Dataset Information

Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

ABSTRACT: BACKGROUND:High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. RESULTS:We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. CONCLUSIONS:We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments.

SUBMITTER: Zhu M

PROVIDER: S-EPMC3854569 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

Zhu Mingzhu M Dahmen Jeremy L JL Stacey Gary G Cheng Jianlin J

BMC bioinformatics 20130922

<h4>Background</h4>High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of gene ...[more]

PMID: 24053776

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Project description:Cytoplasmic male sterility (CMS) is an important plant characteristic for exploiting heterosis to enhance crop traits during breeding. However, the CMS regulatory network remains unclear in plants, even though researchers have attempted to isolate genes associated with CMS. In this study, we performed high-throughput sequencing and degradome analyses to identify microRNAs (miRNAs) and their targets in a soybean CMS line (JLCMS9A) and its maintainer line (JLCMS9B). Additionally, the differentially expressed genes during reproductive development were identified using RNA-seq data. A total of 280 miRNAs matched soybean miRNA sequences in miRBase, including mature miRNAs and pre-miRNAs. Of the 280 miRNAs, 30, 23, and 21 belonged to the miR166, miR156, and miR171 families, respectively. Moreover, 410 novel low-abundant miRNAs were identified in the JLCMS9A and JLCMS9B flower buds. Furthermore, 303 and 462 target genes unique to JLCMS9A and JLCMS9B, respectively, as well as 782 common targets were predicted based on the degradome analysis. Target genes differentially expressed between the CMS line and the maintainer line were revealed by an RNA-seq analysis. Moreover, all target genes were annotated with diverse functions related to biological processes, cellular components, and molecular functions, including transcriptional regulation, the nucleus, meristem maintenance, meristem initiation, cell differentiation, auxin-activated signaling, plant ovule development, and anther development. Finally, a network was built based on the interactions. Analyses of the miRNA, degradome, and transcriptome datasets generated in this study provided a comprehensive overview of the reproductive development of a CMS soybean line. The data presented herein represent useful information for soybean hybrid breeding. Furthermore, the study results indicate that miRNAs might contribute to the soybean CMS regulatory network by modulating the expression of CMS-related genes. These findings lay the foundation for future studies on the molecular mechanisms underlying soybean CMS.

Dataset Information

Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

Publications

Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

Similar Datasets

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