Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

MicroRNAs01:22

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...
MicroRNAs01:22

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...
MicroRNAs01:22

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...
siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the ATP-dependent...
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA (lncRNA)...
Experimental RNAi02:15

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

Increased expression of autophagy-related proteins in keratocystic odontogenic tumours: its possible association with growth potential.

The British journal of oral & maxillofacial surgery·2014
Same author

Inhibition of Akt reverses the acquired resistance to sorafenib by switching protective autophagy to autophagic cell death in hepatocellular carcinoma.

Molecular cancer therapeutics·2014
Same author

The sabotage of the bacterial transcription machinery by a small bacteriophage protein.

Bacteriophage·2014
Same author

In vivo and in vitro evidence of the sex-dependent pharmacokinetics and disposition of G004, a potential hypoglycemic agent, in rats.

European journal of drug metabolism and pharmacokinetics·2014
Same author

Inositol pyrophosphates mediate the effects of aging on bone marrow mesenchymal stem cells by inhibiting Akt signaling.

Stem cell research & therapy·2014
Same author

Sequential release of autophagy inhibitor and chemotherapeutic drug with polymeric delivery system for oral squamous cell carcinoma therapy.

Molecular pharmaceutics·2014
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 16, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Identifying miRNAs, targets and functions.

Bing Liu1, Jiuyong Li, Murray J Cairns

  • 1School of Biomedical Sciences and Pharmacy, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia. murray.cairns@newcastle.edu.au.

Briefings in Bioinformatics
|November 24, 2012
PubMed
Summary
This summary is machine-generated.

This review explores computational methods for understanding microRNA (miRNA) functions. It covers miRNA functional annotation and regulatory module inference using diverse data, highlighting challenges in computational biology.

Keywords:
functional annotationfunctional miRNA–mRNA regulatory modulesmiRNA

More Related Videos

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay
12:49

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Published on: May 25, 2015

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
09:06

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method

Published on: October 7, 2025

Related Experiment Videos

Last Updated: May 16, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay
12:49

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Published on: May 25, 2015

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
09:06

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method

Published on: October 7, 2025

Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Genomics

Background:

  • MicroRNAs (miRNAs) are key regulators of gene expression.
  • Understanding miRNA function is crucial for biology and disease research.
  • Computational approaches are vital for miRNA target identification and functional inference.

Purpose of the Study:

  • To review computational methods for inferring microRNA (miRNA) functions.
  • To focus on miRNA functional annotation and regulatory module inference.
  • To highlight challenges in computational biology for miRNA research.

Main Methods:

  • Integration of heterogeneous data sources for functional inference.
  • Review of computational strategies for miRNA discovery.
  • Examination of computational methods for miRNA-target identification.

Main Results:

  • Computational methods enable functional annotation of miRNAs.
  • Inferring miRNA regulatory modules is achievable through data integration.
  • Significant challenges remain in computational miRNA research.

Conclusions:

  • Computational methods are essential for elucidating miRNA functions.
  • Integrating diverse data improves understanding of miRNA roles.
  • Further advancements in computational biology are needed for miRNA research.