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Updated: Jul 18, 2026

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
Published on: October 7, 2025
Unique folding of precursor microRNAs: quantitative evidence and implications for de novo identification
Stanley Ng Kwang Loong1, Santosh K Mishra
1Bioinformatics Institute, Matrix, Singapore. stanley@bii.a-star.edu.sg
Abstract:
MicroRNAs (miRNAs) participate in diverse cellular and physiological processes through the post-transcriptional gene regulatory pathway. Hairpin is a crucial structural feature for the computational identification of precursor miRNAs (pre-miRs), as its formation is critically associated with the early stages of the mature miRNA biogenesis. Our incomplete knowledge about the number of miRNAs present in the genomes of vertebrates, worms, plants, and even viruses necessitates thorough understanding of their sequence motifs, hairpin structural characteristics, and topological descriptors. In this in-depth study, we investigate a comprehensive and heterogeneous collection of 2241 published (nonredundant) pre-miRs across 41 species (miRBase 8.2), 8494 pseudohairpins extracted from the human RefSeq genes, 12,387 (nonredundant) ncRNAs spanning 457 types (Rfam 7.0), 31 full-length mRNAs randomly selected from GenBank, and four sets of synthetically generated genomic background corresponding to each of the native RNA sequence. Our large-scale characterization analysis reveals that pre-miRs are significantly different from other types of ncRNAs, pseudohairpins, mRNAs, and genomic background according to the nonparametric Kruskal-Wallis ANOVA (p<0.001). We examine the intrinsic and global features at the sequence, structural, and topological levels including %G+C content, normalized base-pairing propensity P(S), normalized minimum free energy of folding MFE(s), normalized Shannon entropy Q(s), normalized base-pair distance D(s), and degree of compactness F(S), as well as their corresponding Z scores of P(S), MFE(s), Q(s), D(s), and F(S). The findings will promote more accurate guidelines and distinctive criteria for the prediction of novel pre-miRs with improved performance.
Insights
Precursor microRNAs (pre-miRs) possess unique sequence and structural features distinguishing them from other RNA types. This study characterizes these features to improve the computational prediction of novel pre-miRs.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genomics
Background:
- MicroRNAs (miRNAs) regulate gene expression post-transcriptionally.
- Precursor miRNAs (pre-miRs) form hairpin structures essential for mature miRNA biogenesis.
- Accurate identification of pre-miRs is crucial due to their diverse roles across species.
Purpose of the Study:
- To comprehensively characterize the sequence, structural, and topological features of pre-miRs.
- To differentiate pre-miRs from other RNA types and genomic backgrounds.
- To establish criteria for improved computational prediction of novel pre-miRs.
Main Methods:
- Analysis of 2241 published pre-miRs from miRBase 8.2 across 41 species.
- Comparison with 8494 human pseudohairpins, 12,387 ncRNAs from Rfam 7.0, 31 mRNAs, and synthetic genomic backgrounds.
- Examination of sequence features (%G+C content) and structural/topological descriptors (P(S), MFE(s), Q(s), D(s), F(S), and their Z scores).
- Statistical analysis using the Kruskal-Wallis ANOVA test.
Main Results:
- Pre-miRs exhibit statistically significant differences from ncRNAs, pseudohairpins, mRNAs, and genomic backgrounds (p<0.001).
- Distinct sequence and structural characteristics were identified in pre-miRs compared to other RNA classes.
- Key features like base-pairing propensity, minimum free energy, Shannon entropy, base-pair distance, and compactness differentiate pre-miRs.
Conclusions:
- Pre-miRs possess unique intrinsic and global features that distinguish them from other RNA molecules.
- These identified characteristics provide a basis for developing more accurate computational tools for pre-miR prediction.
- The study offers improved guidelines and criteria for identifying novel precursor microRNAs.
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