How to Predict miRNA Targets?

MicroRNAs (miRNAs) have emerged as pivotal regulators of gene expression, exerting profound influences on intricate cellular processes and disease mechanisms. An in-depth comprehension of the target genes of miRNAs is imperative for elucidating their functional roles and deciphering intricate regulatory networks. Recent years have witnessed remarkable advancements in the realm of miRNA target prediction, utilizing innovative computational approaches and extensive experimental data. This article delves into the intricate landscape of miRNA target prediction, showcasing state-of-the-art methodologies and tools that empower researchers to unravel the enigmatic aspects of miRNA-mediated gene regulation.

The Role of miRNAs in Gene Regulation

MicroRNAs, which are small non-coding RNA molecules typically consisting of approximately 22 nucleotides, have been unveiled as pivotal entities in the complex network of gene regulation. Through their remarkable ability to bind to messenger RNAs (mRNAs), miRNAs modulate gene expression at the post-transcriptional level, leading to the degradation of target mRNAs or repression of translation. This intricate regulatory mechanism enables miRNAs to orchestrate diverse cellular processes such as development, differentiation, proliferation, and disease progression.

Explore the role of miRNAs by reading our article Overview of MicroRNAs (miRNAs).

Challenges in miRNA Target Prediction

Accurate prediction of miRNA target genes presents several challenges owing to the intricate nature of miRNA-target interactions, which are highly dependent on the complex context in which they occur. Several factors contribute to this complexity, including the degree of sequence complementarity between miRNAs and their targets, accessibility of target sites, conservation of target sites across species, and potential cooperative interactions with other regulatory molecules. Furthermore, the presence of multiple potential target sites within a single mRNA and the dynamic nature of miRNA-mediated regulation further exacerbates the complexity of this task.

Stepwise strategy for miRNA target prediction.Stepwise strategy for miRNA target prediction. (Riolo et al., 2020)

Computational Tools for miRNA Target Prediction


TargetScan is a widely used resource that provides comprehensive miRNA target predictions across various species. Leveraging evolutionary conservation, TargetScan employs sophisticated algorithms to predict target genes, considering both conserved and non-conserved target sites. The platform offers valuable insights into miRNA target interactions and supports further analysis of the functional consequences of these interactions.


RNAhybrid employs a thermodynamics-based approach, considering the dimeric secondary structure of both miRNAs and target mRNAs. By restricting intramolecular dimer formation and detecting optimal target sites, RNAhybrid enhances the accuracy of miRNA target prediction. Customization options, including energy thresholds and hybridization site biases, empower researchers to fine-tune their predictions.


miRDB is an online repository that harnesses the power of machine learning to predict miRNA targets. By integrating high-throughput sequencing data and utilizing identified features associated with miRNA binding and target down-regulation, miRDB offers comprehensive predictions for several species. Beyond traditional 3' UTR regions, miRDB also explores coding regions and 5' UTR regions, broadening the scope of target exploration.


The Starbase analysis platform is a powerful tool utilized for the comprehensive analysis of data associated with 32 different cancer types, integrating information sourced from the TCGA project. This vast dataset encompasses various types of RNA molecules, including lncRNAs, miRNAs, snoRNAs, mRNAs, circRNAs, and more. Starbase provides valuable insights into miRNA-lncRNA, miRNA-pseudogene, miRNA-sncRNA, and miRNA-mRNA interactions within the pan-cancer network. Additionally, it offers a pan-cancer map showcasing interactions between RNA-binding proteins (RBPs) and lncRNAs, pseudogenes, and mRNAs, validated through CLIP-seq experiments. Given its extensive range of information, the Starbase database serves as a valuable resource for data mining across different cancer histologies.

Furthermore, Starbase offers a comprehensive plant miRNA target database, supported by mRNA degradome sequencing data. It also features a web-based tool that leverages mRNA degradome data to predict miRNA targets. These functionalities enhance the understanding of plant miRNA regulation and facilitate in-depth investigations into their target genes.


miRTarBase stands as a comprehensive database, accumulating over 360,000 experimentally validated miRNA-target interactions (MTIs). These interactions undergo rigorous validation through diverse experimental techniques such as reporter analysis, protein blotting, microarrays, and next-generation sequencing experiments. Since its inception in 2011, miRTarBase has continuously updated its repository of miRNA-target gene-related information. The latest release, version 9.0 from September 2021, incorporates data from more than 13,000 experimentally supported articles covering miRNA-target interactions across 37 species. Users can conveniently search the database online or download MTI datasets for local screening, facilitating comprehensive analysis of miRNA-target interactions.


TarBase provides a curated collection of experimentally verified miRNA-target data, specifically for human and mouse target genes. Experimental evidence is categorized into two groups: low and high confidence. The low-confidence category represents traditional experimental approaches, which exhibit relatively higher reliability compared to high-throughput sequencing-based analysis results. Researchers can access miRNA target gene information supported by low-confidence experimental methods through TarBase's online search feature. The platform allows input of miRNA names in the miRBase database format and supports gene names in the form of gene symbols or Ensembl gene IDs.


PsRNATarget is a web-based tool dedicated to predicting plant miRNA target genes. The platform consists of three modules: "Submit small RNAs," "Submit target candidates," and "Submit small RNAs and targets." The latter module facilitates the analysis of miRNAs from non-model organisms. PsRNATarget empowers researchers to explore and gain insights into the complex regulatory mechanisms of plant miRNAs.


psRobot is a web-based tool designed for the prediction of target sites within transcripts of a given plant species. Leveraging newly discovered or published small RNA data, psRobot enables the identification of target sites for small RNAs. Moreover, researchers can utilize psRobot for large-scale prediction of plant miRNA target genes by deploying the tool locally, offering flexibility and scalability in target prediction analyses.

CD Genomics miRNA Target Prediction Platform

CD Genomics provides an in-house miRNA target gene prediction analysis service, focusing not only on miRNA target gene prediction but also on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of target genes. This integrated approach allows for the prediction of biological processes and potential involvement in miRNA regulation. Our extensive database encompasses a wealth of information on both plants and animals, facilitating target gene analysis, regulatory analysis, and other advanced trinity analyses.


  1. Riolo, Giulia, et al. "miRNA targets: from prediction tools to experimental validation." Methods and protocols 4.1 (2020): 1.
* For Research Use Only. Not for use in diagnostic procedures.

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