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

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...

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...

Chromatin Structure and RNA Splicing

Chromatin Structure and RNA Splicing

Base Excision Repair

Base Excision Repair

You might also read

Related Articles

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

Sort by

Same author

CodonRL: Multi-Objective Codon Sequence Optimization Using Demonstration-Guided Reinforcement Learning.

bioRxiv : the preprint server for biology·2026

Same author

seq2ribo: Structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

bioRxiv : the preprint server for biology·2026

Same author

Augmenting Electronic Health Records for Adverse Event Detection.

medRxiv : the preprint server for health sciences·2026

Same author

Dithioether Inhibitors of Bacterial RNA Polymerase-Sigma Factor Interactions Exhibit In Vivo Efficacy against MRSA.

Journal of medicinal chemistry·2025

Same author

Spatial dynamics of brain development and neuroinflammation.

Nature·2025

Same author

Pinoresinol diglucoside alleviates ovariectomy-induced osteoporosis by modulating the "Microbiota-gut-bone" axis.

Biochemical and biophysical research communications·2025

Same journal

Haplotype-aware long-read error correction.

Algorithms for molecular biology : AMB·2026

Same journal

Extension of partial atom-to-atom maps: uniqueness and algorithms.

Algorithms for molecular biology : AMB·2026

Same journal

Lossless pangenome indexing using tag arrays.

Algorithms for molecular biology : AMB·2026

Same journal

Dolphyin: a combinatorial algorithm for identifying 1-Dollo phylogenies in cancer.

Algorithms for molecular biology : AMB·2026

Same journal

Probing transcription factor subsets in gene regulatory networks.

Algorithms for molecular biology : AMB·2026

Same journal

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features.

Algorithms for molecular biology : AMB·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 6, 2025

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

Exact transcript quantification over splice graphs.

Cong Ma¹, Hongyu Zheng², Carl Kingsford³

¹Computer Science Department, School of Engineering and Applied Science, Princeton University, 35 Olden Street, Princeton, NJ, 08544, USA.

Algorithms for Molecular Biology : AMB

|May 11, 2021

Summary

This summary is machine-generated.

This study enhances graph quantification for RNA-seq data by handling variable read lengths and incorporating bias correction. The improved model offers a more flexible and accurate approach for analyzing splice junctions and transcript expression.

Keywords:

Alternative splicing Network flow RNA-seq Splice graph Transcript quantification

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: Nov 6, 2025

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

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:

Bioinformatics
Computational Biology
Genomics

Background:

RNA-seq read quantification traditionally uses transcript abundances.
Graph quantification models splice junction abundances for RNA-seq analysis.
Prior models assumed fixed read lengths, limiting their applicability.

Purpose of the Study:

To improve graph quantification for RNA-seq data.
To accommodate variable-length reads and fragments.
To incorporate bias correction into the graph quantification model.

Main Methods:

Developed an extended splice graph using Aho-Corasick automata.
Introduced a novel reparameterization of the read generation model.
Proved model equivalence to transcript quantification with all compatible transcripts.

Main Results:

Successfully adapted graph quantification for variable-length RNA-seq reads and fragments.
Integrated bias correction for more accurate quantification.
Demonstrated the model's equivalence to a comprehensive transcript quantification approach.

Conclusions:

The enhanced graph quantification method provides a robust alternative for RNA-seq analysis.
This approach is valuable when reference transcriptomes are incomplete or unavailable.
Applicable to transcriptome assembly and alternative splicing analyses.