular mechanisms governing a desired set of output traits can be performed by ranking of accessions based on their efficiencies relative to all other analyzed accessions. To this end, we propose a framework for evaluating accessions via their relative efficiencies which establish a relationship between multidimensional system’s inputs and outputs from different environmental conditions. The framework combines data envelopment analysis (DEA) with a novel valency index characterizing the difference in congruence between the efficiency rankings of accessions under various conditions. We illustrate the advantages of the proposed approach for analyzing genetic variability on a publicly available data set comprising quantitative data on metabolic and morphological traits for 23 Arabidopsis accessions under three conditions of nitrogen availability. In addition, we extend the proposed framework to identify the set of traits displaying the highest influence on ranking based on the relative efficiencies of the considered accessions. As an outlook, we discuss how the proposed framework can be combined with well-established statistical techniques to further dissect the relationship between natural variability and metabolism. Frontiers Research Foundation 2012-09-25 /pmc/articles/PMC3457008/ /pubmed/23056002 http://dx.doi.org/10.3389/fpls.2012.00217 Text en Copyright © 2012 Kleessen, Fernie and Nikoloski. http://www.frontiersin.org/licenseagreement This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc. |
spellingShingle | Plant Science Kleessen, Sabrina Fernie, Alisdair R. Nikoloski, Zoran A Computational Framework for Evaluating the Efficiency of Arabidopsis Accessions in Response to Nitrogen Stress Reveals Important Metabolic Mechanisms |
title | A Computational Framework for Evaluating the Efficiency of Arabidopsis Accessions in Response to Nitrogen Stress Reveals Important Metabolic Mechanisms |
title_full | A Computational Framework for Evaluating the Efficiency of Arabidopsis Accessions in Response to Nitrogen Stress Reveals Important Metabolic Mechanisms |
title_fullStr | A Computational Framework for Evaluating the Efficiency of Arabidopsis Accessions in Response to Nitrogen Stress Reveals Important Metabolic Mechanisms |
title_full_unstemmed | A Computational Framework for Evaluating the Efficiency of Arabidopsis Accessions in Response to Nitrogen Stress Reveals Important Metabolic Mechanisms |
title_short | A Computational Framework for Evaluating the Efficiency of Arabidopsis Accessions in Response to Nitrogen Stress Reveals Important Metabolic Mechanisms |
title_sort | computational framework for evaluating the efficiency of arabidopsis accessions in response to nitrogen stress reveals important metabolic mechanisms |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3457008/ https://www.ncbi.nlm.nih.gov/pubmed/23056002 http://dx.doi.org/10.3389/fpls.2012.00217 |
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