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...
RNA Interference01:23

RNA Interference

RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
Nucleic Acid Structure01:25

Nucleic Acid Structure

The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA has a double-helix structure. The...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

You might also read

Related Articles

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

Sort by
Same author

Circulating cell type senescence signatures track distinct dimensions of health status and trajectories in human longitudinal cohorts.

Cell reports·2026
Same author

SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types.

Molecular cell·2026
Same author

SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types.

bioRxiv : the preprint server for biology·2026
Same author

Circulating Cell Type Senescence Signatures Reveal High-Resolution Health Status and Trajectories in Human Longitudinal Studies.

medRxiv : the preprint server for health sciences·2026
Same author

Rebuttal letter to "The need for functional correlation in assessing peripheral and CNS immunity during EBV reactivation".

Journal of neuroimmunology·2026
Same author

Fast barcode calling based on <i>k</i>-mer distances.

PNAS nexus·2026
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 25, 2026

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells
07:19

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

Published on: September 28, 2011

Functional microRNA targets in protein coding sequences.

Martin Reczko1, Manolis Maragkakis, Panagiotis Alexiou

  • 1Institute of Molecular Oncology, Biomedical Sciences Research Center, Vari, Greece. reczko@fleming.gr

Bioinformatics (Oxford, England)
|January 31, 2012
PubMed
Summary
This summary is machine-generated.

MicroRNA (miRNA) binding sites in protein-coding sequences (CDSs) are functional. A new computational model, DIANA-microT-CDS, improves miRNA target gene prediction by analyzing both CDS and 3'-untranslated regions.

More Related Videos

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library
08:40

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Published on: April 6, 2012

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

Related Experiment Videos

Last Updated: May 25, 2026

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells
07:19

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

Published on: September 28, 2011

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library
08:40

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Published on: April 6, 2012

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

Area of Science:

  • Genetics
  • Bioinformatics
  • Molecular Biology

Background:

  • MicroRNA (miRNA) binding sites within protein-coding sequences (CDSs) are experimentally validated as functional regulators of gene expression.
  • Understanding miRNA targeting mechanisms is crucial for deciphering gene regulation.
  • Previous computational models primarily focused on miRNA binding sites in 3'-untranslated regions (3'-UTRs).

Purpose of the Study:

  • To computationally analyze miRNA target sites within CDSs and 3'-UTRs independently.
  • To develop a novel, integrated computational model for predicting miRNA target genes.
  • To compare the performance of the new model against existing popular programs.

Main Methods:

  • Computational analysis of miRNA target sites using features from mammalian high-throughput immunoprecipitation and sequencing data.
  • Independent feature extraction and model development for CDS and 3'-UTR regions.
  • Integration of CDS and 3'-UTR models into the DIANA-microT-CDS tool.

Main Results:

  • Distinct sets of features and predictive models were identified for miRNA binding sites in CDSs versus 3'-UTRs.
  • The DIANA-microT-CDS model demonstrated higher sensitivity in predicting miRNA target genes compared to existing methods.
  • Genes with shorter 3'-UTRs showed preferential targeting in their CDS, suggesting evolutionary selection for alternative binding sites.

Conclusions:

  • The DIANA-microT-CDS model provides a more sensitive approach to miRNA target gene prediction by incorporating CDS targeting.
  • Evolutionary pressure may favor miRNA binding sites in CDSs when 3'-UTR space is limited.
  • This study highlights the functional importance and distinct characteristics of miRNA binding sites in different gene regions.