Abstract
BackgroundCurrent 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.ResultsWe 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.ConclusionsThe 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.
Highlights
Current experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions
Data The input data required by our approach includes the soybean genome sequence and gene annotations, a list of the candidate transcription factors (TF) curated in SoyDB [13], and the gene expression profiles calculated from the microarray data of soybean cells from a number of stress-induced experiments [15]
The set of transcription factors are assumed to regulate the expression of the genes in a module through a path in the binary decision tree composed of the TFs as internal nodes and condition sub-groups as leaf nodes (Figure 1)
Summary
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. 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. Upon internal or external cellular stimuli, transcription factors of a module may be activated to either up- or down-regulate the target genes in order to respond to the stimuli. Accurate prediction of transcription regulatory modules can generate valuable testable hypotheses for designing biological experiments to identify genes and interactions important for biological phenotypes and to elucidate cellular mechanisms underlying various biological conditions and environmental stresses
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