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

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

2.9K
2.9K
Regulated mRNA Transport02:22

Regulated mRNA Transport

6.3K
In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
6.3K
Nuclear Localization Signals and Import01:46

Nuclear Localization Signals and Import

5.9K
Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
5.9K
Ribosome Profiling02:24

Ribosome Profiling

3.6K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.6K
Post-translational Translocation of Proteins to the RER01:27

Post-translational Translocation of Proteins to the RER

5.8K
A sizable fraction of proteins destined for ER are first synthesized in the cell cytosol and then transported across the ER membrane–a process called post-translational translocation. Similar to cotranslationally translocated proteins, these proteins also use the Sec translocon complex to enter the ER lumen.
Targeting proteins to the ER
Hsp40 and Hsp70 chaperone molecules bind the translated proteins in the cytosol to prevent their folding. The chaperone binding helps to keep the signal...
5.8K
Nuclear Protein Sorting01:34

Nuclear Protein Sorting

4.7K
Nuclear protein sorting is the selective trafficking of histones, polymerases, gene regulatory proteins into the nucleus and exporting RNAs and ribosomes to the cytosol. It is a tightly controlled process that regulates gene expression within a cell.
Proteins targeted to the nucleus carry nuclear localization signals or NLS recognized by import receptors in the cytosol. Similarly, proteins with nuclear export signals are recognized by export receptors. Import and export receptors are...
4.7K

You might also read

Related Articles

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

Sort by
Same author

FluNexus: A versatile web platform for antigenic prediction and visualization of influenza A viruses.

iMeta·2026
Same author

Mosaic integration of spatial multi-omics with SpaMosaic.

Nature genetics·2026
Same author

Artificial Intelligence Powers Protein Functional Annotation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

NanoLoop: A Deep Learning Framework Leveraging Nanopore Sequencing for Chromatin Loop Prediction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Incremental Value of Non-Gated Chest CT Coronary Artery Calcium Score in Predicting Major Adverse Cardiovascular Events by GRACE Score After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome.

Clinical cardiology·2025
Same author

scHLens: a web server for hierarchically and interactively exploring single cell RNA-seq data.

Briefings in bioinformatics·2025
Same journal

Literature-informed gene extraction and ranking for multimodal data fusion.

Briefings in bioinformatics·2026
Same journal

SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.

Briefings in bioinformatics·2026
Same journal

Genome assemblies and annotations are not static and need support for tracking their evolution.

Briefings in bioinformatics·2026
Same journal

A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.

Briefings in bioinformatics·2026
Same journal

Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.

Briefings in bioinformatics·2026
Same journal

Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.

Briefings in bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Aug 16, 2025

Author Spotlight: RNA FISH for Locating lncRNA-SNHG6 in Osteosarcoma Cells
05:27

Author Spotlight: RNA FISH for Locating lncRNA-SNHG6 in Osteosarcoma Cells

Published on: June 16, 2023

1.6K

GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on

Min Li1, Baoying Zhao1, Rui Yin2

  • 1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.

Briefings in Bioinformatics
|December 22, 2022
PubMed
Summary
This summary is machine-generated.

GraphLncLoc, a novel deep learning model, accurately predicts long non-coding RNA (lncRNA) subcellular localization by transforming sequences into graphs. This approach captures sequence patterns better than traditional methods, improving functional understanding.

Keywords:
de Bruijn graphdeep learninggraph convolutional networkslong non-coding RNAsubcellular localization prediction

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

840
RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

25.5K

Related Experiment Videos

Last Updated: Aug 16, 2025

Author Spotlight: RNA FISH for Locating lncRNA-SNHG6 in Osteosarcoma Cells
05:27

Author Spotlight: RNA FISH for Locating lncRNA-SNHG6 in Osteosarcoma Cells

Published on: June 16, 2023

1.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

840
RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

25.5K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding long non-coding RNA (lncRNA) subcellular localization is vital for elucidating their functions.
  • Current prediction methods often rely on k-mer frequency features, which neglect crucial sequence order and motif information.

Purpose of the Study:

  • To develop an advanced deep learning model for predicting lncRNA subcellular localization.
  • To overcome the limitations of traditional k-mer based feature extraction methods.

Main Methods:

  • Proposed GraphLncLoc, a graph convolutional network (GCN) based deep learning model.
  • Transformed lncRNA sequences into de Bruijn graphs, converting sequence classification to graph classification.
  • Utilized GCNs to extract high-level features from the graph representations for prediction.

Main Results:

  • GraphLncLoc demonstrated superior performance compared to existing predictors and traditional machine learning models.
  • Graph-based sequence representation showed more distinguishable and robust features than k-mer frequency features.
  • A case study successfully identified important motifs associated with nuclear lncRNA localization.

Conclusions:

  • GraphLncLoc offers a powerful and effective approach for predicting lncRNA subcellular localization.
  • The graph-based representation is a significant advancement over k-mer frequency methods for lncRNA sequence analysis.
  • The developed model and web server provide valuable tools for lncRNA research.