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Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes

With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene...

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Autores principales: Childs, Kevin L., Davidson, Rebecca M., Buell, C. Robin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3142134/
https://www.ncbi.nlm.nih.gov/pubmed/21799793
http://dx.doi.org/10.1371/journal.pone.0022196
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author Childs, Kevin L.
Davidson, Rebecca M.
Buell, C. Robin
author_facet Childs, Kevin L.
Davidson, Rebecca M.
Buell, C. Robin
author_sort Childs, Kevin L.
collection PubMed
description With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa) gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional annotation of those modules. Additionally, the expression patterns of genes across the treatments/conditions of an expression experiment comprise a second form of useful annotation.
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spelling pubmed-31421342011-07-28 Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes Childs, Kevin L. Davidson, Rebecca M. Buell, C. Robin PLoS One Research Article With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa) gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional annotation of those modules. Additionally, the expression patterns of genes across the treatments/conditions of an expression experiment comprise a second form of useful annotation. Public Library of Science 2011-07-22 /pmc/articles/PMC3142134/ /pubmed/21799793 http://dx.doi.org/10.1371/journal.pone.0022196 Text en Childs et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Childs, Kevin L.
Davidson, Rebecca M.
Buell, C. Robin
Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes
title Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes
title_full Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes
title_fullStr Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes
title_full_unstemmed Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes
title_short Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes
title_sort gene coexpression network analysis as a source of functional annotation for rice genes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3142134/
https://www.ncbi.nlm.nih.gov/pubmed/21799793
http://dx.doi.org/10.1371/journal.pone.0022196
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