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Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis

BACKGROUND: Identification of the novel genes relevant to plant cell-wall (PCW) synthesis represents a highly important and challenging problem. Although substantial efforts have been invested into studying this problem, the vast majority of the PCW related genes remain unknown. RESULTS: Here we pre...

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Detalles Bibliográficos
Autores principales: Wang, Shan, Yin, Yanbin, Ma, Qin, Tang, Xiaojia, Hao, Dongyun, Xu, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463447/
https://www.ncbi.nlm.nih.gov/pubmed/22877077
http://dx.doi.org/10.1186/1471-2229-12-138
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author Wang, Shan
Yin, Yanbin
Ma, Qin
Tang, Xiaojia
Hao, Dongyun
Xu, Ying
author_facet Wang, Shan
Yin, Yanbin
Ma, Qin
Tang, Xiaojia
Hao, Dongyun
Xu, Ying
author_sort Wang, Shan
collection PubMed
description BACKGROUND: Identification of the novel genes relevant to plant cell-wall (PCW) synthesis represents a highly important and challenging problem. Although substantial efforts have been invested into studying this problem, the vast majority of the PCW related genes remain unknown. RESULTS: Here we present a computational study focused on identification of the novel PCW genes in Arabidopsis based on the co-expression analyses of transcriptomic data collected under 351 conditions, using a bi-clustering technique. Our analysis identified 217 highly co-expressed gene clusters (modules) under some experimental conditions, each containing at least one gene annotated as PCW related according to the Purdue Cell Wall Gene Families database. These co-expression modules cover 349 known/annotated PCW genes and 2,438 new candidates. For each candidate gene, we annotated the specific PCW synthesis stages in which it is involved and predicted the detailed function. In addition, for the co-expressed genes in each module, we predicted and analyzed their cis regulatory motifs in the promoters using our motif discovery pipeline, providing strong evidence that the genes in each co-expression module are transcriptionally co-regulated. From the all co-expression modules, we infer that 108 modules are related to four major PCW synthesis components, using three complementary methods. CONCLUSIONS: We believe our approach and data presented here will be useful for further identification and characterization of PCW genes. All the predicted PCW genes, co-expression modules, motifs and their annotations are available at a web-based database: http://csbl.bmb.uga.edu/publications/materials/shanwang/CWRPdb/index.html.
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spelling pubmed-34634472012-10-04 Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis Wang, Shan Yin, Yanbin Ma, Qin Tang, Xiaojia Hao, Dongyun Xu, Ying BMC Plant Biol Research Article BACKGROUND: Identification of the novel genes relevant to plant cell-wall (PCW) synthesis represents a highly important and challenging problem. Although substantial efforts have been invested into studying this problem, the vast majority of the PCW related genes remain unknown. RESULTS: Here we present a computational study focused on identification of the novel PCW genes in Arabidopsis based on the co-expression analyses of transcriptomic data collected under 351 conditions, using a bi-clustering technique. Our analysis identified 217 highly co-expressed gene clusters (modules) under some experimental conditions, each containing at least one gene annotated as PCW related according to the Purdue Cell Wall Gene Families database. These co-expression modules cover 349 known/annotated PCW genes and 2,438 new candidates. For each candidate gene, we annotated the specific PCW synthesis stages in which it is involved and predicted the detailed function. In addition, for the co-expressed genes in each module, we predicted and analyzed their cis regulatory motifs in the promoters using our motif discovery pipeline, providing strong evidence that the genes in each co-expression module are transcriptionally co-regulated. From the all co-expression modules, we infer that 108 modules are related to four major PCW synthesis components, using three complementary methods. CONCLUSIONS: We believe our approach and data presented here will be useful for further identification and characterization of PCW genes. All the predicted PCW genes, co-expression modules, motifs and their annotations are available at a web-based database: http://csbl.bmb.uga.edu/publications/materials/shanwang/CWRPdb/index.html. BioMed Central 2012-08-09 /pmc/articles/PMC3463447/ /pubmed/22877077 http://dx.doi.org/10.1186/1471-2229-12-138 Text en Copyright ©2012 Wang et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Shan
Yin, Yanbin
Ma, Qin
Tang, Xiaojia
Hao, Dongyun
Xu, Ying
Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis
title Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis
title_full Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis
title_fullStr Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis
title_full_unstemmed Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis
title_short Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis
title_sort genome-scale identification of cell-wall related genes in arabidopsis based on co-expression network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463447/
https://www.ncbi.nlm.nih.gov/pubmed/22877077
http://dx.doi.org/10.1186/1471-2229-12-138
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