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MicroRNAs (miRNAs) are a type of regulatory molecule that allows (or prevents) the translation of mRNA (messenger RNA), thereby blocking the encoding of a given protein. Between 18 and 25 nucleotides in length, they act as important regulators of gene expression in plants, animals, and viruses. In plants, miRNAs play crucial roles in processes such as morphogenesis, floral induction, reproductive development, and responses to biotic and abiotic stresses. The identification and characterization of these molecules through experimental methods are costly and time-consuming, making computational approaches essential tools for reducing the hypothesis space to be experimentally validated. This study aimed to survey the main public databases and computational methods for predicting miRNAs in plants. To this end, an integrative review was conducted of the scientific literature published between 2020 and 2025, searching scientific article databases using descriptors in both Portuguese and English related to miRNA, gene regulation, and bioinformatics. The main miRNA data repositories were identified and characterized, as well as the main computational methods for predicting miRNAs in plants, categorized as comparative, non-comparative, and based on next-generation sequencing data. Additionally, a descriptive tutorial was developed for using the TargetFinder tool on the Galaxy platform. The inherent challenges in running bioinformatics tools limit the conduct of this type of research, which indicates that partnerships between researchers in the Biological Sciences and related fields and information technology professionals can be excellent alternatives for developing this type of research, whose results can be highly promising in understanding the mechanisms of gene expression regulated by miRNA molecules, with the use of biological models such as plants and other living organisms.
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