Small RNA Library Preparation and Illumina Sequencing in Plants

Methodologies for Discovery and Quantitative Profiling of sRNAs in Potato

Small RNAs (sRNAs) are short noncoding RNAs involved in the regulation of a wide range of biological processes in plants. Advances in high-throughput sequencing and development of new computational tools had facilitated the discovery of different classes of sRNAs, their quantification, and elucidation of their functional role in gene expression regulation by target transcript predictions. The workflow presented here allows identification of different sRNA species: known and novel potato miRNAs, and their sequence variants (isomiRs), as well as identification of phased small interfering RNAs (phasiRNAs). Moreover, it includes steps for differential expression analysis to search for regulated sRNAs across different tested biological conditions. In addition, it describes two different methods for predicting sRNA targets, in silico prediction, and degradome sequencing data analysis. All steps of the workflow are written in a clear and user-friendly way; thus they can be followed also by the users with minimal bioinformatics knowledge. We also included several in-house scripts together with valuable notes to facilitate data (pre)processing steps and to reduce the analysis time.

Integrated RNA and miRNA sequencing analysis reveals a complex regulatory network of Magnolia sieboldii seed germination

Magnolia sieboldii K. Koch (M. sieboldii) is a deciduous Chinese tree species of the Magnoliaceae family with high ornamental, medicinal, and economic benefits. The germination of M. sieboldii seeds under natural conditions is extremely difficult, thereby hindering the cultivation and breeding of this important species. The molecular mechanisms underlying M. sieboldii seed germination remain unclear due to the lack of genomic and transcriptomic resources. Here, we integrated both mRNA and miRNA sequencing to identify the genes and pathways related to M. sieboldii germination. A comprehensive full-length transcriptome containing 158,083 high-quality unigenes was obtained by single-molecule real-time (SMRT) sequencing technology. We identified a total of 13,877 genes that were differentially expressed between non-germinated and germinated seeds. These genes were mainly involved in plant hormone signal transduction and diverse metabolic pathways such as those involving lipids, sugars, and amino acids. Our results also identified a complex regulatory network between miRNAs and their target genes. Taken together, we present the first transcriptome of M. sieboldii and provide key genes and pathways associated with seed germination for further characterization. Future studies of the molecular basis of seed germination will facilitate the genetic improvement M. sieboldii.