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Assessing the Evolution of Gene Expression Using Microarray Data

Classical studies of the evolution of gene function have predominantly focused on mutations within protein coding regions. With the advent of microarrays, however, it has become possible to evaluate the transcriptional activity of a gene as an additional characteristic of function. Recent studies ha...

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Detalles Bibliográficos
Autores principales: Woody, Owen Z., Doxey, Andrew C., McConkey, Brendan J.
Formato: Texto
Lenguaje:English
Publicado: Libertas Academica 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2614203/
https://www.ncbi.nlm.nih.gov/pubmed/19204814
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author Woody, Owen Z.
Doxey, Andrew C.
McConkey, Brendan J.
author_facet Woody, Owen Z.
Doxey, Andrew C.
McConkey, Brendan J.
author_sort Woody, Owen Z.
collection PubMed
description Classical studies of the evolution of gene function have predominantly focused on mutations within protein coding regions. With the advent of microarrays, however, it has become possible to evaluate the transcriptional activity of a gene as an additional characteristic of function. Recent studies have revealed an equally important role for gene regulation in the retention and evolution of duplicate genes. Here we review approaches to assessing the evolution of gene expression using microarray data, and discuss potential influences on expression divergence. Currently, there are no established standards on how best to identify and quantify instances of expression divergence. There have also been few efforts to date that incorporate suspected influences into mathematical models of expression divergence. Such developments will be crucial to a comprehensive understanding of the role gene duplications and expression evolution play in the emergence of complex traits and functional diversity. An integrative approach to gene family evolution, including both orthologous and paralogous genes, has the potential to bring strong predictive power both to the functional annotation of extant proteins and to the inference of functional characteristics of ancestral gene family members.
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spelling pubmed-26142032009-02-09 Assessing the Evolution of Gene Expression Using Microarray Data Woody, Owen Z. Doxey, Andrew C. McConkey, Brendan J. Evol Bioinform Online Review Classical studies of the evolution of gene function have predominantly focused on mutations within protein coding regions. With the advent of microarrays, however, it has become possible to evaluate the transcriptional activity of a gene as an additional characteristic of function. Recent studies have revealed an equally important role for gene regulation in the retention and evolution of duplicate genes. Here we review approaches to assessing the evolution of gene expression using microarray data, and discuss potential influences on expression divergence. Currently, there are no established standards on how best to identify and quantify instances of expression divergence. There have also been few efforts to date that incorporate suspected influences into mathematical models of expression divergence. Such developments will be crucial to a comprehensive understanding of the role gene duplications and expression evolution play in the emergence of complex traits and functional diversity. An integrative approach to gene family evolution, including both orthologous and paralogous genes, has the potential to bring strong predictive power both to the functional annotation of extant proteins and to the inference of functional characteristics of ancestral gene family members. Libertas Academica 2008-04-24 /pmc/articles/PMC2614203/ /pubmed/19204814 Text en Copyright © 2008 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. (http://creativecommons.org/licenses/by/3.0)
spellingShingle Review
Woody, Owen Z.
Doxey, Andrew C.
McConkey, Brendan J.
Assessing the Evolution of Gene Expression Using Microarray Data
title Assessing the Evolution of Gene Expression Using Microarray Data
title_full Assessing the Evolution of Gene Expression Using Microarray Data
title_fullStr Assessing the Evolution of Gene Expression Using Microarray Data
title_full_unstemmed Assessing the Evolution of Gene Expression Using Microarray Data
title_short Assessing the Evolution of Gene Expression Using Microarray Data
title_sort assessing the evolution of gene expression using microarray data
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2614203/
https://www.ncbi.nlm.nih.gov/pubmed/19204814
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