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Open Access Research article

Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

Mingzhu Zhu1, Xin Deng1, Trupti Joshi123, Dong Xu123, Gary Stacey34 and Jianlin Cheng123*

Author Affiliations

1 Department of Computer Science, University of Missouri, Columbia, MO 65211, U.S.A

2 Informatics Institute, University of Missouri, Columbia, MO 65211, U.S.A

3 C.S. Bond Life Science Center, University of Missouri, Columbia, MO 65211, U.S.A

4 Divisions of Plant Sciences and Biochemistry, University of Missouri, Columbia, MO 65211, U.S.A

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BMC Genomics 2012, 13:437  doi:10.1186/1471-2164-13-437

Published: 31 August 2012

Abstract

Background

Current experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions. In this way, the cell transcriptional machinery can respond optimally to internal or external stimuli. This provides a research opportunity to identify and study co-expressed gene modules whose transcription is controlled by shared gene regulatory networks.

Results

We developed and integrated a set of computational methods of differential gene expression analysis, gene clustering, gene network inference, gene function prediction, and DNA motif identification to automatically identify differentially co-expressed gene modules, reconstruct their regulatory networks, and validate their correctness. We tested the methods using microarray data derived from soybean cells grown under various stress conditions. Our methods were able to identify 42 coherent gene modules within which average gene expression correlation coefficients are greater than 0.8 and reconstruct their putative regulatory networks. A total of 32 modules and their regulatory networks were further validated by the coherence of predicted gene functions and the consistency of putative transcription factor binding motifs. Approximately half of the 32 modules were partially supported by the literature, which demonstrates that the bioinformatic methods used can help elucidate the molecular responses of soybean cells upon various environmental stresses.

Conclusions

The bioinformatics methods and genome-wide data sources for gene expression, clustering, regulation, and function analysis were integrated seamlessly into one modular protocol to systematically analyze and infer modules and networks from only differential expression genes in soybean cells grown under stress conditions. Our approach appears to effectively reduce the complexity of the problem, and is sufficiently robust and accurate to generate a rather complete and detailed view of putative soybean gene transcription logic potentially underlying the responses to the various environmental challenges. The same automated method can also be applied to reconstruct differentially co-expressed gene modules and their regulatory networks from gene expression data of any other transcriptome.

Keywords:
Gene co-expression module; Gene regulatory network; Transcription factor; Microarray; Soybean