In the past two decades, research on microRNAs (miRNAs) as gene regulatory factors has received extensive attention. In plants, miRNAs are not only related to growth and development and environmental stimulation, but in crops, miRNAs are associated with many important agronomic traits. Modifications to miRNAs have been used to improve the agricultural traits of crops.
However, there is currently no professional plant miRNA database. For instance, as the most popular miRNA database, miRBase only provides storage and access to miRNAs in all species through the submission of miRNA data by researchers. There is no uniform method and there is no real quality control for these miRNAs. Some scholars have pointed out that the overall quality of miRBase is suboptimal, not only because the data is not integrated, but also there is a lot of noise.
Meanwhile, annotation or identification of plant miRNAs is much more difficult than that in animals with the following reasons. First, compared to relatively uniform miRNAs in animals, many plant miRNAs have more variable precursor lengths and more prevalent large paralogous families, which greatly increased the difficulty of plant miRNA discovery. Another challenge of accurately identifying plant miRNAs is how to exclude short interfering RNAs (siRNAs) from deeply sequenced small RNA libraries, and the large number of endogenous siRNAs has been a major factor in the erroneous annotation of siRNAs as miRNAs. This annotation noise resulting in many questionable miRNA annotations, is significantly enlarged in public miRNA repository when considering there are no standard methods to process and annotate plant miRNAs.
Fortunately, in the last decade, we developed miRDeep-P, a popular tool specifically used to identify plant miRNAs, and achieved much success since there are many miRNA annotated by this tool (over 40 plant species). In addition, new plant miRNA criteria were released in Jan. 2018. Thus, we decided to update our tools miRDeep-P to miRDeep-P2 through overhauling its old algorithm and combining the new plant miRNA criteria.
Taken together, to overcome these challenges and bridge the gaps (lack of comprehensive plant miRNA database) and take the advantage of our experience on annotating/identifying plant miRNAs, two labs, Yang lab from BAAFS (Beijing Academy of Agricultural and Forestry Sciences) and Li lab from Peking University, together initialized this project which aims to construct a comprehensive functional plant miRNA database, and we named it PmiRDB (plant miRNA DataBase).