Jiao, B. | bioRxiv Science

bioRxiv2017

Systematic Analysis Of RNA-Seq-Based Gene Co-Expression Across Multiple Plants

The complex cellular network was formed by the interacting gene modules. Building the high-quality RNA-seq-based Gene Co-expression Network (GCN) is critical for uncovering these modules and understanding the phenotypes of an organism. Here, we established and analyzed the RNA-seq-based GCNs in two monocot species rice and maize, and two eudicot species Arabidopsis and soybean, and subdivided them into co-expressed modules. Taking rice as an example, we associated these modules with biological functions and agronomic traits by enrichment analysis, and discovered a large number of conditin-specific or tissue-specific modules. In addition, we also explored the regulatory mechanism of the modules by enrichment of the known cis-elements, transcription factors and miRNA targets. Their coherent enrichment with the inferred functions of the modules revealed their synergistic effect on the gene expression regulation. Moreover, the comparative analysis of gene co-expression was performed to identify conserved and species-specific functional modules across 4 plant species. We discovered that the modules shared across 4 plants participate in the basic biological processes, whereas the species-specific modules were involved in the spatiotemporal-specific processes linking the genotypes to phenotypes. Our research provides the massive modules relating to the cellular activities and agronomic traits in several model and crop plant species.

bioinformatics↗

bioRxiv2017

High-Quality Rice RNA-Seq-Based Co-Expression Network For Predicting Gene Function And Regulation

Inferring the genome-scale gene co-expression network is important for understanding genetic architecture underlying the complex and various biological phenotypes. The recent availability of large-scale RNA-seq sequencing-data provides great potential for co-expression network inference. In this study, for the first time, we presented a novel heterogeneous ensemble pipeline integrating three frequently used inference methods, to build a high-quality RNA-seq-based Gene Co-expression Network (GCN) in rice, an important monocot species. The quality of the network obtained by our proposed method was first evaluated and verified with the curated positive and negative gene functional link datasets, which obviously outperformed each single method. Secondly, the powerful capability of this network for associating unknown genes with biological functions and agronomic traits was showed by enrichment analysis and case studies. Particularly, we demonstrated the potential applications of our proposed method to predict the biological roles of long non-coding RNA (lncRNA) and circular RNA (circRNA) genes. Our results provided a valuable data source for selecting candidate genes to further experimental validation during rice genetics research and breeding. To enhance identification of novel genes regulating important biological processes and agronomic traits in rice and other crop species, we released the source code of constructing high-quality RNA-seq-based GCN and rice RNA-seq-based GCN, which can be freely downloaded online at https://github.com/czllab/NetMiner.

bioinformatics↗