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

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Exon Recombination02:32

Exon Recombination

The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon has three reading...

You might also read

Related Articles

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

Sort by
Same author

The howler monkey genome provides new insights into the distinctive howling and folivorous adaptations of New World monkeys.

Genome biology·2026
Same author

Accurate, comprehensive gene annotation and ortholog identification across thousands of vertebrate genomes with TOGA2.

bioRxiv : the preprint server for biology·2026
Same author

The Vertebrate Genomes Project Phase I: A global reference genome resource.

bioRxiv : the preprint server for biology·2026
Same author

The genomic basis of independent marine transitions in turtles: convergent episodic adaptation and demographic shifts.

Molecular biology and evolution·2026
Same author

Chromosome-level genome assembly of the common tenrec, Tenrec ecaudatus (Schreber, 1778), a new model for early placental mammal evolution.

BMC genomics·2026
Same author

Correction: Inhibition of the Nuclear Export Receptor XPO1 as a Therapeutic Target for Platinum-Resistant Ovarian Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026

Related Experiment Video

Updated: Jun 27, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

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

Published on: June 6, 2025

Improved identification of conserved cassette exons using Bayesian networks.

Rileen Sinha1, Michael Hiller, Rainer Pudimat

  • 1Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany. rsinha@fli-leibniz.de

BMC Bioinformatics
|November 19, 2008
PubMed
Summary

Bayesian networks accurately predict alternative splicing events, improving upon previous methods. This advancement aids in understanding transcriptome diversity and identifying novel alternative exons.

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

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

Related Experiment Videos

Last Updated: Jun 27, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

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

Published on: June 6, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

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

Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • Alternative splicing significantly increases the diversity of eukaryotic transcriptomes and proteomes.
  • Current methods like ESTs and microarrays have limitations in capturing all alternative splicing events.
  • There is a growing need for improved in silico prediction of alternative splicing due to rapid genomic data production.

Purpose of the Study:

  • To develop and evaluate Bayesian networks (BNs) for accurate prediction of alternative splicing, specifically exon skipping events.
  • To improve the prediction performance by incorporating novel features, including intronic splicing regulatory elements and mRNA secondary structures.
  • To compare the performance of BNs with existing methods like support vector machines (SVMs).

Main Methods:

  • Utilized Bayesian networks (BNs) for predicting evolutionary conserved exon skipping events.
  • Incorporated novel features such as intronic splicing regulatory elements and mRNA secondary structures.
  • Performed cross-validation on multiple datasets and random labeling tests to ensure robustness and rule out overfitting.

Main Results:

  • Achieved a 61% true positive rate at a 0.5% false positive rate for conserved exon skipping, outperforming SVMs (50%).
  • Demonstrated that incorporating intronic regulatory elements and mRNA secondary structures enhances prediction accuracy.
  • Identified that approximately half of exons predicted as potentially alternative by BNs were confirmed by EST data, suggesting novel alternative exons.

Conclusions:

  • Bayesian networks are effective for accurate identification of alternative exons and can reveal feature dependencies.
  • The choice of features is more critical for classification performance than the specific classifier (BN vs. SVM).
  • Conservation-based features remain the most informative for distinguishing alternative from constitutive exons, highlighting the challenge of prediction without them.