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

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...
RNA Splicing01:32

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 Splicing01:32

Pre-mRNA Processing: 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 Splicing02:18

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 Splicing02:18

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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

You might also read

Related Articles

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

Sort by
Same author

Frequency and Prognostic Significance of Genetic Abnormalities in a Subgroup of Patients With Intermediate-Risk Neuroblastoma: A SIOPEN Study.

JCO precision oncology·2026
Same author

Neuronal differentiation of neuroblastoma cell lines for neurological disease modeling.

iScience·2026
Same author

Centromeric footprints preserve telomere integrity in ALT cancers.

Nature·2026
Same author

Infection cycles of viruses of the phylum Nucleocytoviricota.

Nature reviews. Microbiology·2026
Same author

Detection and quantification of thermotolerant Campylobacter spp. on eggshells by cultivation and viability Real-time PCR.

International journal of food microbiology·2026
Same author

Accurate prediction of ecDNA in interphase cancer cells using deep neural networks.

Communications biology·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 13, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

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

Published on: December 9, 2016

SplicingCompass: differential splicing detection using RNA-seq data.

Moritz Aschoff1, Agnes Hotz-Wagenblatt, Karl-Heinz Glatting

  • 1Bioinformatics HUSAR, Genomics Proteomics Core Facility, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. m.aschoff@dkfz.de

Bioinformatics (Oxford, England)
|March 2, 2013
PubMed
Summary
This summary is machine-generated.

We developed a new RNA sequencing analysis method to detect differential gene splicing. This approach uses geometric angles to identify complex splicing patterns, aiding in disease research and patient stratification.

More Related Videos

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Related Experiment Videos

Last Updated: May 13, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

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
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Alternative splicing significantly expands transcriptome and proteome diversity, crucial for cellular functions.
  • Aberrant splicing is linked to diseases, including various cancer types.
  • Next-generation RNA sequencing (RNA-seq) enables large-scale alternative splicing analysis, but standardized computational tools are lacking.

Purpose of the Study:

  • To introduce a novel computational method and software for predicting differentially spliced genes from RNA-seq data.
  • To address the need for robust algorithms in analyzing complex alternative splicing events.

Main Methods:

  • Utilized geometric angles between high-dimensional exon read count vectors to detect differential splicing.
  • Applied the method to neuroblastoma tumor data comparing favorable and unfavorable clinical outcomes.
  • Validated predictions through simulated experiments, in silico analyses, and patient clustering.

Main Results:

  • Successfully identified differentially spliced genes, even with complex or novel splicing patterns.
  • Demonstrated the method's applicability in patient stratification and tissue clustering.
  • Found significant associations between predicted genes, splicing factor motifs, and existing literature.

Conclusions:

  • The new method effectively detects differential splicing from RNA-seq data.
  • Splicing information can be leveraged for clinical applications like patient clustering.
  • The freely available SplicingCompass software facilitates advanced splicing analysis.