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 Experiment Videos

SpliceMachine: predicting splice sites from high-dimensional local context representations.

Sven Degroeve1, Yvan Saeys, Bernard De Baets

  • 1Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Technologiepark 927, Gent 9052, Belgium. sven.degroeve@psb.ugent.be

Bioinformatics (Oxford, England)
|November 27, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

CARGO: A Cytometry Analysis framework via Regularized Graph Optimal-transport.

PLoS computational biology·2026
Same author

iDeepLC: Chemical Structure Information Yields Improved Retention Time Prediction of Peptides with Unseen Modifications.

Analytical chemistry·2026
Same author

PhaLP 2.0: extending the community-oriented phage lysin database with a SUBLYME pipeline for metagenomic discovery.

Database : the journal of biological databases and curation·2026
Same author

Toward explainable and generalizable data-driven modeling in real wastewater treatment plants: Utilizing bidimensional interpretable deep learning and cross-scenario transfer learning.

Journal of environmental management·2026
Same author

anndataR improves interoperability between R and Python in single-cell transcriptomics.

Bioinformatics (Oxford, England)·2026
Same author

Automated Computational Flow Cytometry Correlates Decreasing Neutrophil-to-Lymphocyte Ratio to Improved Survival in NSCLC After Immune Checkpoint Blockade.

Cancer immunology research·2026
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·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
See all related articles

SpliceMachine is a new tool for predicting gene intron boundaries in DNA sequences. It offers high accuracy and speed, improving gene structure prediction for molecular research.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Accurate gene structure prediction is vital for molecular research, especially with complete genome sequencing.
  • Predicting intron boundaries is key to understanding gene structure in nuclear genomes.
  • Existing splice site prediction tools often generate false positive predictions.

Purpose of the Study:

  • To introduce SpliceMachine, a novel, publicly available tool for splice site prediction.
  • To address limitations of current tools by offering improved accuracy and user-trainability.

Main Methods:

  • Development of a novel splice site prediction algorithm.
  • Implementation of SpliceMachine as a computationally fast annotation tool.
  • Enabling user-specific training capabilities for custom datasets.

Related Experiment Videos

Main Results:

  • SpliceMachine demonstrates state-of-the-art prediction performance on *Arabidopsis thaliana* and human sequences.
  • The tool provides computationally efficient gene annotation.
  • Users can train SpliceMachine on their own specific data.

Conclusions:

  • SpliceMachine offers a significant advancement in splice site prediction accuracy and efficiency.
  • The tool's user-trainability enhances its applicability across diverse genomic research.
  • SpliceMachine is a valuable resource for gene structure prediction in molecular biology.