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...

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...

Riboswitches

Riboswitches

Riboswitches are non-coding mRNA domains that regulate the transcription and translation of downstream genes without the help of proteins. Riboswitches bind directly to a metabolite and can form unique stem-loop or hairpin structures in response to the amount of the metabolite present. They have two distinct regions – a metabolite-binding aptamer and an expression platform.
The aptamer has high specificity for a particular metabolite which allows riboswitches to specifically regulate...

Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

You might also read

Related Articles

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

Sort by

Same author

How to Use Quantum Computers for Biomolecular Free Energies.

Journal of chemical theory and computation·2026

Same author

What Do Single-Cell Models Already Know About Perturbations?

Genes·2025

Same author

Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.

Journal of chemical theory and computation·2025

Same author

Hierarchical Quantum Embedding by Machine Learning for Large Molecular Assemblies.

Journal of chemical theory and computation·2025

Same author

Foundation models of protein sequences: A brief overview.

Current opinion in structural biology·2025

Same author

multiDGD: A versatile deep generative model for multi-omics data.

Nature communications·2024

Same journal

Limited evidence for evolutionarily conserved targeting of long non-coding RNAs by microRNAs.

Silence·2013

Same journal

Analysis of hairpin RNA transgene-induced gene silencing in Fusarium oxysporum.

Silence·2013

Same journal

Generation of a luciferase-based reporter for CHH and CG DNA methylation in Arabidopsis thaliana.

Silence·2013

Same journal

Strand-specific libraries for high throughput RNA sequencing (RNA-Seq) prepared without poly(A) selection.

Silence·2013

Same journal

MicroRNAs from the same precursor have different targeting properties.

Silence·2012

Same journal

RNA: methods and protocols - a new series.

Silence·2012

See all related articles

Search research articles

Related Experiment Video

Updated: May 11, 2026

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Published on: June 15, 2016

cWords - systematic microRNA regulatory motif discovery from mRNA expression data.

Simon H Rasmussen¹, Anders Jacobsen², Anders Krogh¹

¹Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, Copenhagen N, 2200, Denmark.

|May 22, 2013

Summary

This summary is machine-generated.

The cWords tool efficiently discovers regulatory motifs in gene expression data, identifying small RNA targets and binding sites. This method offers improved speed and performance for analyzing complex biological datasets.

More Related Videos

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

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

Published on: April 25, 2022

Related Experiment Videos

Last Updated: May 11, 2026

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Published on: June 15, 2016

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

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

Published on: April 25, 2022

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Post-transcriptional gene regulation by small RNAs is crucial for organism development.
Dysregulation of regulatory RNAs is implicated in various diseases.
Identifying sequence motifs is key to understanding RNA-mediated gene regulation.

Purpose of the Study:

To present an improved computational method, cWords, for discovering regulatory motifs.
To enhance the speed and analytical capabilities of motif discovery tools.
To provide intuitive data interpretation for RNA regulatory analysis.

Main Methods:

Developed and refined the cWords algorithm for motif discovery in differential gene expression datasets.
Implemented rigorous statistical methods and motif clustering for analysis.
Benchmarked cWords against existing methods using miRNA perturbation experiments.

Main Results:

Achieved over 100x speed improvement in cWords analysis.
Demonstrated comparable or superior performance to miReduce and Sylamer.
Successfully identified miRNA binding motifs and potential siRNA off-target sites.

Conclusions:

cWords is a versatile, user-friendly tool for regulatory motif discovery.
The method offers robust statistical underpinnings and enhanced performance.
Integrated visualization tools facilitate efficient interpretation of complex biological data.