Homology-Based Prediction of Putative miRNA Loci and Their Candidate Target Genes in Phaseolus vulgaris

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression through a sequence-specific recognition of their targets, leading to degradation or inhibition of translation. In the case of plant miRNAs, they have been involved in a multitude of biological processes, from developmental processes to environmental stress responses. Massive sequencing by RNA-seq is becoming a widely used technique to discover plant miRNAs. However, if costs are considered, bioinformatics prediction is a valuable tool for miRNA discovery in plants. Among the tools available, ShortStack is the best for comprehensive prediction and annotation of miRNAs. Therefore, ShortStack was used in this study to predict miRNAs of common bean (Phaseolus vulgaris), one of the most important legumes in the world. Briefly, a high-stringency, homology-based in silico pipeline was followed to systematically predict miRNAs of P. vulgaris without the dependency on high-throughput experimental sequencing infrastructure; meaning that this study was based exclusively on computationally generated miRNA datasets derived from miRbase. Therefore, no biological small RNA-seq, degradome sequencing, or expression validation were performed in this study. In total, 57 distinct, non-redundant clusters were predicted, from which 35 miRNAs showed unique characteristic sequences. Comparative genomic cross-referencing against historical seminal common bean datasets supported our predicted loci, finding that 18 out of the 35 miRNA sequences were located at the same genomic coordinates as the previously reported loci. In addition, functional categorization of all the potential target genes revealed their putative roles in plant development and other cellular processes. It is important to emphasize that, although the predicted loci in P. vulgaris represent candidate miRNAs that require independent experimental validation, this work serves as a foundational approach to be applied for the prediction of miRNAs in underexplored plant species or those with limited genomic resources.