MicroRNA Target Prediction in Gene Regulation

MicroRNAs (miRNAs) are short non-coding RNAs of approximately 22 nucleotides that exert post-transcriptional control over gene expression. By pairing with complementary sequences, typically within the 3′ untranslated region (3′ UTR) of messenger RNAs (mRNAs), miRNAs guide the RNA-induced silencing complex (RISC) to repress translation or induce mRNA decay. Accurate prediction of miRNA targets is critical for elucidating regulatory networks in development, physiology and disease. Computational approaches have evolved from simple seed-match algorithms to integrative models that combine sequence complementarity, thermodynamic stability, evolutionary conservation and RNA secondary-structure accessibility. Recent efforts harness machine learning and deep learning to improve specificity, incorporate non-canonical interactions and integrate multi-omic data. Large-scale experimental methods, such as crosslinking and immunoprecipitation sequencing (CLIP-seq), generate high-confidence interaction maps that refine in silico predictions. Together, these advances enhance our understanding of miRNA-mediated regulation, support biomarker discovery and inform therapeutic strategies targeting dysregulated miRNA networks.

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MicroRNA Target Prediction in Gene Regulation publication trend

The graph below shows the total number of articles in microrna target prediction in gene regulation across all publications each year (not limited to Nature Index journals).

Technical terms

microRNA (miRNA): Small non-coding RNA molecule, ~22 nt long, that regulates gene expression post-transcriptionally.

Seed region: Short nucleotide stretch at the 5′ end of a miRNA (typically nt 2–8) crucial for target recognition via base pairing.

3′ untranslated region (3′ UTR): Segment of mRNA downstream of the coding sequence where miRNAs predominantly bind to modulate transcript stability.

Crosslinking and immunoprecipitation sequencing (CLIP-seq): Experimental technique for mapping RNA–protein interactions in vivo by UV crosslinking, immunoprecipitation of RISC components and high-throughput sequencing.

Extracellular miRNA (exmiR): miRNA molecules detected outside cells, for example in biofluids, serving as non-invasive biomarkers.

Deep learning: Subfield of machine learning employing multi-layered neural networks to model complex, high-dimensional biological data.

References

1. TarBase-v9.0 extends experimentally supported miRNA–gene interactions to cell-types and virally encoded miRNAs. Nucleic Acids Research (2023).
2. Exploring miRNA–target gene pair detection in disease with coRmiT. Briefings in Bioinformatics (2024).
3. A deep learning method to integrate extracelluar miRNA with mRNA for cancer studies. Bioinformatics (2024).