Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1782320243276775424 |
---|---|
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. |
format | Online Article Text |
id | pubmed-4052503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhaohui stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT caofenglin stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT gongyonghui stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT xuhuafeng stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT feiyiping stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT wulongyue stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT yexiangmei stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT yangdongguang stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT liuxiuhua stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT lixia stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries AT zhoujin stratificationofgenecoexpressionpatternsandgofunctionminingforarnaseqdataseries |