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Background: MicroRNAs (miRNAs) are small noncoding RNAs that play important post-transcriptional regulatory roles in animals and plants. Despite the importance of plant miRNAs, the inherent complexity of miRNA biogenesis in plants hampers the application of standard miRNA prediction tools, which are often optimized for animal sequences. Therefore, computational approaches to predict putative miRNAs (merely) from genomic sequences, regardless of their expression levels or tissue specificity, are of great interest.
Results: Here, we present AmiR-P3, a novel ab initio plant miRNA prediction pipeline that leverages the strengths of various utilities for its key computational steps. Users can readily adjust the prediction criteria based on the state-of-the-art biological knowledge of plant miRNA properties. The pipeline starts with finding the potential homologs of the known plant miRNAs in the input sequence(s) and ensures that they do not overlap with protein-coding regions. Then, by computing the secondary structure of the presumed RNA sequence based on the minimum free energy, a deep learning classification model is employed to predict potential pre-miRNA structures. Finally, a set of criteria is used to select the most likely miRNAs from the set of predicted miRNAs. We show that our method yields acceptable predictions in a variety of plant species.
Conclusion: AmiR-P3 does not (necessarily) require sequencing reads and/or assembled reference genomes, enabling it to identify conserved and novel putative miRNAs from any genomic or transcriptomic sequence. Therefore, AmiR-P3 is suitable for miRNA prediction even in less-studied plants, as it does not require any prior knowledge of the miRNA repertoire of the organism. AmiR-P3 is provided as a docker container, which is a portable and self-contained software package that can be readily installed and run on any platform and is freely available for non-commercial use from: https://hub.docker.com/r/micrornaproject/amir-p3.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308016 | PLOS |
PLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Objective: This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility.
Methods: We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis.
Background: This study aimed to identify the diagnostic and prognostic ability of serum miR-411-3p in patients with acute myeloid leukemia (AML).
Methods: Blood samples were collected from 60 AML patients and 60 healthy controls to measure serum miR-411-3p and thereafter discuss its potential clinical value.
Results: Serum miR-411-3p was decreased in AML patients and was even lower in those with M4/M5 subtypes or high white blood cell count or adverse cytogenetic risk.
Background: The lncRNA-miRNA-mRNA regulatory network is recognized for its significant role in cardiovascular diseases, yet its involvement in in-stent restenosis (ISR) remains unexplored. Our study aimed to investigate how this regulatory network influences ISR occurrence and development by modulating inflammation and immunity.
Methods: By utilizing data extracted from the Gene Expression Omnibus (GEO) database, we constructed the lncRNA-miRNA-mRNA regulatory network specific to ISR.
Bioimpacts
August 2025
Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
Introduction: Hepatocellular carcinoma (HCC) remains a major cause of cancer mortality, and effective therapeutic options are limited. MicroRNA‑372‑3p (miR‑372‑3p) has been implicated in HCC, yet its exact role is unclear.
Methods: We established miR‑372‑3p‑overexpressing HCC cell lines (HepG2, SNU‑449, JHH‑4) via lentiviral transduction.