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BioMed Research International
Volume 2014 (2014), Article ID 969768, 8 pages
Research Article

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

1Department of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, China
2Health Ministry Key Lab of Cell Transplantation, Harbin 150001, China
3Heilongjiang Institute of Hematology and Oncology, Harbin 150001, China
4College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
5College of Life Science, Heilongjiang University, Harbin 150080, China

Received 16 February 2014; Revised 5 April 2014; Accepted 6 April 2014; Published 19 May 2014

Academic Editor: Leng Han

Copyright © 2014 Hui Zhao et al. 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.


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.