Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...

MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...

MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Experimental RNAi

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Age, sex and Alzheimer's disease: a longitudinal study of 3xTg-AD mice reveals sex-specific disease trajectories and inflammatory responses mirrored in postmortem brains from Alzheimer's patients.

Alzheimer's research & therapy·2024

Same author

Age, Sex and Alzheimer's disease: A longitudinal study of 3xTg-AD mice reveals sex-specific disease trajectories and inflammatory responses mirrored in postmortem brains from Alzheimer's patients.

bioRxiv : the preprint server for biology·2024

Same author

The endosomal escape vehicle platform enhances delivery of oligonucleotides in preclinical models of neuromuscular disorders.

Molecular therapy. Nucleic acids·2023

Same author

A synthesis strategy for tetracyclic terpenoids leads to agonists of ERβ.

Nature communications·2019

Same author

Synthesis of C14-Desmethylene Corialactone D and Discovery of Inhibitors of Nerve Growth Factor Mediated Neurite Outgrowth.

Organic letters·2019

Same author

Clinical Utility of a STAT3-Regulated miRNA-200 Family Signature with Prognostic Potential in Early Gastric Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2018

Same journal

Research on multi-trait genome association study method based on Shannon information entropy.

BMC bioinformatics·2026

Same journal

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

BMC bioinformatics·2026

Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries

Published on: December 1, 2023

Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy.

Bing Liu¹, Jiuyong Li, Anna Tsykin

¹School of Computer and Information Science, University of South Australia, Adelaide, SA 5095, Australia. Bing.Liu@unisa.edu.au

BMC Bioinformatics

|December 17, 2009

Summary

This summary is machine-generated.

This study introduces a novel Bayesian network method to uncover complex microRNA-mRNA interactions, including up-, down-, and mixed regulation. The approach identifies significant known and novel interactions, advancing our understanding of gene regulation in biological processes like EMT.

More Related Videos

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Related Experiment Videos

Last Updated: Jun 17, 2026

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries

Published on: December 1, 2023

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Area of Science:

Computational Biology
Genomics
Molecular Biology

Background:

MicroRNAs (miRNAs) post-transcriptionally regulate gene expression, playing critical roles in biological processes.
The precise regulatory mechanisms and functions of most miRNAs remain largely unknown.
Existing computational methods primarily focus on miRNA-mediated gene silencing (down-regulation), neglecting complex regulatory patterns.

Purpose of the Study:

To develop a computational method for identifying complex miRNA-mRNA interactions, encompassing all regulatory types (up-, down-, and mixed-regulation).
To analyze miRNA-mRNA interactions in the context of epithelial-mesenchymal transition (EMT).

Main Methods:

Utilized Bayesian network structure learning with a splitting-averaging strategy.
Integrated miRNA-targeting information, miRNA and mRNA expression profiles, and sample categories.
Applied the method to analyze EMT-related datasets.

Main Results:

The proposed method successfully identified all types of miRNA-mRNA interactions from the analyzed data.
Discovered interactions of significant biological relevance, including validated targets like the miR-200 family's regulation of ZEB1/ZEB2.
Identified novel, statistically significant interactions, such as LOX's extensive interactions with miR-200 family members, warranting future validation.

Conclusions:

Presented a novel Bayesian network-based method for exploring complex miRNA-mRNA interactions across various physiological conditions.
The method effectively leverages heterogeneous data to uncover both known and novel miRNA-mRNA interactions.
This approach surpasses traditional Bayesian network methods in capturing the full spectrum of miRNA-mRNA regulatory relationships.