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

RNA Structure01:19

RNA Structure

7.9K
The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...
7.9K
RNA Structure01:23

RNA Structure

79.4K
Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
79.4K
RNA-seq03:21

RNA-seq

12.2K
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...
12.2K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

10.0K
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...
10.0K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

3.7K
3.7K
Ribosomal RNA Synthesis02:53

Ribosomal RNA Synthesis

15.0K
Ribosome synthesis is a highly complex and coordinated process involving more than 200 assembly factors. The synthesis and processing of ribosomal components occurs not only in the nucleolus but also in the nucleoplasm and the cytoplasm of eukaryotic cells.
Ribosome biogenesis begins with the synthesis of 5S and 45S pre-rRNAs by distinct RNA polymerases. The primary transcripts are extensively processed and modified before they are bound and folded by ribosomal proteins and assembly factors,...
15.0K

You might also read

Related Articles

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

Sort by
Same author

TUSC3 serves as a rate-limiting gatekeeper of a glycan-mediated ER triage checkpoint for BMP4/Dpp.

Cell reports·2026
Same author

Overprinting with tomographic volumetric additive manufacturing.

Nature communications·2026
Same author

Identification of Antioxidant and Anti-Inflammatory Activity of Sea Cucumber (<i>Holothuria tubulosa</i>) Active Peptides by a Combined Approach of Omics Data and Bioinformatics Analysis.

Marine drugs·2026
Same author

High-efficiency multi-scale holographic volumetric 3D printing with a phase light modulator.

Light, science & applications·2026
Same author

Mining cancer genomes for copy number alterations identifies glycosylation enzymes as oncogenic drivers.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Intra-diaphragmatic extralobar pulmonary sequestration: Surgical approaches and outcome.

Journal of pediatric surgery·2025
Same journal

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

BioData mining·2026
Same journal

Learning a distance for the clustering of patients with amyotrophic lateral sclerosis.

BioData mining·2026
Same journal

Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.

BioData mining·2026
Same journal

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

BioData mining·2026
Same journal

AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease.

BioData mining·2026
Same journal

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.

BioData mining·2026
See all related articles

Related Experiment Video

Updated: Feb 25, 2026

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

5.3K

nRC: non-coding RNA Classifier based on structural features.

Antonino Fiannaca1, Massimo La Rosa1, Laura La Paglia1

  • 1ICAR-CNR, National Research Council of Italy, Via Ugo La Malfa, Palermo, 90146 Italy.

Biodata Mining
|August 9, 2017
PubMed
Summary
This summary is machine-generated.

A new tool called nRC (non-coding RNA Classifier) uses deep learning to classify non-coding RNA (ncRNA) types based on their secondary structures. This bioinformatics approach achieves 74% accuracy, outperforming existing methods.

Keywords:
ClassificationDeep learningStructural featuresncRNA

More Related Videos

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.3K
RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

26.4K

Related Experiment Videos

Last Updated: Feb 25, 2026

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

5.3K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.3K
RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

26.4K

Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Non-coding RNAs (ncRNAs) play crucial roles in gene regulation across biological processes and diseases.
  • High-throughput technologies necessitate advanced bioinformatics tools for understanding ncRNA functions.
  • Distinguishing between diverse ncRNA classes is essential for biological and clinical applications.

Purpose of the Study:

  • To introduce nRC (non-coding RNA Classifier), a novel tool for classifying ncRNA.
  • To leverage deep learning and secondary structure features for improved ncRNA classification.

Main Methods:

  • Extraction of features from ncRNA secondary structures.
  • Implementation of a supervised classification algorithm using convolutional neural networks (deep learning).
  • Testing the nRC tool on 13 distinct ncRNA classes.

Main Results:

  • The nRC tool achieved approximately 74% accuracy and sensitivity in classifying 13 ncRNA classes.
  • Performance was evaluated using standard statistical measures.

Conclusions:

  • The nRC method surpasses existing classification tools that rely on secondary structure features and machine learning.
  • nRC demonstrates superior performance compared to the benchmark RNAcon classifier.
  • The nRC tool is publicly available as a Docker image and its source code is accessible on GitHub.