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

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...

Alternative RNA Splicing

Alternative RNA Splicing

RNA Splicing

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...

lncRNA - Long Non-coding RNAs

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...

Chromatin Structure and RNA Splicing

Chromatin Structure and RNA Splicing

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: RNA Splicing

You might also read

Related Articles

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

Sort by

Same author

An Integrated Skin Cell Atlas Decodes the Pilosebaceous Unit.

The Journal of investigative dermatology·2026

Same author

Astrocyte-specific FoxF2 modulates immune and myelin repair: Evidence from multiple sclerosis lesions and an animal model.

Journal of neuroimmunology·2026

Same author

PeptideNet: An Integrative Deep Learning Framework for Predicting Diverse Bioactive Peptides Using Protein Language Model Embeddings.

Journal of chemical information and modeling·2026

Same author

Deletion of cardiac fibroblast growth factor-23 beneficially impacts myocardial energy metabolism in left ventricular hypertrophy.

npj metabolic health and disease·2025

Same author

Reticulated platelets in coronary artery disease: a multidimensional approach unveils prothrombotic signalling and novel therapeutic targets.

European heart journal·2025

Same author

Phenotypic Changes in a Monocyte Cluster with High Interleukin-1 Beta Expression during Long-Term Anti-CD20 Therapy.

Annals of neurology·2025

Same journal

Tissue MicroRNAs in Arrhythmogenic Cardiomyopathy: A Systematic Review of Studies in Human Myocardium and Animal Models with Implications for Post-Mortem Molecular Diagnostics.

Genes·2026

Same journal

Genetic Variants and Dental Caries Susceptibility: An Umbrella Review and Multilevel Meta-Analysis.

Genes·2026

Same journal

Generative AI and Language Models in Human Genetics and Health: From Variant Interpretation to Clinical Decision Support.

Genes·2026

Same journal

Familial White-Sutton Syndrome Caused by a Pathogenic POGZ p.Arg508* Variant: Intrafamilial Variability from Childhood to Adulthood.

Genes·2026

Same journal

Genetic Influence on LDL-Cholesterol Levels: Role of Polygenic Risk Scores and Lp(a) Beyond Monogenic Hypercholesterolemia.

Genes·2026

Same journal

THBS1 as a Key Regulator of Myoblasts: Validation of Its Inhibitory Roles in Skeletal Muscle Development.

Genes·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 21, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning.

Zakaria Louadi¹, Mhaned Oubounyt¹, Hilal Tayara²

¹Department of Electronics and Information Engineering, Chonbuk National University, Jeonju 54896, Korea.

|August 4, 2019

Summary

This summary is machine-generated.

Deep Splicing Code (DSC) uses deep learning to predict alternative splicing (AS) events from genomic sequences. This computational method accurately distinguishes between constitutive and alternative splicing, improving our understanding of gene expression regulation.

Keywords:

alternative splicing computational biology deep learning splicing code splicing predictor

More Related Videos

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Published on: October 9, 2014

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Published on: April 26, 2017

Related Experiment Videos

Last Updated: Jan 21, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Published on: October 9, 2014

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Published on: April 26, 2017

Area of Science:

Computational biology
Genomics
Molecular biology

Background:

Alternative splicing (AS) generates protein diversity from a single gene.
Computational models, or "Splicing Codes," aim to predict AS outcomes.
Understanding AS regulation is crucial for deciphering gene expression.

Purpose of the Study:

To develop a deep learning approach, Deep Splicing Code (DSC), for AS event prediction.
To categorize alternatively skipped exons, alternative 5'ss, alternative 3'ss, and constitutively spliced exons.
To improve the accuracy of in-silico AS prediction based on exon junction sequences.

Main Methods:

Developed a deep learning model named Deep Splicing Code (DSC).
Trained the model using exon junction sequences to predict AS events.
Utilized motif visualization to analyze the model's learned features.

Main Results:

DSC significantly improved the prediction accuracy of AS events.
Identified distinct local sequence characteristics differentiating constitutive and alternatively spliced exons.
Demonstrated that the model precisely identifies alternative splice sites and splicing factor motifs.

Conclusions:

Deep learning offers a powerful approach for modeling alternative splicing.
Constitutive exons possess unique sequence features compared to alternatively spliced exons.
The developed method and web server enhance AS prediction and research opportunities.