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RNA sequencing (RNA-Seq) is the leading, routine, high-throughput, and cost-effective next-generation sequencing (NGS) approach for mapping and quantifying transcriptomes, and determining the transcriptional structure. The transcriptome is a complete collection of transcripts found in a cell or tissue or organism at a given time point or specific developmental or environmental or physiological condition. The emergence and evolution of RNA-Seq chemistries have changed the landscape and the pace of transcriptome research in life sciences over a decade. This chapter introduces RNA-Seq and surveys its recent food and agriculture applications, ranging from differential gene expression, variants calling and detection, allele-specific expression, alternative splicing, alternative polyadenylation site usage, microRNA profiling, circular RNAs, single-cell RNA-Seq, metatranscriptomics, and systems biology. A few popular RNA-Seq databases and analysis tools are also presented for each application. We began to witness the broader impacts of RNA-Seq in addressing complex biological questions in food and agriculture.

MicroRNAs (miRNAs) are small non-coding endogenous riboregulators that play significant roles in plant growth, development, metabolism, defense, and stress responses. Upland cotton is best known for its economic value as natural fiber and oil. It is also an important model for genome evolution, polyploidy effects, cell growth, and cell wall biosynthesis. The polyploid species, Gossypium hirsutum L. (2n = 52), evolved from two progenitors (2n = 26), one contributing an ancestral genome similar to A genomes of G. herbaceum and G. arboreum, and the other like the D genome of G. raimondii. There is much to be learned about Gossypium miRNAs and their gene targets, about the effects of polyploidy and their practical ramifications. In this study, small-RNA (sRNA) libraries from four Gossypium species and three tissue libraries (seed, seedling, and leaf) from G. hirsutum were constructed and analyzed using high-throughput sequencing with Illumina/MiSeq then computational prediction by MIREAP. Results showed 245 known miRNAs belonging to 67 miRNA families and 149 novel (non-redundant and redundant) miRNAs derived from 394 genomic loci. This unified study identified 21 and 33 conserved miRNA families across four Gossypium species and three tissue libraries, respectively. Similar to canonical miRNAs, the 5′ of novel miRNAs had the higher preference for U and A residues. In this study, 551 potential target genes were predicted in 45 conserved miRNA families, while 439 targets were associated with 113 novel miRNAs. The majority of the conserved (34%) and novel (22%) miRNA targets identified belonged to cellular processes and signaling. However, the functional diversity of novel miRNA targets is attributed to the tissue and species bias. Most of the identified targets were regulated by mRNA cleavage than by translational repression. These results provide a basis for better understanding regulatory networks in upland cotton (G. hirsutum) and its closest progenitors. Keywords: Cotton, Progenitor, Sequencing, Prediction, MIREAP, sRNA, miRNA, Family, Known, Novel, Gene regulation, Target