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 Splicing01:32

RNA Splicing

60.3K
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...
60.3K
Pre-mRNA Processing: RNA Splicing01:36

Pre-mRNA Processing: RNA Splicing

6.6K
6.6K
Alternative RNA Splicing02:18

Alternative RNA Splicing

24.7K
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...
24.7K
Alternative RNA Splicing02:18

Alternative RNA Splicing

4.8K
4.8K
Chromatin Structure Regulates pre-mRNA Processing02:41

Chromatin Structure Regulates pre-mRNA Processing

8.1K
In eukaryotic cells, nascent mRNA transcripts need to undergo many post-transcriptional modifications to reach the cell cytoplasm and translate into functional proteins. For a long time, transcription and pre-mRNA processing were considered two independent events that occur sequentially in the cell. However, it has now been well established that transcription and pre-mRNA processing are two simultaneous processes that are precisely regulated inside the cell.
The chromatin structure, especially...
8.1K
Chromatin Structure and RNA Splicing02:41

Chromatin Structure and RNA Splicing

3.2K
3.2K

You might also read

Related Articles

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

Sort by
Same author

pyVIPER: a fast and scalable Python package for protein activity estimation and master regulator analysis of single-cell RNA sequencing data.

BMC bioinformatics·2026
Same author

Variations in the latitudinal diversity gradients of the ocean microbiome.

Cell host & microbe·2026
Same author

In silico typing maps the natural diversity of Escherichia coli transporter-dependent capsules.

Nature microbiology·2026
Same author

Exon inclusion signatures enable accurate estimation of splicing factor activity.

Nature communications·2026
Same author

Coral microbiomes as reservoirs of unknown genomic and biosynthetic diversity.

Nature·2026
Same author

Sources of essential lipids for Mycoplasma pneumoniae via P116 to target liver and atherosclerotic lesions.

Nature communications·2025

Related Experiment Video

Updated: Jan 14, 2026

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency
08:53

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

Published on: September 15, 2021

3.2K

Using single-cell perturbation screens to decode the regulatory architecture of splicing factor programs.

Miquel Anglada-Girotto1, Samuel Miravet-Verde2, Luis Serrano1,3,4

  • 1Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain.

Nucleic Acids Research
|October 16, 2025
PubMed
Summary

This study introduces a new AI method to measure splicing factor activity from gene expression, aiding cancer research. It reveals a regulatory network linking cancer mutations to splicing, offering insights into disease mechanisms.

More Related Videos

Using the E1A Minigene Tool to Study mRNA Splicing Changes
10:25

Using the E1A Minigene Tool to Study mRNA Splicing Changes

Published on: April 22, 2021

5.4K
Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells
10:06

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Published on: April 26, 2017

9.4K

Related Experiment Videos

Last Updated: Jan 14, 2026

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency
08:53

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

Published on: September 15, 2021

3.2K
Using the E1A Minigene Tool to Study mRNA Splicing Changes
10:25

Using the E1A Minigene Tool to Study mRNA Splicing Changes

Published on: April 22, 2021

5.4K
Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells
10:06

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Published on: April 26, 2017

9.4K

Area of Science:

  • Molecular Biology
  • Genomics
  • Computational Biology

Background:

  • Splicing factors regulate gene isoforms, crucial for cell function.
  • Dysregulated splicing is linked to diseases like cancer.
  • Current methods for mapping splicing factor interactions are limited.

Purpose of the Study:

  • To develop a novel computational approach for assessing splicing factor activity.
  • To map the regulatory network of splicing factors in cancer.
  • To link cancer driver mutations to splicing dysregulation.

Main Methods:

  • Utilized shallow artificial neural networks to estimate splicing factor activity from gene expression data.
  • Applied Perturb-seq (CRISPR perturbations + single-cell RNA sequencing) for high-throughput screening.
  • Analyzed splicing program activity shifts as a reporter for oncogenic splicing factor activity.

Main Results:

  • Developed an AI model that bypasses the need for exon-level data to measure splicing factor activity.
  • Mapped a cross-regulatory network of splicing factors in carcinogenesis.
  • Identified MYC and other pathways connecting cancer mutations to splicing regulation.

Conclusions:

  • Established a versatile computational framework for studying splicing factor regulation.
  • Demonstrated the utility of the framework in uncovering cancer-related disease mechanisms.
  • Highlighted the recapitulation of developmental splicing dynamics in cancer regulation.