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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doaj-6c3c04d71c864cd0830f7457d5af49952020-11-24T23:25:46ZengHindawi LimitedBioMed Research International2314-61332314-61412014-01-01201410.1155/2014/969768969768Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data SeriesHui Zhao0Fenglin Cao1Yonghui Gong2Huafeng Xu3Yiping Fei4Longyue Wu5Xiangmei Ye6Dongguang Yang7Xiuhua Liu8Xia Li9Jin Zhou10Department of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, ChinaDepartment of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, ChinaCollege of Life Science, Heilongjiang University, Harbin 150080, ChinaDepartment of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, ChinaDepartment of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, ChinaDepartment of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, ChinaDepartment of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, ChinaDepartment of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, ChinaDepartment of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, ChinaRNA-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.http://dx.doi.org/10.1155/2014/969768 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hui Zhao Fenglin Cao Yonghui Gong Huafeng Xu Yiping Fei Longyue Wu Xiangmei Ye Dongguang Yang Xiuhua Liu Xia Li Jin Zhou |
spellingShingle |
Hui Zhao Fenglin Cao Yonghui Gong Huafeng Xu Yiping Fei Longyue Wu Xiangmei Ye Dongguang Yang Xiuhua Liu Xia Li Jin Zhou Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series BioMed Research International |
author_facet |
Hui Zhao Fenglin Cao Yonghui Gong Huafeng Xu Yiping Fei Longyue Wu Xiangmei Ye Dongguang Yang Xiuhua Liu Xia Li Jin Zhou |
author_sort |
Hui Zhao |
title |
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_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_sort |
stratification of gene coexpression patterns and go function mining for a rna-seq data series |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2014-01-01 |
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. |
url |
http://dx.doi.org/10.1155/2014/969768 |
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