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Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series

RNA-Seq is emerging as an increasingly important tool in biological research, and it provides the most direct evidence of the relationship between the physiological state and molecular changes in cells. A large amount of RNA-Seq data across diverse experimental conditions have been generated and dep...

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Autores principales: Zhao, Hui, Cao, Fenglin, Gong, Yonghui, Xu, Huafeng, Fei, Yiping, Wu, Longyue, Ye, Xiangmei, Yang, Dongguang, Liu, Xiuhua, Li, Xia, Zhou, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4052503/
https://www.ncbi.nlm.nih.gov/pubmed/24955372
http://dx.doi.org/10.1155/2014/969768
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author Zhao, Hui
Cao, Fenglin
Gong, Yonghui
Xu, Huafeng
Fei, Yiping
Wu, Longyue
Ye, Xiangmei
Yang, Dongguang
Liu, Xiuhua
Li, Xia
Zhou, Jin
author_facet Zhao, Hui
Cao, Fenglin
Gong, Yonghui
Xu, Huafeng
Fei, Yiping
Wu, Longyue
Ye, Xiangmei
Yang, Dongguang
Liu, Xiuhua
Li, Xia
Zhou, Jin
author_sort Zhao, Hui
collection PubMed
description RNA-Seq is emerging as an increasingly important tool in biological research, and it provides the most direct evidence of the relationship between the physiological state and molecular changes in cells. A large amount of RNA-Seq data across diverse experimental conditions have been generated and deposited in public databases. However, most developed approaches for coexpression analyses focus on the coexpression pattern mining of the transcriptome, thereby ignoring the magnitude of gene differences in one pattern. Furthermore, the functional relationships of genes in one pattern, and notably among patterns, were not always recognized. In this study, we developed an integrated strategy to identify differential coexpression patterns of genes and probed the functional mechanisms of the modules. Two real datasets were used to validate the method and allow comparisons with other methods. One of the datasets was selected to illustrate the flow of a typical analysis. In summary, we present an approach to robustly detect coexpression patterns in transcriptomes and to stratify patterns according to their relative differences. Furthermore, a global relationship between patterns and biological functions was constructed. In addition, a freely accessible web toolkit “coexpression pattern mining and GO functional analysis” (COGO) was developed.
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spelling pubmed-40525032014-06-22 Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series Zhao, Hui Cao, Fenglin Gong, Yonghui Xu, Huafeng Fei, Yiping Wu, Longyue Ye, Xiangmei Yang, Dongguang Liu, Xiuhua Li, Xia Zhou, Jin Biomed Res Int Research Article RNA-Seq is emerging as an increasingly important tool in biological research, and it provides the most direct evidence of the relationship between the physiological state and molecular changes in cells. A large amount of RNA-Seq data across diverse experimental conditions have been generated and deposited in public databases. However, most developed approaches for coexpression analyses focus on the coexpression pattern mining of the transcriptome, thereby ignoring the magnitude of gene differences in one pattern. Furthermore, the functional relationships of genes in one pattern, and notably among patterns, were not always recognized. In this study, we developed an integrated strategy to identify differential coexpression patterns of genes and probed the functional mechanisms of the modules. Two real datasets were used to validate the method and allow comparisons with other methods. One of the datasets was selected to illustrate the flow of a typical analysis. In summary, we present an approach to robustly detect coexpression patterns in transcriptomes and to stratify patterns according to their relative differences. Furthermore, a global relationship between patterns and biological functions was constructed. In addition, a freely accessible web toolkit “coexpression pattern mining and GO functional analysis” (COGO) was developed. Hindawi Publishing Corporation 2014 2014-05-19 /pmc/articles/PMC4052503/ /pubmed/24955372 http://dx.doi.org/10.1155/2014/969768 Text en Copyright © 2014 Hui Zhao et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhao, Hui
Cao, Fenglin
Gong, Yonghui
Xu, Huafeng
Fei, Yiping
Wu, Longyue
Ye, Xiangmei
Yang, Dongguang
Liu, Xiuhua
Li, Xia
Zhou, Jin
Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series
title Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series
title_full Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series
title_fullStr Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series
title_full_unstemmed Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series
title_short Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series
title_sort stratification of gene coexpression patterns and go function mining for a rna-seq data series
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4052503/
https://www.ncbi.nlm.nih.gov/pubmed/24955372
http://dx.doi.org/10.1155/2014/969768
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