MicroRNA in disease regulation

OMCD provides a web-based database for pan-cancer miRNA expression analysis, enabling visualization, statistical analysis, and exploration of miRNA clusters across cancer types.

The paper predicts plant-derived xenomiRs using random forest and 1D CNN models, identifying 241 potential candidates and analyzing their potential functions in the human body.

The paper investigates how miRNA-mRNA networks regulate cell-type-specific transcriptomes during human brain development using AGO2-HITS-CLIP, single-cell profiling, and bipartite network analysis.

The paper identifies 21 key differentially expressed miRNAs across 14 cancer types using TCGA data, showing their potential as biomarkers and therapeutic targets.

The paper develops ADmiRE to annotate miRNA variants in large population datasets, identifies highly constrained miRNAs, and provides conservation scores for future disease studies.

TEsmall identifies small RNAs associated with targeted inhibitor resistance in melanoma by analyzing multiple small RNA classes in an integrated workflow.

The paper develops a quantitative model to predict microRNA targeting efficacy in Drosophila, outperforming existing models and updating TargetScanFly.

The paper analyzes how intronic miRNAs regulate gene expression in specific cellular contexts and shows their role in controlling protein crowding through translation regulation.

The paper investigates the role of DDX6 in linking microRNA functions to mRNA stability and translation in ESCs, finding that DDX6 loss separates microRNA's translational repression from transcript destabilization.

The paper introduces a deep learning method for miRNA target prediction that analyzes the entire miRNA and 3UTR mRNA, outperforming existing methods by considering non-seed region interactions.

The paper shows that multiple microRNAs work together to control EMT by reducing noise and enhancing gene expression changes, more accurately reflecting endogenous regulation.

The paper identifies a GAU motif recognized by LIN28's CSD domain, which enhances let-7 microRNA suppression in Class I precursors, contributing to development and cancer.

The paper introduces xMD-miRNA-seq to obtain in vivo miRNA expression from colon epithelial cells using FFPE tissues, showing cell-specific miRNA profiles comparable to other epithelial sources.

This study reveals how hnRNP A1 binds to the terminal loop of pri-mir-18a, destabilizing stem base-pairing to enhance miRNA processing.

The paper shows miR-21-5p and miR-30a-5p are identical between human and bovine, and their presence in human blood after milk ingestion is likely due to endogenous regulation or technical issues, not xenomiR uptake.

The paper uses Frac-seq to analyze microRNA effects on gene expression in asthma, identifying six key microRNAs that regulate ~90% of their targets and contribute to disease mechanisms.

This study confirms the stability of miRNAs in platelet-poor plasma and purified RNA, showing they are less prone to degradation than other RNAs.

The paper identifies two Jurkat subpopulations with distinct miRNA profiles during camptothecin-induced apoptosis, showing 6 miRNAs with inverse expression.

SeedVicious predicts microRNA target and near-target sites, detects functional variation, and complements existing tools with improved precision.

The paper shows that miRNA activity and specificity depend on both target site architecture and miRNA abundance, challenging the binary view of target site functionality.

The paper shows that plant miRNAs detected in human plasma are likely artifacts, not evidence of cross-kingdom RNAi.

This paper introduces a structural perturbation method (SPM) to predict miRNA-disease associations, showing improved accuracy over existing methods in cross-validation and case studies.

The paper introduces miRNAtools, a web repository of miRNA analysis tools and tutorials for advanced training in miRNA functional analysis.

The paper compares miRNA structures of mirtrons and non-mirtrons in human and mouse to explore their maturation and structural differences.

The paper presents a mathematical model of the Myc/E2F/mir-17-92 network to explain how differential cellular proliferation can be influenced by microRNA dynamics in a cell-type specific manner.

The paper identifies miR-106b-regulated genes in cholangiocarcinoma cells, revealing repression of KLF family members and their potential role in tumor biology.

The paper establishes the landscape of human cell-specific microRNA expression using small RNA-seq data from diverse cell types and tissues, identifying novel microRNA loci and patterns correlated with super-enhancer activity.

The paper presents a comprehensive miRNA and ncRNA catalogue for the short-lived fish Nothobranchius furzeri and compares it to other fish species.

The paper develops a method to study pri-miRNA processing kinetics in vivo and shows co- and post-transcriptional processing occurs with varying speeds.

The paper shows that binary isomiR profiles can accurately classify 32 TCGA cancer types with high sensitivity and low false discovery rate.