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

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: 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...

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

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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

Biobank-Scale Plasma Proteomics Identifies Novel Biomarkers in Hypertrophic Cardiomyopathy.

Circulation. Genomic and precision medicine·2026

Same author

Genome-wide tandem repeat expansions modify schizophrenia risk in the presence of a 22q11.2 deletion.

Molecular psychiatry·2026

Same author

Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex.

Research square·2025

Same author

Determinants of pleiotropy and monotonic gene dosage responses across human traits.

Research square·2025

Same author

Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex.

bioRxiv : the preprint server for biology·2025

Search research articles

Related Experiment Video

Updated: Dec 14, 2025

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

Predicting the effect of variants on splicing using Convolutional Neural Networks.

Thanyathorn Thanapattheerakul¹, Worrawat Engchuan^2,3, Jonathan H Chan^1,4

¹School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.

|July 25, 2020

Summary

This study introduces a framework using Convolutional Neural Networks (CNNs) to predict how genomic variants affect messenger RNA (mRNA) splicing, aiding in disease variant identification. The CNN model effectively distinguishes pathogenic from benign variants, supporting its use in genetic studies.

Keywords:

Binding sites Convolutional neural networks Deep learning Genomic variants RNA Splice Sites Splice site Splicing events

More Related Videos

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

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Related Experiment Videos

Last Updated: Dec 14, 2025

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

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

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Mutations affecting messenger RNA (mRNA) splicing can cause human diseases.
Computational models are used to identify splice site sequences.
Convolutional Neural Networks (CNNs) show promise in splice site prediction.

Purpose of the Study:

To develop and evaluate a framework using CNNs to predict the impact of genomic variants on mRNA splicing.
To identify disease-causing variants that disrupt RNA splicing.
To assess the utility of CNNs in distinguishing pathogenic from benign variants.

Main Methods:

Trained and compared five machine learning models (three CNN-based, two non-CNN) on two splice site datasets (GWH, DLAI).
Evaluated predictive models using donor sites and the HSplice tool.
Combined datasets to enhance model performance.
Applied the best-performing CNN model to variant data from the ClinVar database.

Main Results:

The four-convolutional-layer CNN model achieved the highest performance, with AUPRC of 93.4% for donor sites and 88.8% for acceptor sites.
The framework successfully differentiated pathogenic from benign variants (p = 5.9 × 10⁻⁷).

Conclusions:

The proposed CNN-based framework effectively predicts the impact of splice variants on mRNA splicing.
This approach can aid in identifying disease-causing genetic variants.
The framework holds potential for future genetic studies investigating splicing-related diseases.