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

RNA Splicing

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...

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: RNA Splicing

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

Chromatin Structure and RNA Splicing

Chromatin Structure and 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 X-linked long non-coding RNA, PTCHD1-AS, and the core features of autism.

Nature·2026

Same author

NeoTCRseek: an integrated platform for high-sensitivity identification of neoantigen-specific TCR clonotypes to track clinical T-cell dynamics.

Frontiers in immunology·2026

Same author

Orthrus: toward evolutionary and functional RNA foundation models.

Nature methods·2026

Same author

Orthrus: Towards Evolutionary and Functional RNA Foundation Models.

bioRxiv : the preprint server for biology·2024

Same author

A graph neural network approach for molecule carcinogenicity prediction.

Bioinformatics (Oxford, England)·2022

Same author

IntroSpect: Motif-Guided Immunopeptidome Database Building Tool to Improve the Sensitivity of HLA I Binding Peptide Identification by Mass Spectrometry.

Biomolecules·2022

Search research articles

Related Experiment Video

Updated: Apr 28, 2026

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

Deep learning of the tissue-regulated splicing code.

Michael K K Leung¹, Hui Yuan Xiong¹, Leo J Lee¹

¹Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.

Bioinformatics (Oxford, England)

|June 17, 2014

Summary

This summary is machine-generated.

A deep neural network model accurately predicts alternative splicing (AS) patterns from genomic data and tissue context. This computational approach advances understanding of gene expression regulation and genetic variation effects.

More Related Videos

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Published on: October 9, 2014

Related Experiment Videos

Last Updated: Apr 28, 2026

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

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Published on: October 9, 2014

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Alternative splicing (AS) generates transcript diversity from single genes.
Predicting AS patterns computationally is crucial for understanding gene regulation and genetic variation impacts.

Purpose of the Study:

To develop a deep learning model for predicting alternative splicing patterns.
To analyze genomic features and tissue context influencing splicing.

Main Methods:

A deep neural network architecture was employed.
The model was trained on mouse RNA-Seq data.
Graphics processing units accelerated model training.

Main Results:

The deep architecture outperformed previous Bayesian methods in predicting AS patterns.
Deep architectures demonstrate effectiveness even with moderately sparse datasets.
The model's learned genomic feature representations were analyzed.

Conclusions:

Deep learning provides a powerful tool for predicting alternative splicing.
The developed model enhances the understanding of splicing regulation and its variation.
Computational models are vital for exploring complex biological processes like AS.