Design, execution, and interpretation of plant RNA-seq analyses.

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

Transcriptome changes in the developing sugarcane culm associated with high yield and early-season high sugar content.

Sugarcane, with its exceptional carbon dioxide assimilation, biomass and sugar yield, has a high potential for the production of bio-energy, bio-plastics and high-value products in the food and pharmaceutical industries. A crucial challenge for long-term economic viability and environmental sustainability is also to optimize the production of biomass composition and carbon sequestration. Sugarcane varieties such as KQ228 and Q253 are highly utilized in the industry. These varieties are characterized by a high early-season sugar content associated with high yield. In order to investigate these correlations, 1,440 internodes were collected and combined to generate a set of 120 samples in triplicate across 24 sugarcane cultivars at five different development stages. Weighted gene co-expression network analysis (WGCNA) was used and revealed for the first time two sets of co-expressed genes with a distinct and opposite correlation between fibre and sugar content. Gene identification and metabolism pathways analysis was used to define these two sets of genes. Correlation analysis identified a large number of interconnected metabolic pathways linked to sugar content and fibre content. Unsupervised hierarchical clustering of gene expression revealed a stronger level of segregation associated with the genotypes than the stage of development, suggesting a dominant genetic influence on biomass composition and facilitating breeding selection. Characterization of these two groups of co-expressed key genes can help to improve breeding program for high fibre, high sugar species or plant synthetic biology.